CN113263954A - Method, device and equipment for predicting driving range of electric automobile and readable storage medium - Google Patents
Method, device and equipment for predicting driving range of electric automobile and readable storage medium Download PDFInfo
- Publication number
- CN113263954A CN113263954A CN202110505343.3A CN202110505343A CN113263954A CN 113263954 A CN113263954 A CN 113263954A CN 202110505343 A CN202110505343 A CN 202110505343A CN 113263954 A CN113263954 A CN 113263954A
- Authority
- CN
- China
- Prior art keywords
- current
- correction factor
- driving range
- temperature
- working condition
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
Landscapes
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Sustainable Development (AREA)
- Sustainable Energy (AREA)
- Power Engineering (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
The embodiment of the invention provides a method, a device, equipment and a readable storage medium for predicting the driving range of an electric automobile, wherein the normal-temperature driving range Y0 under the Chinese working condition is determined according to the current SOC of the automobile and a battery discharge performance curve; determining a temperature correction factor k1 according to the current temperature; determining a correction factor k2 for violent driving according to the characteristics of the current working conditions; determining a speed interval correction factor k3 according to the speed interval of the current vehicle operation and different speed interval data of the Chinese working condition; and determining the current driving range according to the normal-temperature driving range Y0, the temperature correction factor k1, the violent driving correction factor k2 and the speed interval correction factor k3 corresponding to the current SOC under the Chinese working condition. According to the embodiment of the invention, a plurality of influence factors influencing the driving range of the electric automobile are comprehensively considered, and a perfect calculation method is provided, so that more accurate prediction of the current driving range can be provided, and a reference is provided for the trip of a driver.
Description
Technical Field
The invention relates to the field of transportation, in particular to a method, a device, equipment and a readable storage medium for predicting driving range of an electric automobile.
Background
In the 21 st century, the automobile industry in China realizes blowout type development. Since 2009, automobile output and sales in China have remained world first for eight consecutive years. The automobile industry keeps healthy and develops continuously, and the automobile industry becomes a dream of automobile people and even the whole society from the automobile major country to the automobile strong country. In 2016, the external dependency of Chinese crude oil is increased to 65.5%, wherein the oil consumption of automobiles is about 1/3 of the whole oil consumption. The method saves fossil energy and reduces the exhaust emission of automobiles, and is a problem which must be faced and solved by the automobile industry. The vigorous development of the electric vehicle is the most effective way for realizing energy conservation and emission reduction, and 5000 hundred million RMB subsidies are already spent in addition to the support of policies in order to promote the development of the electric vehicle industry. The Ministry of industry and communications has issued the second edition of New energy automobile industry development planning (2021 + 2035), and by 2025, the new automobile sales account for about 25% of the new energy automobile, and by 2035, pure electric vehicles become mainstream, and the fuel cell automobiles are commercially applied, and the vehicles in the public field are fully electric. In 2025 years, the sales volume of new energy automobiles reaches 500 thousands, which is 5 times of the sales volume at present, and in fifteen years, the electricity insertion and hybrid power transmission are in transition, so that the pure electric automobiles are comprehensively popularized in the future.
The electric automobile has great advantages in the aspects of energy conservation and emission reduction, but has a plurality of irrecoverable problems. As the expected mileage is not reached during the use of the electric automobile, the electric automobile has been complained by consumers, and the social public opinion also shows many negative information about the mileage problem of the electric automobile and the electric automobile industry, which causes extensive attention in the industry. The method can be used for scientifically and accurately predicting the driving range of the electric automobile, can be used for remarkably reducing the range anxiety of a user, and has important significance for popularization and promotion of electric automobiles in China.
The driving range of the electric automobile is influenced by various factors, including energy factors and energy consumption factors: firstly, vehicle-mounted residual energy of the electric automobile is mainly related to the battery performance; and the energy consumption comprises energy consumption for driving, energy consumption for vehicle-mounted accessories and the like. In summary, the driving range of the electric vehicle is affected by various common energy consumption factors such as vehicle weight, driving system efficiency, driving conditions, driving habits, and partial environmental factors, as with the conventional fuel vehicle, and the electric vehicle is more sensitive to temperature factors due to the chemical characteristics of the power battery.
The method, the device, the equipment and the readable storage medium for predicting the driving range of the electric vehicle are established by utilizing the actual driving historical data and the rotating hub bench test data of the electric vehicle and comprehensively considering various influence factors on the driving range of the electric vehicle.
Disclosure of Invention
The electric automobile has more energy consumption influence factors, and a complex mapping relation exists between each factor and the actual driving range of the automobile. The current vehicle-mounted driving range prediction model is relatively simple, the influence of factors such as temperature, driving conditions and battery discharge capacity is not comprehensively considered, the prediction precision is relatively low, and the prediction model is a main reason for generating range anxiety of a user. The embodiment of the invention provides a more accurate driving range prediction scheme.
In a first aspect, an embodiment of the present invention provides a method for predicting a driving range of an electric vehicle, including the following steps:
determining battery discharge capacity k0 according to the current SOC and the battery discharge performance curve; determining a normal-temperature driving range Y0 corresponding to the current SOC under the Chinese working condition according to the battery discharging capacity k0 and the driving range L under the Chinese working condition;
determining a temperature correction factor k1 according to the current temperature;
determining a correction factor k2 for violent driving according to the characteristics of the current working condition;
according to different speed interval data of the Chinese working conditions, mutual information of the current working condition characteristics and the working condition characteristics of each interval of the Chinese working conditions is calculated, an interval corresponding to the maximum value of the mutual information is a speed interval of the current vehicle, and a speed interval correction factor k3 is determined;
and determining the current driving range according to the normal-temperature driving range Y0, the temperature correction factor k1, the violent driving correction factor k2 and the speed interval correction factor k3 corresponding to the current SOC under the Chinese working condition.
Preferably, the calculation formula of the normal-temperature driving range Y0 corresponding to the current SOC under the chinese working condition is as follows:
Y0＝L*k0；
wherein, L is the driving range of the Chinese working condition, and k0 is the discharge capacity of the battery.
Preferably, the vehicle is tested on the rotating hub at 23 ℃ and a preset typical temperature for the driving range of the air conditioner under the Chinese working condition in the opening and closing state, the driving range corresponding to each temperature point is obtained by fitting through a cubic spline difference value, and the calculation formula of the temperature correction factor is as follows:
the air conditioner is in an open state:
k1＝a1/b1
wherein a1 is the discharge capacity of the air conditioner at the current temperature in the on state; b1 discharge capacity at 23 deg.C when the air conditioner is on;
the air conditioner is in an off state:
k1＝a2/b2
wherein a2 is the discharge capacity of the air conditioner at the current temperature in the off state; b2 is the discharge capacity at 23 ℃ in the air conditioner off state.
Preferably, the electric vehicle driving data of the preset driving mileage is divided into an idle speed segment and a motion segment, the average speed and the average RPA value m of three complete motion segments before the current moment are calculated, and if the average speed and the average RPA value m are greater than the RPA 90% fractional value n of the corresponding speed segment of the average speed of the three segments, the violent driving correction factor k2 is n/m; otherwise k2 is 1.
Preferably, the calculation formula of RPA (Relative positive acceleration) is as follows:
i-segment time(s);
vi-vehicle speed at segment ith second (m/s);
ai + -a value for acceleration greater than 0m/s2 (m/s 2);
x-vehicle mileage (m).
Preferably, mutual information of the current working condition characteristics and the working condition characteristics of each interval of the Chinese working conditions is calculated, the interval corresponding to the maximum value of the mutual information is the speed interval of the current vehicle, and the calculation formula of the correction factor corresponding to the speed interval is as follows:
k3＝Lj/L；
wherein j is the number of the speed interval, Lj is the driving range of the speed interval j, and L is the driving range of the Chinese working condition;
the current working condition characteristics comprise the highest vehicle speed, the acceleration/deceleration/constant speed/idle speed ratio and the acceleration standard deviation.
Preferably, the calculation formula of the current driving range is as follows:
Y＝Y0*k1*k2*k3。
in a second aspect, an embodiment of the present invention provides an apparatus for predicting a driving mileage of an electric vehicle, including:
the normal-temperature endurance mileage calculation module is used for determining the battery discharge capacity k0 according to the current SOC and the battery discharge performance curve; determining a normal-temperature driving range Y0 corresponding to the current SOC under the Chinese working condition according to the battery discharging capacity k0 and the driving range L under the Chinese working condition;
the temperature correction factor calculation module is used for determining a temperature correction factor k1 according to the current temperature;
the violent driving correction factor calculation module determines a violent driving correction factor k2 according to the current working condition characteristics;
the speed interval correction factor calculation module is used for calculating mutual information of the current working condition characteristics and the working condition characteristics of each interval of the Chinese working conditions according to different speed interval data of the Chinese working conditions, an interval corresponding to the maximum value of the mutual information is a speed interval of the current vehicle, and a speed interval correction factor k3 is determined;
and the current driving range calculating module determines the current driving range according to the normal-temperature driving range Y0, the temperature correction factor k1, the violent driving correction factor k2 and the speed interval correction factor k3 corresponding to the current SOC under the Chinese working condition.
In a third aspect, an embodiment of the present invention provides an electronic device, including:
one or more processors;
a memory for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the electric vehicle range prediction method as described above.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the method for predicting the driving mileage of an electric vehicle as described above.
Compared with the prior art, the embodiment of the invention has the following specific beneficial effects:
the embodiment of the invention utilizes the actual driving history data and the hub test data of the electric vehicle, comprehensively considers various influence factors of the driving range of the electric vehicle, and particularly comprehensively considers influence factors such as a battery discharge performance curve, the normal-temperature driving range corresponding to the current SOC, the driving range under the Chinese working condition, a temperature correction factor, a violent driving correction factor, a speed interval correction factor and the like, and provides a reasonable calculation method aiming at the normal-temperature driving range, the temperature correction factor, the violent driving correction factor and the speed interval correction factor corresponding to the current SOC, so that the method for predicting the driving range of the electric vehicle with higher accuracy is provided, and help is provided for reasonably arranging a trip plan for a driver.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flowchart of a driving range prediction method for an electric vehicle according to an embodiment of the present invention;
FIG. 2 is a graph of the discharge performance of a battery according to an embodiment of the present invention;
FIG. 3 is a temperature correction index (air conditioner on state) according to an embodiment of the present invention;
FIG. 4 is a temperature correction index (air conditioner off state) according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of the idle segment and the motion segment segmentation of an embodiment of the present invention;
FIG. 6 is a schematic structural diagram of an electric vehicle driving range predicting apparatus according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
The method for predicting the driving range of the electric vehicle provided by the embodiment of the invention has the method flow chart shown in fig. 1, and can be executed by an on-board computing device of the electric vehicle, wherein the device can be formed by software and/or hardware and is generally integrated in electronic equipment.
With reference to fig. 1, the method for predicting the driving range of the electric vehicle according to the embodiment of the present invention includes the following steps:
step S110, determining battery discharge capacity k0 according to the current SOC and the battery discharge performance curve; determining a normal-temperature driving range Y0 corresponding to the current SOC under the Chinese working condition according to the battery discharging capacity k0 and the driving range L under the Chinese working condition;
the normal-temperature driving range Y0 corresponding to the current SOC under the Chinese working condition has the calculation formula as follows:
Y0＝L*k0。
specifically, a constant speed test of 80km/h is carried out on the rotating hub, the temperature of a laboratory is set to be 23 ℃, SOC and vehicle driving mileage data are collected, and a battery discharge performance curve is obtained according to the SOC and the vehicle driving mileage data, and the reference is made to the attached figure 2.
In step S120, a temperature correction factor k1 is determined according to the current temperature.
The driving range of the vehicle under the Chinese working condition in the opening and closing state of the air conditioner is tested on the rotating hub at 23 ℃ and the preset typical temperature, and the driving range corresponding to each temperature point is obtained by fitting through a cubic spline difference value, as shown in fig. 3 (the opening state of the air conditioner) and fig. 4 (the closing state of the air conditioner). According to the embodiment of the invention, fitting is carried out through the cubic spline difference value, so that the prediction result is more accurate. Preferably, the predetermined typical temperature is-17 deg.C, -7 deg.C, 3 deg.C, 13 deg.C, 23 deg.C, 33 deg.C, 43 deg.C.
The temperature correction factor calculation formula is as follows:
air conditioner on state
k1＝a1/b1
Wherein a1 is the discharge capacity of the air conditioner at the current temperature in the on state; b1 the on state of the air conditioner is the discharge capacity at 23 ℃.
Off state of air conditioner
k1＝a2/b2
Wherein a2 is the discharge capacity of the air conditioner at the current temperature in the off state; b2 is the discharge capacity at 23 ℃ in the air conditioner off state.
Step S130, determining a correction factor k2 for violent driving according to the characteristics of the current working conditions;
specifically, the driving data of the electric vehicle with the preset driving mileage is cut into an idle speed segment and a motion segment, the average speed and the average RPA value m of three complete motion segments before the current moment are calculated, and if the m is larger than the RPA 90% quantile value n of the speed segment corresponding to the average speed of the three segments (the n value is obtained by cutting short segments through all the actual driving data of the vehicle, calculating the PPA distribution of different speed segments and selecting a 90% quantile value), the violent driving correction factor k2 is n/m; otherwise k2 is 1.
The calculation formula of RPA (Relative positive acceleration) is as follows:
i-segment time(s);
vi-speed of vehicle at segment i second (m/s)
ai + -value for acceleration greater than 0m/s2 (m/s2)
x-vehicle mileage (m)
Preferably, the preset driving mileage is greater than 10 kilometers, so as to ensure the accuracy of the prediction result of the embodiment of the invention.
The method for cutting the electric vehicle driving data with the preset driving mileage into short segments comprises the steps of cutting the electric vehicle driving data into short segments according to the idling and moving scenes of the electric vehicle, wherein the short segments comprise the idling segments and the moving segments. Specifically, the electric vehicle starts from a start position to a stop position, and is influenced by road traffic conditions, and multiple start and stop operations are performed during the process. As shown in fig. 5, the movement of the vehicle from the start of one stop to the start of the next start is defined as an idle segment; the motion of the vehicle from one start to the next stop is defined as a motion segment. Thus, a journey of the vehicle can be regarded as various short segment combinations, and therefore the electric vehicle driving data of the preset driving mileage can be cut into a plurality of short segments.
In a specific embodiment of the invention, the acquisition frequency of the vehicle-mounted data acquisition terminal is 20Hz, and before the electric vehicle driving data with the preset driving mileage is cut into short segments, the data needs to be resampled, so that the data frequency is changed into 1 Hz.
Specifically, the setting method of the speed section comprises the following steps: from 0km/h, a speed section is set every 5km/h speed interval. For example, set 0km/h-5km/h as the first speed segment, j ═ 1; 5km/h-10km/h is a second speed section, j is 2, 10km/h-15km/h is a third speed section, and j is 3; wherein j is a speed interval number; when the average speed of the short segment is 3km/h, the short segment is positioned in a first speed segment; when the average speed of the short segment is 10km/h, the short segment is located in the third speed segment. 5km/h is the preferred speed segment interval through the actual prediction result. The speed interval may also be set to a range of 3km/h to 10 km/h.
Step S140, according to different speed interval data of Chinese working conditions, mutual information of current working condition characteristics (maximum vehicle speed, acceleration/deceleration/constant speed/idle speed ratio, acceleration standard deviation and the like) and working condition characteristics of each interval of the Chinese working conditions is calculated, an interval corresponding to the maximum value of the mutual information is a speed interval of current vehicle operation, and a speed interval correction factor k3 is determined.
Specifically, the calculation formula of the speed interval correction factor is as follows:
k3＝Lj/L；
wherein j is the number of the speed interval, Lj is the driving range of the speed interval j, and L is the driving range of the Chinese working condition. Specifically, j-1 represents an urban section, j-2 represents a suburban section, and j-3 represents a high-speed section.
And S150, determining the current driving range according to the normal-temperature driving range Y0, the temperature correction factor k1, the violent driving correction factor k2 and the speed interval correction factor k3 corresponding to the current SOC under the Chinese working condition.
Specifically, the current driving range is calculated by the following formula:
Y＝Y0*k1*k2*k3。
in a specific embodiment of the invention, the actual operation data of 20 pure electric class B vehicles of a certain model are collected by an autonomous driving method, the collection time is from 5/1/2020 to 11/30/2020, and the accumulated driving mileage is 10.6 kilometers. The electric automobile driving data acquisition device is composed of a vehicle-mounted data acquisition terminal (the sampling frequency is 20Hz) and a data management platform, wherein the vehicle-mounted data acquisition terminal encodes acquired information according to a uniform data protocol and transmits the encoded information to the working condition data management platform in real time through a GPRS network.
According to the result of Chinese working condition driving range test at 23 ℃, the normal temperature driving range Y0-L-k 0-563.3-km-0.71-399.7 km corresponding to the Chinese working condition under the current SOC (72%) is obtained. The battery discharge performance curve is shown in fig. 2.
According to the current temperature (27 ℃) and the air conditioner opening state, the temperature correction factor k1 is determined to be 0.94. The temperature correction factor is schematically shown in fig. 3.
According to the current operating condition characteristics, m is calculated to be larger than n, and the violent driving correction factor k2 is determined to be 0.97.
And (3) calculating mutual information of the current working condition characteristics and the working condition characteristics of each section of the Chinese working condition, wherein the maximum value of the mutual information corresponds to the suburb section, and obtaining a correction factor k3 corresponding to the speed section as 1.01.
The current driving range Y — Y0 — k1 — k2 — k3 — 368.1 km.
Referring to fig. 6, the embodiment of the present invention further provides an apparatus for predicting driving range of an electric vehicle, including a normal temperature driving range calculating module 210, a temperature correction factor calculating module 220, a violent driving correction factor calculating module 230, a speed interval correction factor calculating module 240, and a current driving range calculating module 250.
The normal-temperature endurance mileage calculation module is used for determining the battery discharge capacity k0 according to the current SOC and the battery discharge performance curve; determining a normal-temperature driving range Y0 corresponding to the current SOC under the Chinese working condition according to the battery discharging capacity k0 and the driving range L under the Chinese working condition;
the temperature correction factor calculation module is used for determining a temperature correction factor k1 according to the current temperature;
the violent driving correction factor calculation module determines a violent driving correction factor k2 according to the current working condition characteristics;
the speed interval correction factor calculation module is used for calculating mutual information of current working condition characteristics (highest vehicle speed, acceleration/deceleration/constant speed/idle speed ratio, acceleration standard deviation and the like) and the working condition characteristics of each interval of the Chinese working conditions according to different speed interval data of the Chinese working conditions, wherein the interval corresponding to the maximum value of the mutual information is a speed interval in which the current vehicle runs, and determining a speed interval correction factor k 3;
and the current driving range calculating module determines the current driving range according to the normal-temperature driving range Y0, the temperature correction factor k1, the violent driving correction factor k2 and the speed interval correction factor k3 corresponding to the current SOC under the Chinese working condition.
Optionally, the calculation formula of the normal-temperature driving range Y0 corresponding to the current SOC under the chinese working condition is as follows:
Y0＝L*k0。
optionally, the vehicle is tested for the driving range of the Chinese working condition under the opening and closing states of the air conditioner at 23 ℃ and the preset typical temperature on the rotating hub, and the driving range corresponding to each temperature point is obtained by fitting the three-time spline difference value, so that the temperature correction factor calculation formula is as follows:
k1＝a/b；
wherein a is the discharge capacity at the current temperature; b is the discharge capacity at 23 ℃.
Optionally, the electric vehicle driving data of the preset driving mileage is divided into an idle speed segment and a motion segment, the average speed and the average RPA value m of three complete motion segments before the current time are calculated, and if the average speed and the average RPA value m are greater than the RPA 90% fractional value n of the speed interval corresponding to the average speed of the three segments, the violent driving correction factor k2 is n/m; otherwise k2 is 1.
Optionally, the calculation formula of the RPA (Relative positive acceleration) is as follows:
i-segment time(s);
vi-vehicle speed at segment ith second (m/s);
ai + -a value for acceleration greater than 0m/s2 (m/s 2);
x-vehicle mileage (m).
Optionally, the calculation formula of the speed interval correction factor is as follows:
k3＝Lj/L；
wherein j is the number of the speed interval, Lj is the driving range of the speed interval j, and L is the driving range of the Chinese working condition.
Optionally, the calculation formula of the current driving range is as follows:
Y＝Y0*k1*k2*k3。
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, as shown in fig. 7, the electronic device includes a processor 50, a memory 51, an input device 52, and an output device 53; the number of processors 50 in the device may be one or more, and one processor 50 is taken as an example in fig. 7; the processor 50, the memory 51, the input device 52 and the output device 53 in the apparatus may be connected by a bus or other means, which is exemplified in fig. 7.
The memory 51, which is a computer-readable storage medium, may be used to store software programs, computer-executable programs, and modules. The processor 50 executes various functional applications of the device and data processing by running software programs, instructions and modules stored in the memory 51, so as to realize the electric vehicle driving range prediction method.
The memory 51 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the memory 51 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, the memory 51 may further include memory located remotely from the processor 50, which may be connected to the device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 52 is operable to receive input numeric or character information and to generate key signal inputs relating to user settings and function controls of the apparatus. The output device 53 may include a display device such as a display screen.
The present embodiment also provides a medium having stored thereon computer instructions for causing the computer to execute the above-mentioned calculation method. The medium can make the computer execute the above-mentioned calculation method, so that it has the advantages of high calculation accuracy rate and low cost.
The medium of the present invention may take the form of any combination of one or more computer-readable media. The medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: 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 document, 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.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable 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 medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, or the like, as well as conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It should be understood that various forms of the flows shown above, reordering, adding or deleting steps, may be used. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The method for predicting the driving range of the electric automobile is characterized by comprising the following steps of:
determining battery discharge capacity k0 according to the current SOC and the battery discharge performance curve; determining a normal-temperature driving range Y0 corresponding to the current SOC under the Chinese working condition according to the battery discharging capacity k0 and the driving range L under the Chinese working condition;
determining a temperature correction factor k1 according to the current temperature;
determining a correction factor k2 for violent driving according to the characteristics of the current working condition;
according to different speed interval data of the Chinese working conditions, mutual information of the current working condition characteristics and the working condition characteristics of each interval of the Chinese working conditions is calculated, an interval corresponding to the maximum value of the mutual information is a speed interval of the current vehicle, and a speed interval correction factor k3 is determined;
and determining the current driving range according to the normal-temperature driving range Y0, the temperature correction factor k1, the violent driving correction factor k2 and the speed interval correction factor k3 corresponding to the current SOC under the Chinese working condition.
2. The method of claim 1, wherein the normal-temperature driving range Y0 corresponding to the current SOC under the Chinese working condition is calculated according to the formula:
Y0＝L*k0；
wherein, L is the driving range of the Chinese working condition, and k0 is the discharge capacity of the battery.
3. The method according to claim 1, wherein the driving range of the air conditioner under the open and close states is tested at 23 ℃ and the preset typical temperature of the vehicle on the rotating hub, and the driving range corresponding to each temperature point is obtained by fitting the three-time spline difference, and the temperature correction factor calculation formula is as follows:
the air conditioner is in an open state:
k1＝a1/b1
wherein a1 is the discharge capacity of the air conditioner at the current temperature in the on state; b1 discharge capacity at 23 deg.C when the air conditioner is on;
the air conditioner is in an off state:
k1＝a2/b2
wherein a2 is the discharge capacity of the air conditioner at the current temperature in the off state; b2 is the discharge capacity at 23 ℃ in the air conditioner off state.
4. The method of claim 1, wherein electric vehicle driving data of a preset driving mileage is divided into an idle speed segment and a sport segment, and an average speed and an average RPA value m of three complete sport segments before a current time are calculated, and if the value is greater than an RPA 90% fractional value n of a speed segment corresponding to an average speed of three segments, a violent driving correction factor k2 is n/m; otherwise k2 is 1.
5. The method of claim 4, wherein the calculation formula of RPA (Relative positive acceleration) is as follows:
i-segment time(s);
vi-vehicle speed at segment ith second (m/s);
ai + -a value for acceleration greater than 0m/s2 (m/s 2);
x-vehicle mileage (m).
6. The method according to claim 1, wherein mutual information of the current working condition characteristics and the working condition characteristics of each interval of the Chinese working conditions is calculated, the interval corresponding to the maximum value of the mutual information is the speed interval of the current vehicle, and the calculation formula of the correction factor corresponding to the speed interval is as follows:
k3＝Lj/L；
wherein j is the number of the speed interval, Lj is the driving range of the speed interval j, and L is the driving range of the Chinese working condition;
the current working condition characteristics comprise the highest vehicle speed, the acceleration/deceleration/constant speed/idle speed ratio and the acceleration standard deviation.
7. The method of claim 1, wherein the current driving range is calculated by the formula:
Y＝Y0*k1*k2*k3。
8. an electric vehicle driving mileage predicting device, comprising:
the normal-temperature endurance mileage calculation module is used for determining the battery discharge capacity k0 according to the current SOC and the battery discharge performance curve; determining a normal-temperature driving range Y0 corresponding to the current SOC under the Chinese working condition according to the battery discharging capacity k0 and the driving range L under the Chinese working condition;
the temperature correction factor calculation module is used for determining a temperature correction factor k1 according to the current temperature;
the violent driving correction factor calculation module determines a violent driving correction factor k2 according to the current working condition characteristics;
the speed interval correction factor calculation module is used for calculating mutual information of the current working condition characteristics and the working condition characteristics of each interval of the Chinese working conditions according to different speed interval data of the Chinese working conditions, an interval corresponding to the maximum value of the mutual information is a speed interval of the current vehicle, and a speed interval correction factor k3 is determined;
and the current driving range calculating module determines the current driving range according to the normal-temperature driving range Y0, the temperature correction factor k1, the violent driving correction factor k2 and the speed interval correction factor k3 corresponding to the current SOC under the Chinese working condition.
9. An electronic device, comprising:
one or more processors;
a memory for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the electric vehicle range prediction method of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the electric vehicle mileage prediction method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110505343.3A CN113263954B (en) | 2021-05-10 | 2021-05-10 | Method, device and equipment for predicting driving range of electric vehicle and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110505343.3A CN113263954B (en) | 2021-05-10 | 2021-05-10 | Method, device and equipment for predicting driving range of electric vehicle and readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113263954A true CN113263954A (en) | 2021-08-17 |
| CN113263954B CN113263954B (en) | 2022-07-22 |
Family
ID=77230316
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110505343.3A Active CN113263954B (en) | 2021-05-10 | 2021-05-10 | Method, device and equipment for predicting driving range of electric vehicle and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113263954B (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113978311A (en) * | 2021-10-15 | 2022-01-28 | 潍柴动力股份有限公司 | Battery temperature correction method and device and electronic equipment |
| CN113978316A (en) * | 2021-10-21 | 2022-01-28 | 潍柴动力股份有限公司 | Method and device for calculating cruising mileage and storage medium |
| CN114889492A (en) * | 2022-05-09 | 2022-08-12 | 潍柴动力股份有限公司 | Method, device and system for estimating endurance mileage of electric vehicle and storage medium |
| CN115096331A (en) * | 2022-05-16 | 2022-09-23 | 中国第一汽车股份有限公司 | Vehicle mileage error judgment method and device based on cloud service, and vehicle mileage error correction method and device based on cloud service |
| CN115655753A (en) * | 2022-12-26 | 2023-01-31 | 中汽科技(北京)有限公司 | Electric automobile endurance mileage testing method, system, device and storage medium |
| CN118275886A (en) * | 2024-05-31 | 2024-07-02 | 中汽研（天津）汽车工程研究院有限公司 | Method and device for evaluating driving range optimization potential, medium and equipment |
| CN118654902A (en) * | 2024-08-19 | 2024-09-17 | 中汽研汽车检验中心（天津）有限公司 | Method, device, medium and equipment for developing extreme test working conditions of high-temperature and high-cold automobile |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101071889A (en) * | 2006-05-10 | 2007-11-14 | 财团法人工业技术研究院 | Intelligent electric vehicle power supply display system |
| CN101259843A (en) * | 2006-11-28 | 2008-09-10 | 通用汽车环球科技运作公司 | Range maximization of a hybrid vehicle operating in an electric vehicle operating state |
| CN102358190A (en) * | 2011-09-08 | 2012-02-22 | 重庆长安汽车股份有限公司 | Method for estimating surplus mileage of pure electric vehicle based on power consumption per kilometer |
| US20120143435A1 (en) * | 2010-12-03 | 2012-06-07 | Kia Motors Corporation | Method for estimating remaining travel distance of electric vehicle |
| CN103033752A (en) * | 2011-09-30 | 2013-04-10 | 吴昌旭 | Predication method and prolonging method for service life of electric vehicle battery |
| CN103273921A (en) * | 2013-06-14 | 2013-09-04 | 清华大学 | Method for estimating driving range of electric car |
| CN105291845A (en) * | 2015-11-13 | 2016-02-03 | 华晨汽车集团控股有限公司 | System for monitoring dynamic energy consumption and driving range of electric automobile |
| US20160114698A1 (en) * | 2014-10-28 | 2016-04-28 | North Carolina State University | Method and apparatus for analyzing data related to vehicle range estimation |
| CN105904981A (en) * | 2016-04-07 | 2016-08-31 | 北京现代汽车有限公司 | Electric car driving mileage estimation control method and device, and vehicle control unit |
| EP3069923A1 (en) * | 2015-03-18 | 2016-09-21 | IVECO S.p.A. | Estimation method of a residual electric vehicle range |
| WO2017071657A1 (en) * | 2015-10-30 | 2017-05-04 | 北京新能源汽车股份有限公司 | Method and system for estimating remaining range of electric car, and electric car |
| CN109506946A (en) * | 2018-10-19 | 2019-03-22 | 中国汽车技术研究中心有限公司 | A kind of exploitation of RDE operating condition library and application method |
| CN112124146A (en) * | 2020-09-10 | 2020-12-25 | 华人运通(江苏)技术有限公司 | Method, device and system for predicting driving range of vehicle and storage medium |
-
2021
- 2021-05-10 CN CN202110505343.3A patent/CN113263954B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101071889A (en) * | 2006-05-10 | 2007-11-14 | 财团法人工业技术研究院 | Intelligent electric vehicle power supply display system |
| CN101259843A (en) * | 2006-11-28 | 2008-09-10 | 通用汽车环球科技运作公司 | Range maximization of a hybrid vehicle operating in an electric vehicle operating state |
| US20120143435A1 (en) * | 2010-12-03 | 2012-06-07 | Kia Motors Corporation | Method for estimating remaining travel distance of electric vehicle |
| CN102358190A (en) * | 2011-09-08 | 2012-02-22 | 重庆长安汽车股份有限公司 | Method for estimating surplus mileage of pure electric vehicle based on power consumption per kilometer |
| CN103033752A (en) * | 2011-09-30 | 2013-04-10 | 吴昌旭 | Predication method and prolonging method for service life of electric vehicle battery |
| CN103273921A (en) * | 2013-06-14 | 2013-09-04 | 清华大学 | Method for estimating driving range of electric car |
| US20160114698A1 (en) * | 2014-10-28 | 2016-04-28 | North Carolina State University | Method and apparatus for analyzing data related to vehicle range estimation |
| EP3069923A1 (en) * | 2015-03-18 | 2016-09-21 | IVECO S.p.A. | Estimation method of a residual electric vehicle range |
| WO2017071657A1 (en) * | 2015-10-30 | 2017-05-04 | 北京新能源汽车股份有限公司 | Method and system for estimating remaining range of electric car, and electric car |
| CN105291845A (en) * | 2015-11-13 | 2016-02-03 | 华晨汽车集团控股有限公司 | System for monitoring dynamic energy consumption and driving range of electric automobile |
| CN105904981A (en) * | 2016-04-07 | 2016-08-31 | 北京现代汽车有限公司 | Electric car driving mileage estimation control method and device, and vehicle control unit |
| CN109506946A (en) * | 2018-10-19 | 2019-03-22 | 中国汽车技术研究中心有限公司 | A kind of exploitation of RDE operating condition library and application method |
| CN112124146A (en) * | 2020-09-10 | 2020-12-25 | 华人运通(江苏)技术有限公司 | Method, device and system for predicting driving range of vehicle and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| ANCONA, E AND KEZERASHVILI, RY: "Extrasolar space exploration by a solar sail accelerated via thermal desorption of coating", 《ADVANCES IN SPACE RESEARCH》 * |
| 郑宁安: "纯电动汽车能耗预测与续驶里程估算", 《中国优秀博硕士学位论文全文数据库(硕士)工程科技Ⅱ辑》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113978311A (en) * | 2021-10-15 | 2022-01-28 | 潍柴动力股份有限公司 | Battery temperature correction method and device and electronic equipment |
| CN113978311B (en) * | 2021-10-15 | 2024-05-17 | 潍柴动力股份有限公司 | Battery temperature correction method and device and electronic equipment |
| CN113978316A (en) * | 2021-10-21 | 2022-01-28 | 潍柴动力股份有限公司 | Method and device for calculating cruising mileage and storage medium |
| CN113978316B (en) * | 2021-10-21 | 2024-04-16 | 潍柴动力股份有限公司 | Calculation method, device and storage medium for cruising mileage |
| CN114889492A (en) * | 2022-05-09 | 2022-08-12 | 潍柴动力股份有限公司 | Method, device and system for estimating endurance mileage of electric vehicle and storage medium |
| CN115096331A (en) * | 2022-05-16 | 2022-09-23 | 中国第一汽车股份有限公司 | Vehicle mileage error judgment method and device based on cloud service, and vehicle mileage error correction method and device based on cloud service |
| CN115096331B (en) * | 2022-05-16 | 2024-06-11 | 中国第一汽车股份有限公司 | Cloud service-based vehicle mileage error judging method, device, correcting method and device |
| CN115655753A (en) * | 2022-12-26 | 2023-01-31 | 中汽科技(北京)有限公司 | Electric automobile endurance mileage testing method, system, device and storage medium |
| CN118275886A (en) * | 2024-05-31 | 2024-07-02 | 中汽研（天津）汽车工程研究院有限公司 | Method and device for evaluating driving range optimization potential, medium and equipment |
| CN118654902A (en) * | 2024-08-19 | 2024-09-17 | 中汽研汽车检验中心（天津）有限公司 | Method, device, medium and equipment for developing extreme test working conditions of high-temperature and high-cold automobile |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113263954B (en) | 2022-07-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113263954B (en) | Method, device and equipment for predicting driving range of electric vehicle and readable storage medium | |
| US20240302178A1 (en) | Displaying charging options for an electric vehicle | |
| WO2021244632A1 (en) | Electric automobile energy consumption prediction method and system | |
| US8014914B2 (en) | Energy and emission responsive routing for vehicles | |
| CN109808541B (en) | Electric vehicle charging method and system | |
| CN107284241B (en) | Server for providing battery display status of vehicle and apparatus for setting display status | |
| US20210097781A1 (en) | Route-based vehicle selection | |
| US8589076B2 (en) | Power usage planning for a vehicle | |
| CN110775065A (en) | Hybrid electric vehicle battery life prediction method based on working condition recognition | |
| Ferreira et al. | Dynamic range prediction for an electric vehicle | |
| CN110103773A (en) | A kind of electric car continual mileage prediction technique and system | |
| JP2011140948A (en) | Method, system, and apparatus for operating vehicle | |
| CN103745111A (en) | Method of predicting driving range of all-electric passenger vehicles | |
| CN112703518A (en) | Remaining mileage prediction method and battery remote service system | |
| JP7565667B2 (en) | Method and device for predicting energy consumption of a vehicle air conditioner based on work condition identification | |
| CN113771778A (en) | Dynamic vehicle component data device, system and method | |
| WO2024082201A1 (en) | Cruising range estimation method and apparatus | |
| Fanti et al. | An Innovative Service for Electric Vehicle Energy Demand Prediction | |
| KR20230170305A (en) | Method and apparatus for evaluating driving index of a vehicle | |
| CN115583153A (en) | Endurance mileage calculation method and device and computer equipment | |
| KR20230147435A (en) | Method and apparatus for predicting vehicle availability | |
| CN116186465A (en) | Electric automobile energy consumption calculation method and device and electronic equipment | |
| Shao et al. | An intelligent charging navigation scheme for electric vehicles using a cloud computing platform | |
| CN118144577B (en) | Method, device, equipment and medium for controlling starting of range extender of vehicle | |
| CN112700117B (en) | Data-based electric vehicle charging infrastructure service condition estimation method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |