Method, device, storage medium and electronic device for determining remaining mileage of vehicle

Abstract

The disclosed embodiment provides a method, device, storage medium and electronic device for determining the remaining mileage of a vehicle, the determination method comprising: determining the remaining power of a battery in a vehicle; determining the average energy consumption value of the vehicle in a predetermined working mode based on historical driving data; and determining the remaining mileage of the vehicle based on the remaining power and the average energy consumption value. The disclosed embodiment combines historical driving data, vehicle speed, temperature, battery SOC and other factors to predict energy consumption, and can predict the energy consumption of the vehicle more quickly and accurately, with a faster response speed, and significantly improves the accuracy of calculating the remaining mileage of the vehicle.

Description

Technical Field

[0001] The present disclosure relates to the technical field of vehicle driving control, and in particular to a method, device, storage medium and electronic device for determining the remaining mileage of a vehicle. Background Art

[0002] As the global demand for clean energy continues to increase, pure electric vehicles are gradually being widely used as a new type of clean energy vehicle. However, due to the special energy storage method of electric vehicles, the accurate prediction of the remaining mileage of pure electric vehicles during driving has become a challenging task. At present, there are some methods and systems for predicting the remaining mileage of pure electric vehicles on the market, but these methods often require a large amount of real-time data, relatively complex mathematical models and the assistance of artificial intelligence, resulting in high costs and not conducive to promotion and application. Summary of the invention

[0003] The purpose of the embodiments of the present disclosure is to provide a method, device, storage medium and electronic device for determining the remaining mileage of a vehicle, so as to solve the problems existing in the prior art.

[0004] In order to solve the above technical problems, the embodiments of the present disclosure adopt the following technical solutions:

[0005] An aspect of an embodiment of the present disclosure provides a method for determining a remaining mileage of a vehicle, comprising:

[0006] Determine the remaining charge of the battery in the vehicle;

[0007] Determining an average energy consumption value of the vehicle in a predetermined working mode based on historical driving data;

[0008] A remaining range of the vehicle is determined based on the remaining power and the average energy consumption value.

[0009] In some embodiments, determining the remaining power of a battery in the vehicle includes:

[0010] Determining the rated capacity of a battery in a vehicle based on historical driving data;

[0011] determining a rated total energy of the battery based on the rated capacity;

[0012] The remaining capacity is determined based on the rated total energy and the state of charge.

[0013] In some embodiments, determining the rated capacity of a battery in a vehicle based on historical driving data includes:

[0014] grouping the historical driving data based on the ambient temperature to obtain the rated capacity in different temperature ranges;

[0015] The rated capacity of the battery is determined based on the current ambient temperature of the vehicle.

[0016] In some embodiments, determining the average energy consumption value of the vehicle in a predetermined working mode based on historical driving data includes:

[0017] determining a current operating mode of the vehicle based on historical driving data;

[0018] determining an estimated average energy consumption of the vehicle in the operating mode based on historical driving data;

[0019] The average energy consumption estimate is corrected to obtain the average energy consumption value.

[0020] In some embodiments, determining the current working mode of the vehicle based on historical driving data includes:

[0021] grouping the historical driving data based on ambient temperature, average vehicle speed, and state of charge to determine a plurality of operating modes;

[0022] The current operating mode is determined based on the current ambient temperature, average vehicle speed and state of charge of the vehicle.

[0023] In some embodiments, the correcting the average energy consumption estimate to obtain the average energy consumption value includes:

[0024] Predicting the speed of the vehicle within a predetermined time in the future;

[0025] The average energy consumption estimate is revised based on the prediction result.

[0026] In some embodiments, the prediction of the vehicle speed value within a predetermined future time is implemented based on a BP neural network, and the BP neural network is trained based on historical driving data.

[0027] Another aspect of the present disclosure provides a device for determining a remaining mileage of a vehicle, comprising:

[0028] A remaining power determination module, used to determine the remaining power of a battery in a vehicle;

[0029] an average energy consumption value determination module, used to determine an average energy consumption value of the vehicle in a predetermined working mode based on historical driving data;

[0030] The remaining mileage determination module is used to determine the remaining mileage of the vehicle based on the remaining power and the average energy consumption value.

[0031] The present disclosure also provides a storage medium storing a computer program, wherein the computer program implements the steps of any of the above methods when executed by a processor.

[0032] The present disclosure also provides an electronic device, comprising at least a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the above methods when executing the computer program on the memory.

[0033] The disclosed embodiment establishes an offline model for estimating the remaining mileage through historical driving data, and performs short-term vehicle speed prediction by querying the average energy consumption estimate at the same time, so as to achieve online correction of the average energy consumption value, and finally obtains the remaining mileage estimate in combination with the remaining battery power. The disclosed embodiment combines historical driving data, vehicle speed, temperature, battery SOC and other factors to predict energy consumption, and can predict the energy consumption of the vehicle more quickly and accurately, with a faster response speed, and significantly improves the accuracy of calculating the remaining mileage of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0035] Figure 1 A schematic diagram of the steps of a method for determining the remaining mileage of a vehicle according to an embodiment of the present disclosure;

[0036] Figure 2 A schematic diagram of the steps of a method for determining the remaining mileage of a vehicle according to an embodiment of the present disclosure;

[0037] Figure 3 A schematic diagram of the steps of a method for determining the remaining mileage of a vehicle according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0038] Various aspects and features of the present disclosure are described herein with reference to the drawings.

[0039] It should be understood that various modifications may be made to the embodiments of the present application. Therefore, the above description should not be considered as limiting, but only as an example of an embodiment. Other modifications within the scope and spirit of the present disclosure will occur to those skilled in the art.

[0040] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the general description of the present disclosure given above and the detailed description of the embodiments given below, serve to explain the principles of the present disclosure.

[0041] 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 attached drawings.

[0042] It should also be understood that, although the present disclosure has been described with reference to some specific examples, those skilled in the art will be able to realize many other equivalent forms of the present disclosure that have the features described in the claims and are therefore within the scope of protection defined thereby.

[0043] The above and other aspects, features and advantages of the present disclosure will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings.

[0044] Specific embodiments of the present disclosure are described hereinafter with reference to the accompanying drawings; however, it should be understood that the embodiments claimed are merely examples of the present disclosure, which may be implemented in a variety of ways. Well-known and / or repeated functions and structures are not described in detail to avoid obscuring the present disclosure with unnecessary or redundant details. Therefore, the specific structural and functional details claimed herein are not intended to be limiting, but merely serve as a basis and representative basis for the claims to teach those skilled in the art to use the present disclosure in a variety of ways with substantially any suitable detailed structure.

[0045] This specification may use the phrases "in one embodiment," "in another embodiment," "in a further embodiment," or "in other embodiments," all of which may refer to one or more of the same or different embodiments according to the present disclosure.

[0046] The first embodiment of the present disclosure provides a method for determining the remaining mileage of a vehicle, which is used to estimate the remaining mileage of the vehicle based on big data, and the vehicle here can be an electric vehicle in particular. The big data here is historical data related to the driving of the vehicle, which is generally stored on a cloud server. The cloud server is used here to store data, realize real-time access and processing of data, and improve the efficiency and real-time performance of data analysis.

[0047] Specifically, this embodiment is based on signal and transmission rules and remote data collection and transmission devices, and uses the signal fed back by the current vehicle to enter the database located on the cloud server. After pre-processing such as data decoding and cleaning, the vehicle driving data is stored in the database, and relevant data is extracted during the vehicle driving process to estimate and calculate the remaining mileage.

[0048] Here, the process of collecting data for each vehicle to form big data mainly includes the following steps:

[0049] Step 1: Data collection step, data collection is performed by installing sensors inside and outside the vehicle. The sensors here include but are not limited to temperature sensors, GPS sensors, current sensors, voltage sensors, etc. The above sensors are used to collect data such as driving status, environmental information, driving behavior, power battery status, etc.

[0050] Step 2: Data cleaning step. To avoid affecting the data quality, data cleaning is performed on the collected data. The data cleaning work includes data denoising, missing data processing, and data formatting.

[0051] Step 3: Data storage step, the cleaned data is transmitted to the cloud server and stored in the database.

[0052] As mentioned above, the above-mentioned multiple sensors are used to comprehensively collect the vehicle's driving status, driving behavior, power battery status, etc., with higher data quality and more accurate analysis results. The driving data generated during each vehicle driving is stored in the cloud server in the above-mentioned manner, and the remaining mileage is determined by using the above-mentioned stored data during the current vehicle driving, such as Figure 1 As shown, including:

[0053] S101, determining the remaining power of a battery in a vehicle.

[0054] In this step, the remaining power of the battery in the vehicle is determined. Specifically, the battery SOC value collected in the vehicle is used to determine the remaining power RE of the battery, where the remaining power can be expressed as:

[0055] RE=SOC*W bat

[0056] W bat =C bat *U

[0057] In the above formula, SOC is the state of charge of the battery, which is generally used to reflect the remaining capacity of the battery, and its numerical value is defined as the ratio of the remaining capacity to the battery capacity; W bat is the rated total energy of the battery; C bat is the rated capacity of the battery; U is the terminal voltage of the battery.

[0058] It can be seen from the above formula that the remaining power RE of the battery is related to the SOC value of the battery and the rated total energy W of the battery. bat However, the rated capacity of the battery C bat It is not a fixed value but will change with the ambient temperature.

[0059] To this end, the rated capacity C of the battery is determined batIn the process, the current ambient temperature of the vehicle is obtained, so that the rated capacity C of the battery at different ambient temperatures can be obtained based on the big data in the cloud server. bat The MAP table is shown in Table 1 below;

[0060] Table 1 MAP table of the rated capacity of the battery at different ambient temperatures

[0061] Ambient temperature Battery rated capacity <-30℃ <![CDATA[C bat1 ]]> (-30,-20℃] <![CDATA[C bat2 ]]> (-20,-10℃] …… (-10,0℃] …… (0,10℃] …… (10,20℃] …… (20,30℃] <![CDATA[C bat7 ]]> >30℃ <![CDATA[C bat8 ]]>

[0062] In this way, 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 the table. bat , and finally the remaining capacity RE of the battery is determined by the above formula.

[0063] S102: Determine an average energy consumption value of the vehicle in a predetermined working mode based on historical driving data.

[0064] After the remaining power of the battery in the vehicle is determined in step S101, in this step, the average energy consumption value of the vehicle in the predetermined working mode is determined based on the historical driving data. The average energy consumption value E c It refers to the power consumption of the vehicle in different predetermined working modes. Here, the average energy consumption value E is determined. c In the process of , a hierarchical estimation method is used. Specifically, Figure 2 As shown, including:

[0065] S201, determining a current operating mode of the vehicle based on historical driving data.

[0066] In this step, the current working mode of the vehicle is determined based on the historical driving data. Specifically, in the process of determining the average energy consumption value, the average energy consumption estimate is first calculated offline based on the upper layer through the historical data about the vehicle's driving, that is, the average energy consumption basic value of the vehicle in the current working mode. Since the energy consumption of the vehicle is different in different working modes, there will be large differences in the average energy consumption values. Therefore, it is necessary to first clearly define and divide the working modes of the vehicle. In this embodiment, the principal component analysis method is used to extract features that can distinguish different working modes from the historical driving data stored in the database. The features here include at least average vehicle speed, ambient temperature and battery SOC.

[0067] The principal component analysis method here is a commonly used feature extraction method. Specifically, considering the average energy consumption value E c is the average energy consumption of the vehicle in different working modes. This value is affected by multiple factors.c The present embodiment realizes the accurate calculation of the average energy consumption value E according to the principal component analysis result, that is, considering the comprehensive influence of ambient temperature, average vehicle speed, and battery SOC. c According to preliminary estimates, the above influencing factors can be broken down into the following categories:

[0068] Considering the vehicle's driving conditions throughout the year, the ambient temperature information grouping intervals are set to: <-30°C, (-30, -20°C], (-20, -10°C], (-10, 0°C], (0, 10°C], (10, 20°C], (20, 30°C], >30°C, a total of 8 intervals;

[0069] Considering the driving state of the vehicle, the average vehicle speed information grouping interval is set to: (0, 20km / h], (20, 40km / h], (40, 60km / h], (60, 80km / h], > 80km / h;

[0070] Considering the usage status of the battery SOC value of the vehicle, the battery SOC value grouping interval is set to: (0, 20], (20, 40], (40, 60], (60, 80], (80, 100];

[0071] Considering the free combination of the intervals of the above three factors, the driving of the vehicle is divided into a total of 8*5*5=200 subdivided working modes, as shown in Table 2 below;

[0072] Table 2 Classification of working modes of the vehicles described

[0073] Working Mode Ambient temperature Average 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]

[0074] S202: Determine an estimated average energy consumption of the vehicle in the working mode based on historical driving data.

[0075] After the working mode of the vehicle is determined in step S201, in this step, an estimated average energy consumption of the vehicle in the working mode is determined based on historical driving data.

[0076] Specifically, based on the historical driving data stored in the database of the cloud server, the average vehicle speed information, ambient temperature information and battery SOC information of each driving segment of the vehicle are read, the historical driving data are grouped based on the determined working mode, and the energy consumption average E of all historical data segments of the vehicle in different working modes is calculated offline. ck (k=1, 2, 3..., 200) is used as the average energy consumption estimation value under a certain working mode, that is, the average energy consumption basic value, and a MAP table of average energy consumption estimation values ​​under different working modes is generated.

[0077] Specifically, the average energy consumption estimated value E in different working modes is ck The calculation process of (k=1,2,3...,200) is as follows: read the battery current and voltage data of each segment of historical driving data in different working modes, calculate the battery power, and then integrate and divide it by the driving mileage represented by the segment to obtain the average energy consumption estimation sub-value corresponding to the segment, which can be specifically expressed as:

[0078]

[0079] Further based on the average energy consumption estimation sub-value, an average energy consumption estimation value E of all segments of historical driving data of the vehicle in different working modes is obtained. ck (k=1,2,3...,200) can be expressed as:

[0080]

[0081] Based on the above formula, a MAP table of the average energy consumption estimation value of the vehicle in different working modes is obtained. When the vehicle is driving, the average vehicle speed information, the ambient temperature information and the battery SOC information within the first predetermined time before the current moment are read. The first predetermined time here can be set according to the situation, for example, set to 180s. Then, the working mode of the vehicle is determined by querying the MAP table of the average energy consumption estimation value and the average energy consumption estimation value E under the current working mode is read. ck (k=1,2,3...,200).

[0082] S203: Correct the estimated average energy consumption value to obtain the average energy consumption value.

[0083] After the average energy consumption estimate of the vehicle in the working mode is determined based on the historical driving data in step S202, in this step, the average energy consumption estimate is corrected to obtain the average energy consumption value. Specifically, the average energy consumption estimate obtained above is determined based on the historical driving data of the vehicle, and does not take into account the prediction of the future driving conditions of the vehicle. For this reason, the average energy consumption estimate needs to be predicted in this step.

[0084] Specifically, the average energy consumption estimate is corrected to obtain the average energy consumption value, such as Figure 3 As shown, further comprising:

[0085] S301, predicting the speed value of the vehicle within a predetermined time in the future.

[0086] S302: Correct the estimated average energy consumption based on the prediction result.

[0087] Specifically, considering that the upper layer calculates and determines the average energy consumption estimate in an offline manner through historical data about the vehicle's travel, the lower layer mainly uses the BP neural network to predict the short-term vehicle speed of the vehicle, thereby correcting the average energy consumption estimate obtained by the upper-layer algorithm.

[0088] The process of realizing online energy consumption correction based on vehicle speed prediction at the lower layer is as follows: first, based on the fact that the ambient temperature and battery SOC rarely change in a very short time interval, it is assumed that the possibility of changes in the ambient temperature and battery SOC within the second predetermined time in the future is low. Therefore, only the vehicle speed value within the future predetermined time needs to be predicted. The second predetermined time here can be set to 20s, for example.

[0089] Specifically, the BP neural network method is used to predict the vehicle speed value of the vehicle's driving condition in the next 20 seconds, and the average vehicle speed v in the next 20 seconds is calculated. avg_pre .

[0090] The basic process of the BP neural network method for vehicle speed prediction is as follows: First, the BP neural network is established and trained, and the average vehicle speed v is selected. mean , acceleration standard deviation σ a , deceleration standard deviation σ d , average acceleration a mean , idling time ratio r idle , the vehicle speed value within the next 5s is used as the input neuron, the vehicle speed information in the historical driving data in the database of the cloud server is extracted as the neural network training sample, and the vehicle speed in the next 20s is selected as the output neuron to establish a BP neural network.

[0091] After the BP neural network training is completed, the average vehicle speed v in the next second predetermined time, that is, the next 20 seconds, is output. avg_pre Thus, the average energy consumption prediction value E of the battery is obtained by looking up the table according to the predicted average vehicle speed, ambient temperature, and battery SOC. c_pre .

[0092] Furthermore, the average energy consumption estimate is corrected by the average energy consumption prediction value to finally obtain the average energy consumption value. Here, the average energy consumption value used for the remaining mileage estimation can be expressed as:

[0093] E c =δE ck +(1-δ)E c_pre

[0094] In the formula, δ is a constant, which is taken as 0.7;

[0095] S103: Determine the remaining mileage of the vehicle based on the remaining power and the average energy consumption value.

[0096] After determining the remaining power of the battery in the vehicle through the above step S101 and determining the average energy consumption value of the vehicle in a predetermined working mode based on historical driving data through the above step 102, the remaining mileage of the vehicle is determined based on the remaining power and the average energy consumption value in this step. Here, the remaining power and the average energy consumption value are input into the remaining mileage estimation offline model based on the working mode. The remaining mileage estimation offline model here can be established and trained based on historical driving data in the database of the cloud server, wherein the remaining mileage of the vehicle in the remaining mileage estimation offline model can be calculated by the following formula:

[0097]

[0098] Where RDR is the remaining range of the vehicle, RE is the remaining battery charge in the vehicle, and E c It is the average energy consumption value corresponding to the vehicle in the current working mode, that is, the remaining mileage RDR here is a function of the remaining power of the vehicle and the average energy consumption value corresponding to the current working mode. Here, to accurately estimate the remaining mileage of the vehicle, it is necessary to ensure that the accuracy of the remaining power of the vehicle and the average energy consumption value meet the requirements.

[0099] The disclosed embodiment establishes an offline model for estimating the remaining mileage through historical driving data, and performs short-term vehicle speed prediction by querying the average energy consumption estimate at the same time, so as to achieve online correction of the average energy consumption value, and finally obtains the remaining mileage estimate in combination with the remaining battery power. The disclosed embodiment combines historical driving data, vehicle speed, temperature, battery SOC and other factors to predict energy consumption, and can predict the energy consumption of the vehicle more quickly and accurately, with a faster response speed, and significantly improves the accuracy of calculating the remaining mileage of the vehicle.

[0100] Based on the same inventive concept as the first embodiment, the second embodiment of the present disclosure provides a device for determining the remaining mileage of a vehicle, comprising a remaining power determination module, an average energy consumption value determination module and a remaining mileage determination module coupled to each other, wherein:

[0101] The remaining power determination module is used to determine the remaining power of the battery in the vehicle;

[0102] The average energy consumption value determination module is used to determine the average energy consumption value of the vehicle in a predetermined working mode based on historical driving data;

[0103] The remaining mileage determination module is used to determine the remaining mileage of the vehicle based on the remaining power and the average energy consumption value.

[0104] Furthermore, the remaining power determination module includes:

[0105] a rated capacity determination unit, for determining a rated capacity of a battery in the vehicle based on historical driving data;

[0106] a rated total energy determination unit, configured to determine a rated total energy of the battery based on the rated capacity;

[0107] The remaining power determination unit is used to determine the remaining power based on the rated total energy and the state of charge.

[0108] Furthermore, the rated capacity determination unit includes:

[0109] an acquisition subunit, configured to group the historical driving data based on the ambient temperature to acquire the rated capacity in different temperature ranges;

[0110] The determination subunit is used to determine the rated capacity of the battery based on the current ambient temperature of the vehicle.

[0111] Furthermore, the average energy consumption value determination module includes:

[0112] a current operating mode determining unit, configured to determine a current operating mode of the vehicle based on historical driving data;

[0113] An estimation unit, configured to determine an estimated average energy consumption value of the vehicle in the working mode based on historical driving data;

[0114] The correction unit is used to correct the average energy consumption estimate to obtain the average energy consumption value.

[0115] Furthermore, the current working mode determination unit includes:

[0116] a grouping subunit, configured to group the historical driving data based on ambient temperature, average vehicle speed, and state of charge to determine a plurality of operating modes;

[0117] The working mode determination subunit is used to determine the current working mode based on the current ambient temperature, average vehicle speed and charge state of the vehicle.

[0118] Furthermore, the correction unit comprises:

[0119] A prediction subunit, used for predicting the speed value of the vehicle within a predetermined time in the future;

[0120] The correction subunit is used to correct the average energy consumption estimate based on the prediction result.

[0121] Furthermore, the prediction of the vehicle speed value within a predetermined future time is implemented based on a BP neural network, and the BP neural network is trained based on historical driving data.

[0122] The disclosed embodiment establishes an offline model for estimating the remaining mileage through historical driving data, and performs short-term vehicle speed prediction by querying the average energy consumption estimate at the same time, so as to achieve online correction of the average energy consumption value, and finally obtains the remaining mileage estimate in combination with the remaining battery power. The disclosed embodiment combines historical driving data, vehicle speed, temperature, battery SOC and other factors to predict energy consumption, and can predict the energy consumption of the vehicle more quickly and accurately, with a faster response speed, and significantly improves the accuracy of calculating the remaining mileage of the vehicle.

[0123] The third embodiment of the present disclosure provides a storage medium, which is a computer-readable medium and stores a computer program. When the computer program is executed by a processor, the method provided by the first embodiment of the present disclosure is implemented, including the following steps S11 to S13:

[0124] S11, determining the remaining power of the battery in the vehicle;

[0125] S12, determining an average energy consumption value of the vehicle in a predetermined working mode based on historical driving data;

[0126] S13: Determine the remaining mileage of the vehicle based on the remaining power and the average energy consumption value.

[0127] Furthermore, when the computer program is executed by the processor, other methods provided by the first embodiment of the present disclosure are implemented.

[0128] The disclosed embodiment establishes an offline model for estimating the remaining mileage through historical driving data, and performs short-term vehicle speed prediction by querying the average energy consumption estimate at the same time, so as to achieve online correction of the average energy consumption value, and finally obtains the remaining mileage estimate in combination with the remaining battery power. The disclosed embodiment combines historical driving data, vehicle speed, temperature, battery SOC and other factors to predict energy consumption, and can predict the energy consumption of the vehicle more quickly and accurately, with a faster response speed, and significantly improves the accuracy of calculating the remaining mileage of the vehicle.

[0129] The fourth embodiment of the present disclosure provides an electronic device, which includes at least a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the method provided in any embodiment of the present disclosure when executing the computer program in the memory. Exemplarily, the steps of the electronic device computer program are as follows S21 to S23:

[0130] S21, determining the remaining power of the battery in the vehicle;

[0131] S22, determining an average energy consumption value of the vehicle in a predetermined working mode based on historical driving data;

[0132] S23: Determine the remaining mileage of the vehicle based on the remaining power and the average energy consumption value.

[0133] Furthermore, the processor also executes the computer program in the third embodiment

[0134] The disclosed embodiment establishes an offline model for estimating the remaining mileage through historical driving data, and performs short-term vehicle speed prediction by querying the average energy consumption estimate at the same time, so as to achieve online correction of the average energy consumption value, and finally obtains the remaining mileage estimate in combination with the remaining battery power. The disclosed embodiment combines historical driving data, vehicle speed, temperature, battery SOC and other factors to predict energy consumption, and can predict the energy consumption of the vehicle more quickly and accurately, with a faster response speed, and significantly improves the accuracy of calculating the remaining mileage of the vehicle.

[0135] The storage medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0136] The storage medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains at least two Internet Protocol addresses; sends a node evaluation request including at least two Internet Protocol addresses to a node evaluation device, wherein the node evaluation device selects an Internet Protocol address from the at least two Internet Protocol addresses and returns it; receives the Internet Protocol address returned by the node evaluation device; wherein the obtained Internet Protocol address indicates an edge node in a content distribution network.

[0137] Alternatively, the storage medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device: receives a node evaluation request including at least two Internet Protocol addresses; selects an Internet Protocol address from the at least two Internet Protocol addresses; and returns the selected Internet Protocol address; wherein the received Internet Protocol address indicates an edge node in a content distribution network.

[0138] Computer program code for performing operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the passenger computer, partially on the passenger computer, as a separate software package, partially on the passenger computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the passenger computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0139] It should be noted that the storage medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any storage medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0140] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0141] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

[0142] 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: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0143] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0144] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

[0145] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0146] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

[0147] Multiple embodiments of the present disclosure are described in detail above, but the present disclosure is not limited to these specific embodiments. Those skilled in the art can make various variations and modifications to the embodiments based on the concepts of the present disclosure, and these variations and modifications should all fall within the scope of protection required by the present disclosure.

Claims

1. A method for determining the remaining mileage of a vehicle, characterized in that: include: Determining a remaining charge of a battery in a vehicle, including determining a rated capacity of a battery in the vehicle based on historical driving data, determining a rated total energy of the battery based on the rated capacity, and determining the remaining charge based on the rated total energy and a state of charge; Wherein, 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 the rated capacity in different temperature ranges; determining the rated capacity of the battery based on the current ambient temperature of the vehicle; Determining an average energy consumption value of the vehicle in a predetermined working mode based on historical driving data, including determining a current working mode of the vehicle based on historical driving data, determining an estimated average energy consumption value of the vehicle in the working mode based on historical driving data, and correcting the estimated average energy consumption value to obtain the average energy consumption value; The correcting of the average energy consumption estimate to obtain the average energy consumption value comprises: predicting the vehicle speed value of the vehicle within a predetermined time in the future, the predicting of the vehicle speed value of the vehicle within the predetermined time in the future is realized based on a BP neural network, the BP neural network is trained based on historical driving data, the historical driving data at least includes an average vehicle speed, an ambient temperature and a battery state of charge; correcting the average energy consumption estimate based on the prediction result; A remaining range of the vehicle is determined based on the remaining power and the average energy consumption value.

2. The determination method according to claim 1, characterized in that: The determining the current working mode of the vehicle based on the historical driving data includes: grouping the historical driving data based on ambient temperature, average vehicle speed, and state of charge to determine a plurality of operating modes; The current operating mode is determined based on the current ambient temperature, average vehicle speed and state of charge of the vehicle.

3. A device for determining the remaining mileage of a vehicle, characterized in that: include: A remaining power determination module, used to determine the remaining power of a battery in a vehicle; wherein the remaining power determination module includes a rated capacity determination unit, used to determine the rated capacity of the battery in the vehicle based on historical driving data; a rated total energy determination unit, used to determine the rated total energy of the battery based on the rated capacity; and a remaining power determination unit, used to determine the remaining power based on the rated total energy and the state of charge; The rated capacity determination unit includes: an acquisition subunit, which is used to group the historical driving data based on the ambient temperature to obtain the rated capacity in different temperature ranges; a determination subunit, which is used to determine the rated capacity of the battery based on the current ambient temperature of the vehicle; an average energy consumption value determination module, used to determine the average energy consumption value of the vehicle in a predetermined working mode based on historical driving data; wherein the average energy consumption value determination module comprises: a current working mode determination unit, used to determine the current working mode of the vehicle based on historical driving data; an estimation unit, used to determine the estimated average energy consumption value of the vehicle in the working mode based on historical driving data; and a correction unit, used to correct the estimated average energy consumption value to obtain the average energy consumption value; The correction unit includes: a prediction subunit, used to predict the speed value of the vehicle within a predetermined time in the future, the prediction of the speed value of the vehicle within the predetermined time in the future is realized based on a BP neural network, the BP neural network is trained based on historical driving data, and the historical driving data at least includes an average vehicle speed, an ambient temperature and a battery state of charge; a correction subunit, used to correct the average energy consumption estimate based on the prediction result; The remaining mileage determination module is used to determine the remaining mileage of the vehicle based on the remaining power and the average energy consumption value.

4. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to claim 1 or 2 are implemented.

5. An electronic device, comprising at least a memory and a processor, wherein a computer program is stored in the memory, wherein: The processor implements the steps of the method of claim 1 or 2 when executing the computer program on the memory.

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