Method, system and equipment for predicting endurance mileage of electric vehicle and medium

Abstract

The invention discloses a method, a system, equipment and a medium for predicting the endurance mileage of an electric vehicle, relates to the field of electric vehicles, and solves the problem that a relatively large error exists between a prediction result and a real endurance mileage due to the fact that factors such as an environment outside the vehicle are not considered in a traditional endurance mileage prediction method. The prediction method specifically comprises the following steps that the information collection module obtains vehicle body configuration information of the electric vehicle, the battery analysis module preliminarily analyzes the endurance condition of a battery in the electric vehicle, the driving process of the electric vehicle is divided into a plurality of driving cycles, and the driving monitoring module obtains driving state data of the electric vehicle in each driving cycle and sends the driving state data to the electric vehicle. The driving analysis module analyzes driving behaviors of the electric vehicle, the driving monitoring module further collects environment influence data of all driving cycles of the electric vehicle, the prediction correction module comprehensively corrects the endurance journey of the electric vehicle, and intelligent prediction of the endurance mileage of the electric vehicle is achieved by integrating multiple influence factors.

Description

Technical Field

[0001] The present invention belongs to the field of electric vehicles and relates to electric vehicle range prediction technology, specifically to a method, system, device and medium for predicting the range of an electric vehicle. Background Art

[0002] With the popularity of new energy vehicles represented by electric vehicles, electric vehicles have become an important part of the world's modern transportation system. As a result, users have an increasing demand for accurate prediction of their mileage. At present, the existing electric vehicle mileage prediction solutions on the market are divided into two categories. One is based on the world's commonly used standards such as CLTC, NEDC, and WLTP; the other type of mileage prediction solution is a dynamic estimate based on the average energy consumption of the vehicle over a period of time or driving distance. For example, the power consumption per 100 kilometers is calculated based on the power consumed by the vehicle in the past 50 or 100 kilometers, or the average power consumption is obtained based on the vehicle's driving time in the past half an hour, and then the remaining power of the vehicle is divided by the average power consumption to get the mileage.

[0003] However, the traditional range prediction method has not yet considered the impact of factors such as the external environment, road conditions, driving style, number of people in the car, and whether electrical equipment is used on the range, resulting in a large error between the prediction result and the actual range. As a result, users cannot accurately know the actual range based on the range displayed on the dashboard, which can easily cause users to have "range anxiety". Therefore, how to achieve dynamic intelligent prediction of electric vehicle range is the current problem;

[0004] To this end, we propose a method, system, device and medium for predicting the range of electric vehicles. Summary of the invention

[0005] The purpose of the present invention is to propose a method, system, device and medium for predicting the cruising range of an electric vehicle to solve the problems raised in the above background technology.

[0006] The technical problems to be solved by the present invention are:

[0007] How to integrate multiple influencing factors to achieve intelligent prediction of electric vehicle range.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] In the first aspect, a method for predicting the cruising range of an electric vehicle is provided. The prediction method is specifically as follows:

[0010] Step S101, the information acquisition module acquires the body configuration information of the electric vehicle and sends it to the processor, and the processor sends the body configuration information of the electric vehicle to the battery analysis module;

[0011] Step S102, the battery analysis module performs a preliminary analysis on the battery life of the electric vehicle, and the estimated battery life of the electric vehicle obtained by the analysis is sent to the prediction correction module via the processor;

[0012] Step S103, dividing the driving process of the electric vehicle into a number of driving cycles, the driving monitoring module acquires the driving state data of each driving cycle of the electric vehicle and sends it to the driving analysis module via the processor;

[0013] Step S104, the driving analysis module analyzes the driving behavior of the electric vehicle, and the additional energy consumption value of each driving cycle of the electric vehicle obtained by the analysis is sent to the prediction correction module through the processor;

[0014] Step S105, the driving monitoring module also collects environmental impact data of each driving cycle of the electric vehicle, and the environmental impact data is sent to the prediction and correction module via the processor;

[0015] Step S106, the prediction correction module performs a comprehensive correction on the cruising range of the electric vehicle, and sends the corrected cruising range of the electric vehicle to the processor, and the processor sends the corrected cruising range of the electric vehicle to the display for display.

[0016] Furthermore, the vehicle configuration information includes the production timestamp of the electric vehicle, the total vehicle weight, the driving mode, the battery rated capacity, the battery rated voltage, the battery rated current and the electric energy conversion efficiency;

[0017] The driving cycle includes an acceleration phase, a deceleration phase and a constant speed phase.

[0018] Furthermore, in step S102, the analysis process of the battery analysis module is specifically as follows:

[0019] Obtain the production timestamp and current timestamp of the electric vehicle, subtract the production timestamp from the current timestamp to calculate the actual running time of the electric vehicle, and compare the actual running time of the electric vehicle with the running time interval;

[0020] According to the operating time interval to which the actual operating time belongs, the battery attenuation coefficient of the electric vehicle is obtained; wherein, the larger the value of the battery attenuation coefficient is, the faster the attenuation rate of the battery capacity is;

[0021] Obtain the total vehicle weight and driving mode of the electric vehicle, and calculate the electric energy conversion resistance value of the electric vehicle;

[0022] Then the battery rated capacity, battery rated voltage, battery rated current and power conversion efficiency of the electric vehicle are obtained, and the expected endurance of the electric vehicle is calculated.

[0023] Furthermore, in step S103, the driving monitoring process of the driving monitoring module is specifically as follows:

[0024] If the electric vehicle is in a constant speed driving state, the constant speed cruising speed and constant speed driving time of the electric vehicle are obtained;

[0025] If the electric vehicle is in an accelerating state, the acceleration start speed, acceleration end speed and acceleration duration of the electric vehicle are obtained;

[0026] If the electric vehicle is in a decelerating driving state, the deceleration start speed, deceleration end speed and deceleration duration of the electric vehicle are obtained.

[0027] Furthermore, in step S104, the analysis process of the driving analysis module is specifically as follows:

[0028] Obtain the cruise speed, cruise time, acceleration start speed, acceleration end speed, acceleration time, deceleration start speed, deceleration end speed and deceleration time of each driving cycle of the electric vehicle;

[0029] Calculate the additional energy consumption of electric vehicles in each driving cycle.

[0030] Furthermore, the environmental impact data includes the average external temperature, the average external wind speed and the average vibration amplitude of the electric vehicle during each driving cycle.

[0031] Furthermore, in step S106, the correction process of the prediction correction module is specifically as follows:

[0032] Obtain the average outside temperature i, the average outside wind speed and the average vibration amplitude of the vehicle body during each driving cycle of the electric vehicle;

[0033] Calculate the environmental impact coefficient of each driving cycle of electric vehicles;

[0034] The estimated cruising time of the electric vehicle and the additional energy consumption value of each driving cycle of the electric vehicle are obtained, and the corrected cruising time of the electric vehicle is calculated.

[0035] In a second aspect, a prediction system for the range of an electric vehicle is also provided, comprising:

[0036] An information acquisition module is used to obtain the body configuration information of the electric vehicle, and the obtained body configuration information of the electric vehicle is sent to the battery analysis module via the processor;

[0037] The battery analysis module is used to perform a preliminary analysis on the battery life of the electric vehicle, and obtain the estimated battery life of the electric vehicle through the analysis. The estimated battery life is sent to the prediction correction module through the processor;

[0038] The driving process of the electric vehicle is divided into several driving cycles;

[0039] A driving monitoring module is used to obtain driving status data of each driving cycle of the electric vehicle, and send the driving status data of each driving cycle of the electric vehicle to the driving analysis module via a processor;

[0040] A driving analysis module is used to analyze the driving behavior of the electric vehicle, and obtain the additional energy consumption value of each driving cycle of the electric vehicle through the analysis. The additional energy consumption value is sent to the prediction correction module through the processor;

[0041] The driving monitoring module is also used to collect environmental impact data of each driving cycle of the electric vehicle, and the environmental impact data is sent to the prediction and correction module via the processor;

[0042] A prediction correction module is used to comprehensively correct the cruising range of the electric vehicle to obtain a corrected cruising range of the electric vehicle, and the corrected cruising range is sent to the display via the processor;

[0043] The display is used to display the corrected cruising time of the electric vehicle.

[0044] In a third aspect, an electronic device is further provided, wherein the electronic device comprises:

[0045] A memory storing a computer program;

[0046] A processor is communicatively connected to the memory, and when the computer program is executed by the processor, a method for predicting the cruising range of an electric vehicle is implemented.

[0047] In a fourth aspect, a computer-readable storage medium is also provided, on which a computer program is stored, and when the program is executed by a processor, a method for predicting the cruising range of an electric vehicle is implemented.

[0048] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0049] The present invention firstly uses a battery analysis module to perform a preliminary analysis on the battery life of an electric vehicle, and the estimated battery life of the electric vehicle obtained by analysis is sent to a prediction and correction module. At the same time, the driving process of the electric vehicle is divided into several driving cycles. The driving monitoring module obtains the driving status data of each driving cycle of the electric vehicle and sends it to the driving analysis module. The driving analysis module is used to analyze the driving behavior of the electric vehicle, and the additional energy consumption value of each driving cycle of the electric vehicle is obtained by analysis and sent to the prediction and correction module. At the same time, the driving monitoring module also collects the environmental impact data of each driving cycle of the electric vehicle and sends it to the prediction and correction module. Finally, the battery life of the electric vehicle is comprehensively corrected by the prediction and correction module to obtain the corrected battery life of the electric vehicle. The present invention integrates multiple influencing factors to realize the intelligent prediction of the battery life of the electric vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0051] Figure 1 is a flow chart of the method of the present invention;

[0052] Figure 2 is the overall system block diagram of the present invention;

[0053] Figure 3 It is a schematic diagram of the principle of the driving monitoring module in the present invention;

[0054] Figure 4 It is a schematic diagram of the structure of the electronic device in the present invention. DETAILED DESCRIPTION

[0055] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] Embodiment 1:

[0057] See also Figure 1 The technical solution provided by the present invention is: a method for predicting the cruising range of an electric vehicle, and the specific steps of the method are as follows:

[0058] Step S101, the information acquisition module acquires the body configuration information of the electric vehicle, and sends the body configuration information of the electric vehicle to the processor, and the processor sends the body configuration information of the electric vehicle to the battery analysis module;

[0059] Step S102, the battery analysis module performs a preliminary analysis on the battery life of the electric vehicle, and sends the estimated battery life of the electric vehicle obtained through the preliminary analysis to the processor, and the processor sends the estimated battery life of the electric vehicle to the prediction correction module;

[0060] Step S103, dividing the driving process of the electric vehicle into several driving cycles, the driving monitoring module obtains the driving state data of each driving cycle of the electric vehicle and sends it to the processor, and the processor sends the driving state data of each driving cycle of the electric vehicle to the driving analysis module;

[0061] Step S104, the driving analysis module analyzes the driving behavior of the electric vehicle, and the additional energy consumption value of each driving cycle of the electric vehicle is obtained by the analysis and sent to the processor, and the processor sends the additional energy consumption value of each driving cycle of the electric vehicle to the prediction correction module;

[0062] Step S105, the driving monitoring module also collects environmental impact data of each driving cycle of the electric vehicle and sends it to the processor, and the processor sends the environmental impact data of each driving cycle of the electric vehicle to the prediction and correction module;

[0063] Step S106, the prediction correction module performs a comprehensive correction on the cruising range of the electric vehicle, and sends the corrected cruising range of the electric vehicle to the processor, and the processor sends the corrected cruising range of the electric vehicle to the display for display.

[0064] Embodiment 2:

[0065] See also Figure 2 and Figure 3 As shown, based on another concept of the same invention, a prediction system for the cruising range of an electric vehicle is proposed, the system includes an information collection module, a battery analysis module, a driving monitoring module, a driving analysis module, a prediction correction module, a display and a processor;

[0066] The information acquisition module is used to obtain the body configuration information of the electric vehicle, the information acquisition module sends the body configuration information of the electric vehicle to the processor, and the processor sends the body configuration information of the electric vehicle to the battery analysis module;

[0067] Among them, the body configuration information includes the production timestamp of the electric vehicle, the total vehicle weight, the driving mode, the battery rated capacity, the battery rated voltage, the battery rated current and the electric energy conversion efficiency; among them, the total vehicle weight is the body mass of the electric vehicle in the unloaded state, the driving modes of the electric vehicle include four-wheel drive, front-wheel drive and rear-wheel drive, and the electric energy conversion efficiency is the conversion efficiency of the engine in the electric vehicle to convert electrical energy into mechanical energy. In the actual working process, the user can take a picture of the VIN code (Vehicle Identification Number) of the electric vehicle or manually enter the VIN code to obtain the body configuration information of the electric vehicle through the VIN code;

[0068] The battery analysis module is used to perform a preliminary analysis on the battery life of the electric vehicle. The analysis process is as follows:

[0069] Obtain the production timestamp and current timestamp of the electric vehicle, subtract the production timestamp from the current timestamp to calculate the actual running time ST of the electric vehicle, and compare the actual running time ST of the electric vehicle with the running time interval;

[0070] If the actual operating time belongs to the first operating time interval, the battery attenuation coefficient of the electric vehicle is the first battery attenuation coefficient;

[0071] If the actual operating time belongs to the second operating time interval, the battery attenuation coefficient of the electric vehicle is the second battery attenuation coefficient;

[0072] If the actual operating time belongs to the third operating time interval, the battery attenuation coefficient of the electric vehicle is the third battery attenuation coefficient;

[0073] Among them, the values ​​of the operating time intervals are all greater than zero, the values ​​of the first operating time intervals are all less than the values ​​of the second operating time intervals, the values ​​of the second operating time intervals are all less than the values ​​of the third operating time intervals, the first battery attenuation coefficient is less than the second battery attenuation coefficient, the second battery attenuation coefficient is less than the third battery attenuation coefficient, and the battery attenuation coefficient reflects the attenuation rate of the battery capacity in the electric vehicle. The larger the value of the battery attenuation coefficient, the faster the attenuation rate of the battery capacity;

[0074] Obtain the total vehicle weight CW and driving mode of the electric vehicle, and calculate the electric energy conversion resistance value ZL of the electric vehicle according to the formula. The specific formula is as follows:

[0075] Among them, BW is the standard weight, q1 and q2 are the driving energy consumption coefficients of electric vehicles, the values ​​of q1 and q2 are both greater than zero, q1<q2;

[0076] Obtain the battery rated capacity EC, battery rated voltage EV, battery rated current EI and electric energy conversion efficiency ZH of the electric vehicle;

[0077] The expected driving time YXT of an electric vehicle is calculated according to the formula, which is as follows:

[0078] Wherein, EP is the rated output power of the electric vehicle, SC is the estimated battery capacity of the electric vehicle, SL is the estimated conversion efficiency of the electric vehicle, sj is the battery attenuation coefficient, and the value of sj includes s1, s2 and s3, which correspond to the first battery attenuation coefficient, the second battery attenuation coefficient and the third battery attenuation coefficient respectively;

[0079] The battery analysis module sends the estimated cruising time of the electric vehicle to the processor, and the processor sends the estimated cruising time of the electric vehicle to the prediction correction module;

[0080] In this embodiment, the driving process of the electric vehicle is divided into several driving cycles, and the driving cycle includes an acceleration phase, a deceleration phase and a constant speed phase. Figure 3 As shown, the driving monitoring module is used to obtain the driving status data of each driving cycle of the electric vehicle. The driving monitoring process is as follows:

[0081] If the electric vehicle is in a constant speed driving state, the constant speed cruising speed SVi and constant speed driving duration STi of the electric vehicle are obtained, where i is the driving cycle number and n is the upper limit value of the driving cycle number;

[0082] If the electric vehicle is in an accelerating state, the acceleration start speed AQi, acceleration end speed AZi and acceleration duration ATi of the electric vehicle are obtained. Similarly, if the electric vehicle is in a decelerating state, the deceleration start speed JQi, deceleration end speed JZi and deceleration duration JTi of the electric vehicle are obtained.

[0083] For example, Figure 3 As shown, the first driving cycle includes an acceleration phase, a constant speed phase, and a deceleration phase, and the second cycle includes only a constant speed phase;

[0084] The driving monitoring module sends the driving status data of each driving cycle of the electric vehicle to the processor, and the processor sends the driving status data of each driving cycle of the electric vehicle to the driving analysis module;

[0085] The driving analysis module is used to analyze the driving behavior of the electric vehicle, and the analysis process is as follows:

[0086] Obtain the cruise speed SVi, the constant speed driving time STi, the acceleration start speed AQi, the acceleration end speed AZi, the acceleration time ATi, the deceleration start speed JQi, the deceleration end speed JZi and the deceleration time JTi of each driving cycle of the electric vehicle;

[0087] The additional energy consumption value ANi of each driving cycle of electric vehicles is calculated according to the formula. The specific formula is as follows:

[0088] ANi=AAi×a1+AYi×a1+AJi×a3;

[0089] AAi=(AZi-AQi) / ATi;

[0090] AJi=(JQi-JZi) / JTi;

[0091]

[0092] Among them, a1, a2 and a3 are energy consumption coefficients with fixed values, and the values ​​of a1, a2 and a3 are all greater than zero, AAi is the average acceleration of each driving cycle of the electric vehicle, AJi is the average deceleration acceleration of each driving cycle of the electric vehicle (acceleration under deceleration state), and AYi is the mileage of each driving cycle of the electric vehicle;

[0093] The driving analysis module sends the additional energy consumption value of each driving cycle of the electric vehicle to the processor, and the processor sends the additional energy consumption value of each driving cycle of the electric vehicle to the prediction correction module;

[0094] The driving monitoring module is also used to collect environmental impact data of each driving cycle of the electric vehicle and send it to the processor. The processor sends the environmental impact data of each driving cycle of the electric vehicle to the prediction and correction module. The environmental impact data includes the average temperature outside the vehicle, the average wind speed outside the vehicle and the average amplitude of the vehicle body of the electric vehicle in each driving cycle. In the actual working process, the real-time temperature outside the vehicle and the real-time wind speed during the driving process are obtained by the temperature measuring element and the anemometer installed at the chassis position of the electric vehicle, and the real-time amplitude of the electric vehicle is obtained by obtaining the deformation degree of the shock absorber of the electric vehicle. The real-time temperature outside the vehicle, the real-time wind speed and the real-time amplitude of the electric vehicle in each driving cycle are added and averaged to calculate the average temperature outside the vehicle, the average wind speed outside the vehicle and the average amplitude of the vehicle body;

[0095] The prediction correction module is used to comprehensively correct the cruising range of the electric vehicle, and the correction process is as follows:

[0096] Obtain the average outside temperature PWDi, the average outside wind speed PFSi and the average vibration amplitude PZFi of the vehicle body during each driving cycle of the electric vehicle;

[0097] The environmental impact coefficient HJi of each driving cycle of electric vehicles is calculated according to the formula. The specific formula is as follows:

[0098] Among them, h1, h2 and h3 are weight coefficients with fixed values, the values ​​of h1, h2 and h3 are all greater than zero and h1+h2+h3=1; WCi is the average temperature difference, [t1, t2] is the standard battery temperature range of the electric vehicle, FCi is the average wind speed difference, and BFS is the standard wind speed outside the vehicle;

[0099] Obtain the estimated cruising time YXT of the electric vehicle and the additional energy consumption value ANi of each driving cycle of the electric vehicle, and calculate the corrected cruising time XXT of the electric vehicle according to the formula. The specific formula is as follows:

[0100] It should be specified that the corrected driving time of an electric vehicle is the driving time of the battery at rated voltage and rated current;

[0101] The prediction correction module sends the corrected cruising time of the electric vehicle to the processor, and the processor sends the corrected cruising time of the electric vehicle to the display for display;

[0102] In this application, if corresponding calculation formulas appear, the above calculation formulas are all dimensionless and take their numerical calculations. The weight coefficients, proportional coefficients and other coefficients in the formulas are set to a result value obtained by quantifying each parameter. The size of the weight coefficient and the proportional coefficient can be determined as long as it does not affect the proportional relationship between the parameter and the result value.

[0103] Embodiment 3:

[0104] Figure 4 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 4As shown, the electronic device may include: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus. The processor may call the logic instructions in the memory to execute a method for predicting the range of an electric vehicle, the method comprising: an information acquisition module acquires the body configuration information of the electric vehicle, and sends the body configuration information of the electric vehicle to the processor, the processor sends the body configuration information of the electric vehicle to the battery analysis module; the battery analysis module performs a preliminary analysis on the range of the battery in the electric vehicle, and the preliminary analysis obtains the estimated range of the electric vehicle and sends it to the processor, the processor sends the estimated range of the electric vehicle to the prediction correction module; the driving process of the electric vehicle is divided into several driving cycles, the driving monitoring module acquires the driving status data of each driving cycle of the electric vehicle and sends it to the processor, the processor sends The driving status data of each driving cycle of the electric vehicle is sent to the driving analysis module; the driving analysis module analyzes the driving behavior of the electric vehicle, and the additional energy consumption value of each driving cycle of the electric vehicle obtained by analysis is sent to the processor, and the processor sends the additional energy consumption value of each driving cycle of the electric vehicle to the prediction and correction module; the driving monitoring module also collects the environmental impact data of each driving cycle of the electric vehicle and sends it to the processor, and the processor sends the environmental impact data of each driving cycle of the electric vehicle to the prediction and correction module; the prediction and correction module comprehensively corrects the cruising range of the electric vehicle, and the corrected cruising range of the electric vehicle is sent to the processor, and the processor sends the corrected cruising range of the electric vehicle to the display for display.

[0105] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0106] On the other hand, the present application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute a method for predicting the cruising range of an electric vehicle provided by the above methods, the method including: an information acquisition module obtains body configuration information of the electric vehicle, and sends the body configuration information of the electric vehicle to a processor, and the processor sends the body configuration information of the electric vehicle to a battery analysis module; the battery analysis module performs a preliminary analysis on the cruising range of the battery in the electric vehicle, and the preliminary analysis obtains an estimated cruising range of the electric vehicle and sends it to the processor, and the processor sends the estimated cruising range of the electric vehicle to the prediction correction module; the driving process of the electric vehicle is divided into several driving cycles The driving monitoring module obtains the driving status data of each driving cycle of the electric vehicle and sends it to the processor, and the processor sends the driving status data of each driving cycle of the electric vehicle to the driving analysis module; the driving analysis module analyzes the driving behavior of the electric vehicle, and the additional energy consumption value of each driving cycle of the electric vehicle obtained by analysis is sent to the processor, and the processor sends the additional energy consumption value of each driving cycle of the electric vehicle to the prediction and correction module; the driving monitoring module also collects the environmental impact data of each driving cycle of the electric vehicle and sends it to the processor, and the processor sends the environmental impact data of each driving cycle of the electric vehicle to the prediction and correction module; the prediction and correction module comprehensively corrects the cruising range of the electric vehicle, and the corrected cruising range of the electric vehicle obtained by correction is sent to the processor, and the processor sends the corrected cruising range of the electric vehicle to the display for display.

[0107] On the other hand, the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the above-mentioned method for predicting the range of an electric vehicle, the method comprising: an information acquisition module acquiring body configuration information of the electric vehicle, and sending the body configuration information of the electric vehicle to a processor, the processor sending the body configuration information of the electric vehicle to a battery analysis module; the battery analysis module performs a preliminary analysis on the range of the battery in the electric vehicle, and the preliminary analysis obtains an estimated range of the electric vehicle and sends it to the processor, the processor sends the estimated range of the electric vehicle to a prediction correction module; the driving process of the electric vehicle is divided into a number of driving cycles, and the driving monitoring module obtains the information of each driving cycle of the electric vehicle The driving status data of the electric vehicle is collected and sent to the processor, and the processor sends the driving status data of each driving cycle of the electric vehicle to the driving analysis module; the driving analysis module analyzes the driving behavior of the electric vehicle, and the additional energy consumption value of each driving cycle of the electric vehicle obtained by analysis is sent to the processor, and the processor sends the additional energy consumption value of each driving cycle of the electric vehicle to the prediction and correction module; the driving monitoring module also collects the environmental impact data of each driving cycle of the electric vehicle and sends it to the processor, and the processor sends the environmental impact data of each driving cycle of the electric vehicle to the prediction and correction module; the prediction and correction module comprehensively corrects the cruising range of the electric vehicle, and the corrected cruising range of the electric vehicle obtained by correction is sent to the processor, and the processor sends the corrected cruising range of the electric vehicle to the display for display.

[0108] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0109] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for predicting the cruising range of an electric vehicle, characterized in that: The prediction method is as follows: Step S101, the information acquisition module acquires the body configuration information of the electric vehicle and sends it to the processor, and the processor sends the body configuration information of the electric vehicle to the battery analysis module; Step S102, the battery analysis module performs a preliminary analysis on the battery life of the electric vehicle, and the estimated battery life of the electric vehicle obtained by the analysis is sent to the prediction correction module via the processor; Step S103, dividing the driving process of the electric vehicle into a number of driving cycles, the driving monitoring module acquires the driving state data of each driving cycle of the electric vehicle and sends it to the driving analysis module via the processor; Step S104, the driving analysis module analyzes the driving behavior of the electric vehicle, and the additional energy consumption value of each driving cycle of the electric vehicle obtained by the analysis is sent to the prediction correction module through the processor; Step S105, the driving monitoring module also collects environmental impact data of each driving cycle of the electric vehicle, and the environmental impact data is sent to the prediction and correction module via the processor; Step S106, the prediction correction module performs comprehensive correction on the cruising range of the electric vehicle, and sends the corrected cruising range of the electric vehicle to the processor, and the processor sends the corrected cruising range of the electric vehicle to the display for display.

2. The method for predicting the cruising range of an electric vehicle according to claim 1, characterized in that: The vehicle configuration information includes the production timestamp of the electric vehicle, the total vehicle weight, the driving mode, the battery rated capacity, the battery rated voltage, the battery rated current and the power conversion efficiency; The driving cycle includes an acceleration phase, a deceleration phase and a constant speed phase.

3. The method for predicting the cruising range of an electric vehicle according to claim 2, characterized in that: In step S102, the analysis process of the battery analysis module is as follows: Obtaining the production timestamp and current timestamp of the electric vehicle, subtracting the production timestamp from the current timestamp to calculate the actual running time of the electric vehicle, and comparing the actual running time of the electric vehicle with the running time interval; According to the operating time interval to which the actual operating time belongs, the battery attenuation coefficient of the electric vehicle is obtained; wherein, the larger the value of the battery attenuation coefficient is, the faster the attenuation rate of the battery capacity is; Obtain the total vehicle weight and driving mode of the electric vehicle, and calculate the electric energy conversion resistance value of the electric vehicle; Then the battery rated capacity, battery rated voltage, battery rated current and power conversion efficiency of the electric vehicle are obtained, and the expected endurance of the electric vehicle is calculated.

4. The method for predicting the cruising range of an electric vehicle according to claim 3, characterized in that: In step S103, the driving monitoring process of the driving monitoring module is as follows: If the electric vehicle is in a constant speed driving state, the constant speed cruising speed and constant speed driving time of the electric vehicle are obtained; If the electric vehicle is in an accelerating state, the acceleration start speed, acceleration end speed and acceleration duration of the electric vehicle are obtained; If the electric vehicle is in a decelerating driving state, the deceleration start speed, deceleration end speed and deceleration duration of the electric vehicle are obtained.

5. The method for predicting the cruising range of an electric vehicle according to claim 4, characterized in that: In step S104, the analysis process of the driving analysis module is as follows: Obtain the cruise speed, cruise time, acceleration start speed, acceleration end speed, acceleration time, deceleration start speed, deceleration end speed and deceleration time of each driving cycle of the electric vehicle; Calculate the additional energy consumption of electric vehicles in each driving cycle.

6. The method for predicting the cruising range of an electric vehicle according to claim 1, characterized in that: Environmental impact data include the average outside temperature, average outside wind speed and average body vibration amplitude of electric vehicles in each driving cycle.

7. The method for predicting the cruising range of an electric vehicle according to claim 6, characterized in that: In step S106, the correction process of the prediction correction module is as follows: Obtain the average outside temperature i, the average outside wind speed and the average vibration amplitude of the vehicle body during each driving cycle of the electric vehicle; Calculate the environmental impact coefficient of each driving cycle of electric vehicles; The estimated cruising time of the electric vehicle and the additional energy consumption value of each driving cycle of the electric vehicle are obtained, and the corrected cruising time of the electric vehicle is calculated.

8. A system for predicting the range of an electric vehicle, characterized in that: In combination with a method for predicting the cruising range of an electric vehicle as described in any one of claims 1 to 7, the method comprises: An information acquisition module is used to obtain the body configuration information of the electric vehicle, and the obtained body configuration information of the electric vehicle is sent to the battery analysis module via the processor; The battery analysis module is used to perform a preliminary analysis on the battery life of the electric vehicle, and obtain the estimated battery life of the electric vehicle through the analysis. The estimated battery life is sent to the prediction correction module through the processor; The driving process of the electric vehicle is divided into several driving cycles; A driving monitoring module is used to obtain driving status data of each driving cycle of the electric vehicle, and send the driving status data of each driving cycle of the electric vehicle to the driving analysis module via a processor; A driving analysis module is used to analyze the driving behavior of the electric vehicle, and obtain the additional energy consumption value of each driving cycle of the electric vehicle through the analysis. The additional energy consumption value is sent to the prediction correction module through the processor; The driving monitoring module is also used to collect environmental impact data of each driving cycle of the electric vehicle, and the environmental impact data is sent to the prediction and correction module via the processor; A prediction correction module is used to comprehensively correct the cruising range of the electric vehicle to obtain a corrected cruising range of the electric vehicle, and the corrected cruising range is sent to the display via the processor; The display is used to display the corrected cruising time of the electric vehicle.

9. An electronic device, characterized in that: The electronic device comprises: A memory storing a computer program; A processor is communicatively connected to the memory, and when the computer program is executed by the processor, the method described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method described in any one of claims 1 to 7 is implemented.

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