A dynamic estimation method for electric vehicle mileage

Abstract

The present invention relates to a dynamic prediction method for the cruising range of an electric vehicle. The method fully considers that the current electric vehicle is in its starting stage, the vehicle's power consumption is complex, and the initial large fluctuations in power consumption and driving data will cause serious deviations in the remaining mileage estimation. By using the idea of ​​big data clustering, a large amount of historical driving data, and combining the vehicle load, the average driving distance per unit energy consumption under the current load state is calculated, and the remaining cruising range is obtained according to the current remaining power.

Description

Technical Field

[0001] The invention relates to the field of electric vehicles, and in particular to a method for dynamically predicting the mileage of an electric vehicle. Background Art

[0002] The range of electric vehicles is determined by the remaining energy of the battery pack and the energy consumption of the future route. At present, the research on the remaining range estimation of electric vehicles is divided into two categories. The first category is to estimate the remaining range based on the average energy consumption and the remaining energy of the battery obtained from the driving data of the previous period. The second method is to use GPS to predict the vehicle's driving energy consumption in the future and estimate the mileage. There are many factors affecting vehicle mileage, and it is very difficult to estimate. The existing solutions cannot provide very accurate estimation results. Moreover, the actual range of pure electric vehicles of different masses increases with the increase of the total mass of the vehicle. Data show that the difference between models with a total mass of 1 ton and below is up to 27% compared with models with a total mass of 2 to 3 tons. In addition, the seasonal characteristics of the difference rate between the actual range and the working mileage are obvious. In winter, due to the low external temperature, the actual range is lower than usual, and the difference ratio increases significantly. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method for dynamically predicting the cruising range of an electric vehicle, which takes into account both the influence of season on the cruising range and the influence of different qualities and different road conditions on the estimation accuracy, so that the error accuracy of the estimation meets the expected effect.

[0004] The present invention is achieved in that:

[0005] A method for dynamically predicting the cruising range of an electric vehicle, characterized by comprising the following steps:

[0006] Step 1: The system establishes a vehicle driving force model, estimates the vehicle's current weight using the model based on the vehicle's current driving data, and compares it with the vehicle's empty, lightly loaded, and fully loaded weight ranges stored in the system to determine whether the vehicle is currently empty, lightly loaded, or fully loaded;

[0007] Step 2: The system sends the vehicle's load status to the vehicle monitoring platform and sends a data request instruction. After receiving the data request instruction, the vehicle monitoring platform extracts the corresponding data from the database according to the vehicle's current load status and the preset time period, obtains the historical unit energy consumption mileage m1 through calculation, and sends the historical unit energy consumption mileage m1 back to the system;

[0008] Step 3: The remaining cruising range is predicted every time the battery state of charge (SOC) value decreases by one percentage point. Specifically, at the initial moment of vehicle start, the system obtains the battery state of charge (SOC) value from the battery management system (BMS). In the stage where the SOC value decreases by 5 percentage points before it decreases, that is, when the SOC decreases from the initial M to M-5, the remaining energy under the corresponding SOC is calculated based on the collected real-time voltage value, SOC value, and battery capacity. Then, it is multiplied by the historical unit energy consumption mileage m1 obtained in step 2 to calculate the remaining cruising range as the estimated value of the remaining cruising range under the corresponding SOC.

[0009] Step 4. After the SOC value drops by 5 percentage points, that is, after it drops to M-6, the estimated value of the remaining cruising range is calculated according to the five nearest SOC iteration method. Specifically, when the SOC drops by 6 percentage points, the latest unit energy consumption mileage m2 is obtained according to the driving distance and energy consumed in the first 5 percentage points of the SOC drop stage. According to the voltage U corresponding to the current SOC and the battery capacity C, the initial estimated mileage (M-6)*U*C*m2 under the current SOC is calculated, and then the initial estimated mileage is compared and corrected by the preset threshold. The corrected value finally obtained is used as the estimated value of the remaining cruising range under the current SOC. By analogy, for each percentage point drop in the SOC, a new SOC driving data is added and the first SOC driving data is discarded. That is, the latest SOC driving data is always maintained, and the new unit energy consumption mileage is obtained based on the latest 5 SOC driving data. The estimated value of the remaining cruising range under the current SOC is obtained in the same way.

[0010] Preferably, the vehicle driving force model established in step 1 is the following vehicle motion equation:

[0011]

[0012] Where F is the driving force of the vehicle, m is the mass of the vehicle, g is the acceleration of gravity, which is taken as 9.8, f is the rolling resistance coefficient, θ is the ramp angle, a is the driving acceleration, δ is the rotational inertia coefficient of the transmission system, and C D is the drag coefficient, A is the frontal area, and v is the vehicle speed.

[0013] Preferably, step 1 also includes obtaining initial vehicle driving data, the initial vehicle driving data including the vehicle real-time speed v, the driving motor speed n and the torque information T; the estimation of the current weight of the vehicle is to calculate the vehicle weight m by reverse calculation according to formula (1), specifically, by formula

[0014]

[0015] To obtain.

[0016] Preferably, the driving force F is given by the formula

[0017]

[0018] Calculate, P is the motor power, η is the total efficiency of the actuator, V is the current vehicle speed, η is a constant, the value is η = 95%; where P is given by the formula

[0019]

[0020] It is found that n is the motor speed, T is the motor torque, and the other signals are obtained through the CAN bus network;

[0021] The vehicle weight is finally calculated by the formula

[0022]

[0023] To obtain.

[0024] Preferably, in step one, the weight ranges of the vehicle when empty, lightly loaded and fully loaded stored in the system are calibrated after multiple loading experiments, wherein the weight intervals corresponding to empty, lightly loaded and fully loaded are calculated using formula (1) based on the driving data during the experiment, and the intervals are stored in the system.

[0025] Preferably, in the step 2, the corresponding data extracted by the vehicle monitoring platform from the database is pre-formed and stored in the following manner: the vehicle obtains driving data once every 10 km, and merges it with the vehicle VIN code into a data record and sends it to the vehicle monitoring platform, and the vehicle monitoring platform saves the data record in chronological order in a historical energy consumption data table of the database, wherein the driving data includes the energy consumption, mileage, vehicle load status and unit energy consumption driving distance within the corresponding driving interval; the data in the historical energy consumption data table are the original data for calculating the historical unit energy consumption mileage; wherein the energy consumption within the corresponding driving interval is specifically the difference between the remaining battery power at the starting point and the end point of the corresponding driving interval, wherein the remaining battery power is calculated by three parameters: battery SOC, battery voltage U and battery capacity.

[0026] Preferably, in step 4, comparing and correcting the initial estimated mileage by a preset threshold value includes:

[0027] Set the upper and lower thresholds, where

[0028] Upper threshold = remaining mileage after the last SOC correction,

[0029] Lower limit threshold = remaining range after the previous SOC correction - 2 * average unit mileage corresponding to the last 5 SOCs;

[0030] like

[0031] Lower threshold < initial remaining mileage estimated by current SOC < upper threshold

[0032] Then, the remaining mileage after correction of the current SOC = the initial estimated remaining mileage of the current SOC;

[0033] like

[0034] The initial estimated remaining mileage of the current SOC>=upper threshold

[0035] Then, the remaining mileage after the current SOC correction = the upper limit threshold - ((the initial estimated remaining mileage of the current SOC / the upper limit threshold) - 1) * the average unit mileage corresponding to the last five SOCs;

[0036] like

[0037] The initial estimated remaining mileage of the current SOC <= lower limit threshold

[0038] Then, the remaining mileage after the current SOC correction = the lower limit threshold + the average unit mileage corresponding to the last 5 SOCs.

[0039] The beneficial effects of the present invention are as follows: the method for dynamically predicting the range of the present invention first fully considers that the current electric vehicle is in its starting stage, the on-board power consumption is complex, and the initial large fluctuations in power consumption and driving data will cause serious deviations in the remaining mileage estimation. By using the idea of ​​big data clustering, a large amount of historical driving data is used, and combined with the vehicle load conditions, the average driving distance of the historical unit energy consumption under the current load state is calculated, and the remaining driving range is obtained according to the current remaining power. This method can more accurately estimate the driving range of the vehicle in the initial stage of driving; at the same time, in the stable driving stage of the vehicle, the present invention uses the driving data of the previous moment closest to the current moment as a reference to obtain the driving range per unit energy consumption. And it can dynamically adjust the obtained unit energy consumption driving range parameter value according to the average driving range of the real-time unit energy consumption, estimate the driving range according to the remaining SOC energy, and more realistically reflect the future driving range under the current working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a flow chart of a method for estimating the cruising range of an electric vehicle according to an embodiment of the present invention. DETAILED DESCRIPTION

[0041] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments, but they do not constitute any limitation to the present invention.

[0042] In a preferred embodiment, if Figure 1 As shown, a dynamic prediction method for the cruising range of an electric vehicle is proposed, comprising the following steps:

[0043] Step 1: The cruising range controller establishes a vehicle driving force model, estimates the vehicle's current weight using the model based on the vehicle's current driving data, and compares it with the vehicle's empty, lightly loaded, and fully loaded weight ranges stored in the database to determine whether the vehicle is currently empty, lightly loaded, or fully loaded;

[0044] Step 2: The cruising range controller sends the vehicle's load status to the vehicle monitoring platform and sends a data request instruction. After receiving the data request instruction, the vehicle monitoring platform extracts the corresponding data from the database according to the vehicle's current load status and the preset time period, obtains the historical unit energy consumption mileage m1 through calculation, and sends the historical unit energy consumption mileage m1 back to the cruising range controller;

[0045] Step 3: The remaining cruising range is predicted every time the battery state of charge (SOC) value decreases by one percentage point. Specifically, at the initial moment of vehicle start, the system obtains the battery state of charge (SOC) value from the battery management system (BMS). In the stage where the SOC value decreases by 5 percentage points before it decreases, that is, when the SOC decreases from the initial M to M-5, the remaining energy under the corresponding SOC is calculated based on the collected real-time voltage value, SOC value, and battery capacity. Then, it is multiplied by the historical unit energy consumption mileage m1 obtained in step 2 to calculate the remaining cruising range as the estimated value of the remaining cruising range under the corresponding SOC.

[0046] Step 4. After the SOC value drops by 5 percentage points, that is, after it drops to M-6, the estimated value of the remaining cruising range is calculated according to the five nearest SOC iteration method. Specifically, when the SOC drops by 6 percentage points, the latest unit energy consumption mileage m2 is obtained according to the driving distance and energy consumed in the first 5 percentage points of the SOC drop stage. According to the voltage U corresponding to the current SOC and the battery capacity C, the initial estimated mileage (M-6)*U*C*m2 under the current SOC is calculated, and then the initial estimated mileage is compared and corrected by the preset threshold. The corrected value finally obtained is used as the estimated value of the remaining cruising range under the current SOC. By analogy, for each percentage point drop in the SOC, a new SOC driving data is added and the first SOC driving data is discarded. That is, the latest SOC driving data is always maintained, and the new unit energy consumption mileage is obtained based on the latest 5 SOC driving data. The estimated value of the remaining cruising range under the current SOC is obtained in the same way.

[0047] The vehicle driving force model established in step 1 is the following vehicle motion equation:

[0048]

[0049] Where F is the driving force of the vehicle, m is the mass of the vehicle, g is the acceleration of gravity, which is taken as 9.8, f is the rolling resistance coefficient, θ is the ramp angle, a is the driving acceleration, δ is the rotational inertia coefficient of the transmission system, and C D is the drag coefficient, A is the frontal area, and v is the vehicle speed.

[0050] Among them, step 1 also includes obtaining the initial driving data of the vehicle, which includes the real-time vehicle speed v, the driving motor speed n and the torque information T; the estimation of the current weight of the vehicle is to calculate the vehicle weight m by reverse calculation according to formula (1), which is specifically calculated by formula

[0051]

[0052] To obtain.

[0053] Among them, the driving force F can be expressed by the formula

[0054]

[0055] Calculate, P is the motor power, η is the total efficiency of the actuator, V is the current vehicle speed, η is a constant, the value is η = 95%; where P can be obtained by the formula

[0056]

[0057] It is found that n is the motor speed, T is the motor torque, and the other signals are obtained through the CAN bus network;

[0058] The vehicle weight is finally calculated by the formula

[0059]

[0060] To obtain.

[0061] For example, consider that the vehicle is running on a good road in the city, and the rolling resistance coefficient f is 0.01. The data required for estimating the load only need data from the starting acceleration phase for 20 seconds. The speed of the vehicle on the urban main road is not very fast, so the influence of air resistance is not considered for the time being, and the vehicle load is only estimated. The slope angle θ is not considered for the time being, and it is regarded as a horizontal road, that is, θ = 0. δ is the correction factor introduced by the rotational inertia of rotating parts such as wheels, gears or axles, which is regarded as a constant. η is the total efficiency of the actuator, and the value η is generally 95%. The vehicle weight formula is simplified to:

[0062]

[0063] Among them, g, f, δ, η are known quantities, v is the current vehicle speed, n is the motor speed, T is the motor torque and other information, which can be obtained from the vehicle CAN bus network. The data transmission frequency is 50ms. The vehicle acceleration a can be obtained from the speed difference within the time interval, that is, a = vt -v t-1 , the vehicle speed V at the previous moment T-1 and the current vehicle speed V T , can be obtained, so you only need to collect 20 seconds of data when the vehicle starts, you can get hundreds of corresponding data, after filtering outliers and taking the average, you can estimate the weight of the vehicle.

[0064] After estimating the vehicle weight, it is necessary to determine whether the vehicle is empty, lightly loaded or fully loaded. The empty / lightly / fully loaded ranges stored in the system are pre-set. The same type of test vehicle is used to undergo multiple empty / lightly / fully loaded road tests, and the weight value is estimated using the above formula (1). Abnormal values ​​are eliminated and the empty / lightly / fully loaded weight range is calculated. This data range is used as the basis for determining the current load state of the vehicle.

[0065] First of all, electric vehicles need to be connected to the corresponding vehicle monitoring platform in accordance with national standards. The vehicle monitoring platform will retain the vehicle's historical data. For example, GB / T32960.3-2016 requires that vehicle speed, motor speed, motor torque, SOC, battery voltage and other information need to be uploaded to the vehicle monitoring platform.

[0066] In step 2, when the vehicle is started, the system's mileage estimation device is also awakened and sends the status information of the device to the CAN bus network and uploads it to the vehicle monitoring platform. When the vehicle monitoring platform detects that the device is in normal status and it is the first time the device reports information that day, the platform will extract the corresponding data of the vehicle within 30 days from the historical energy consumption data table for analysis.

[0067] The platform searches the historical energy consumption data table for the vehicle's energy consumption data under the current load state according to the vehicle's current load state uploaded by the system of the present invention, and calculates the average of the selected unit energy consumption mileage. The obtained value can be used as the historical experience reference value for mileage estimation in the initial stage of the vehicle. The historical energy consumption data table in the database of the monitoring platform is shown in the following table:

[0068] VIN time Load status Mileage Energy consumption Distance travelled per unit energy consumption SOC consumption

[0069] The invented device collects vehicle driving data and battery data in real time. When it detects that the vehicle has traveled more than 10km, it sends the platform relevant data such as internal consumption mileage within the driving range and sends it to the monitoring platform. The data includes vehicle VIN code, load status, mileage, energy consumption, driving distance per unit energy consumption, and SOC consumption. The mileage is continuous mileage. When the vehicle is turned off, the program resets the interval mileage and re-accumulates it. Every time the interval mileage is accumulated again, the system also calculates the remaining power under the current SOC. After an interval ends, the system calculates the remaining power at the current SOC. The difference between the remaining power at the beginning and the end of the interval is the actual power consumption. The system also calculates the driving distance per unit energy consumption based on the driving distance within the interval and the calculated actual power consumption. The system merges the calculation results within these interval segments into one piece of data and sends it to the monitoring platform. After the monitoring platform parses the data, it is saved in the historical energy consumption data table in the above format.

[0070] For example: at the beginning of vehicle driving, the system obtains the initial SOC value and voltage based on the battery information collected by the BMS, and calculates the initial estimated mileage based on the energy consumption mileage experience value preset in the system - the comprehensive unit energy consumption mileage, and transmits it to the instrument for display. Then the system collects the real-time driving data of the vehicle, calculates the vehicle load according to Formula 5, and determines the load state according to the vehicle load. After calculation, it is determined that the vehicle is currently driving under full load. Then the system sends a historical data request to the platform. When the platform receives the data request instruction, it extracts the data of the past 30 days with a heavy load state from the historical data table. After screening, the vehicle ran a total of 34 data within 30 days before the current moment, including 9 trips of no-load energy consumption, 10 trips of light-load data, and 15 trips of full-load data. According to the vehicle load state, only 15 full-load data are extracted, and the unit energy consumption driving distance in these 15 full-load data is arithmetic averaged, which is the unit energy consumption mileage mean m1 under full load. The platform then transmits the calculated value m1 back to the system.

[0071] In step 3, the device records the real-time battery SOC value of the vehicle and sets a threshold value based on the acquired SOC initial value. The device collects SOC information in real time and compares it with the set threshold value. When the real-time SOC value is ≥ the threshold value, the device uses the historical experience data m1 under the current load obtained in step 2 and the current remaining power to calculate the remaining driving range. For example, when the vehicle is started, the system collects an initial battery SOC value of 75%, then the threshold value is: 75%-5%=70%, when the battery SOC is 75%, 74%, 73%, 72%, 71%, 70%, calculate the remaining power corresponding to each SOC, according to the known historical experience data m1, the remaining mileage when the battery SOC is 75%-70%, the battery voltage is U1, U2, U3, U4, U5, U6 when the battery SOC is 75%, 74%, 73%, 72%, 71%, 70% respectively, the rated capacity of the battery is C, then the corresponding mileage of each SOC is 75%*U1*C*m1, 74%*U2*C*m1, 73%*U3*C*m1, 72%*U4*C*m1, 71%*U5*C*m1,

[0072] 70%*U6*C*m1.

[0073] In step 4, the initial estimated mileage is compared and corrected by a preset threshold value, including:

[0074] Set the upper and lower thresholds, where

[0075] Upper threshold = remaining mileage after the last SOC correction,

[0076] Lower limit threshold = remaining range after the previous SOC correction - 2 * average unit mileage corresponding to the last 5 SOCs;

[0077] like

[0078] Lower threshold < initial remaining mileage estimated by current SOC < upper threshold

[0079] Then, the remaining mileage after correction of the current SOC = the initial estimated remaining mileage of the current SOC;

[0080] like

[0081] The initial estimated remaining mileage of the current SOC>=upper threshold

[0082] Then, the remaining mileage after the current SOC correction = the upper limit threshold - ((the initial estimated remaining mileage of the current SOC / the upper limit threshold) - 1) * the average unit mileage corresponding to the last five SOCs;

[0083] like

[0084] The initial estimated remaining mileage of the current SOC <= lower limit threshold

[0085] Then, the remaining mileage after the current SOC correction = the lower limit threshold + the average unit mileage corresponding to the last 5 SOCs.

[0086] The principle of setting the upper and lower thresholds is that the mileage estimated by the current SOC cannot be greater than the corrected mileage estimated by the previous SOC, because energy is consumed continuously, and the previous SOC is greater than the next SOC, and the corresponding power is also greater than the latter. In theory, the mileage estimated by the current SOC cannot be greater than the mileage estimated at the previous SOC moment. And it cannot be less than the mileage estimated by the next SOC. According to this basic principle, the unit energy consumption kilometer value corresponding to the continuous consumption of 5 SOCs is calculated by subtracting 2 times the mileage value after the previous SOC is corrected. The meaning is that the estimated mileage corresponding to the next SOC value of the current SOC value is based on the premise that the preset corrected cruising range of the previous SOC is established. That is, the current SOC estimated mileage value cannot be less than the next SOC estimated mileage value. By limiting the range in this way, the upper and lower limits of the current SOC estimated mileage can be dynamically obtained.

[0087] By analogy, during the driving process of the vehicle, the SOC estimated mileage at each moment uses the driving distance and energy consumption data corresponding to the last five consecutive SOC consumptions during this driving process to calculate the mileage under unit energy consumption, and the initial estimated driving distance is obtained based on the remaining energy under the remaining SOC. Then, based on the set threshold, the initial estimated driving distance is judged and corrected. The final correction value is the more realistic estimated remaining driving range.

[0088] For example, when the real-time SOC is detected to be 69%, which is less than the threshold value of 70 set above, the estimation of the driving range can no longer use historical experience data but use the driving data and energy consumption that are closer to that moment as a reference. In this example, the driving distance and the power consumption data consumed in this interval when the SOC drops from 74% to 70% during the trip need to be analyzed. After calculating that the driving range corresponding to a 5% drop in SOC is 13.5km, the energy consumption (U1*75%*C-U5*70%*C) is calculated based on the voltage U1 at 74% and the voltage U2 at 70%, the battery rated capacity, and the battery SOC value. The average driving distance per unit energy consumption is 13.5 / (U1*75%*C-U5*70%*C). When the SOC is 69%, the corresponding remaining power is 69%*U′*C. The corresponding estimated initial driving range of the remaining power is 69%*U′*C*13.5 / (U1*75%*C-U5*70%*C).

[0089] After the initial cruising range is determined, the value is judged and corrected. First, the upper and lower thresholds are determined. The upper limit is the estimated correction value when the SOC is 70%. Because when the SOC is 70, the estimated mileage calls the historical experience value m1, so the estimated initial cruising range is the correction value 70%*U6*C*m1. The lower limit is m1 as the average energy consumption mileage, and the estimated value when the SOC is 70 is the true value. The estimated cruising range when the SOC is 68 is 70%*U6*C*m1-2*m1.

[0090] After the upper and lower thresholds are determined, the estimated initial cruising range value must be judged using the initial cruising range value 69%*U′*C*13.5 / (U1*75%*C-U5*70%*C) and the upper and lower thresholds. If the value is within the upper and lower thresholds, the value is confident, that is, the corresponding cruising range when the current SOC is 69%. If the value exceeds the set upper and lower thresholds, it is necessary to correct the estimated value according to the correction equation, and the final corrected value is the final estimated true value.

[0091] As the SOC continues to decrease, the method for estimating the cruising range has been using the energy consumption and driving distance of the five nearest SOCs as a reference. The latest data is constantly updated, and earlier data is constantly discarded. For example, when the SOC drops to 59, the mileage and energy consumption data of the SOC at 64 and SOC at 60 are used to obtain the latest unit energy consumption cruising range. Similarly, this method can be used to obtain the remaining cruising range when the SOC drops to 1.

[0092] At the beginning of the vehicle's driving in step 3, the system obtains the initial value M of the battery state of charge (SOC) and the estimated mileage corresponding to the SOC dropping to M-5, which are provided by the historical experience data calculated in step 2. During the period from vehicle startup to stable operation, the power-consuming equipment on the vehicle is complex, and the short-term sharp increase in power consumption and driving data are insufficient to accurately estimate the remaining mileage. The historical experience data calculated from the platform is called to more realistically reflect the mileage. This system is based on the current load of the vehicle, and selectively selects the historical unit energy consumption mileage corresponding to the empty / light load / full load conditions in the historical data according to time. The method of estimating mileage under each SOC at the initial moment not only considers the impact of different loads on the mileage, but also extracts the corresponding experience values ​​for reference based on the current load state dimension, and considers the impact of seasonal factors on the mileage, and restricts the extracted experience data in the time dimension. This method can further improve the accuracy of the mileage prediction at this stage.

[0093] In the step 3, the vehicle driving range is estimated by using historical data in the initial stage of vehicle driving, and only the driving range corresponding to the SOC of the initial SOC value M to (M-5) can be estimated. When the SOC drops by more than 6 SOC values ​​from the initial value M, the driving range cannot be estimated by using historical data.

[0094] In step 4, when the SOC drops to (M-6), the cruising range estimation method needs to use the SOC driving distance and energy consumption of 5% between the previous SOC value (M-5) and (M-1) as the basis to obtain the driving distance m2 corresponding to the average energy consumption in each SOC in the previous interval. By calculating the product of the unit energy consumption driving distance m2 and the remaining power at the current SOC, the estimated initial remaining mileage at the current SOC can be obtained. Estimated initial remaining mileage Further threshold judgment correction is required, and the final correction value is the final remaining mileage.

[0095] Finally, it should be noted that the above implementation example only illustrates a technical solution of the present case. Although this article describes the solution in detail through drawings, etc., ordinary technicians in the relevant field should understand that by modifying some specific implementation methods of the present case or replacing some of its technical features with equivalents without departing from the design concept of the present technical solution, the resulting similar solutions still fall within the scope of protection requested in this case.

Claims

1. A method for dynamically predicting the cruising range of an electric vehicle, characterized in that: The following steps are involved: Step 1: The system establishes a vehicle driving force model, estimates the vehicle's current weight using the model based on the vehicle's current driving data, and compares it with the vehicle's empty, lightly loaded, and fully loaded weight ranges stored in the system to determine whether the vehicle is currently empty, lightly loaded, or fully loaded; Step 2: The system sends the vehicle's load status to the vehicle monitoring platform and sends a data request instruction. After receiving the data request instruction, the vehicle monitoring platform extracts the corresponding data from the database according to the vehicle's current load status and the preset time period, obtains the historical unit energy consumption mileage m1 through calculation, and sends the historical unit energy consumption mileage m1 back to the system; Step 3: The remaining cruising range is predicted every time the battery state of charge (SOC) value decreases by one percentage point. Specifically, at the initial moment of vehicle start, the system obtains the battery state of charge (SOC) value from the battery management system (BMS). In the stage before the SOC value decreases by 5 percentage points, that is, the SOC decreases from the initial M to M-5, the remaining energy under the corresponding SOC is calculated based on the collected real-time voltage value, SOC value and battery capacity, and then multiplied by the historical unit energy consumption mileage m1 obtained in step 2 to calculate the remaining cruising range as the estimated value of the remaining cruising range under the corresponding SOC. Remaining cruising range = real-time voltage value * SOC value * battery capacity * m1; Step 4. After the SOC value drops by 5 percentage points, that is, after it drops to M-6, the estimated value of the remaining cruising range is calculated according to the nearest five SOC iteration method, specifically: when the SOC drops by 6 percentage points, the latest unit energy consumption mileage m2 is obtained according to the driving distance and energy consumed in the first 5 percentage points of the SOC drop stage, and the initial estimated mileage (M-6)*U*C*m2 under the current SOC is calculated according to the voltage U corresponding to the current SOC and the battery capacity C. The initial estimated mileage is then compared and corrected by the preset threshold, and the corrected value finally obtained is used as the estimated value of the remaining cruising range under the current SOC. By analogy, every time the SOC drops by one percentage point, a new SOC driving data is added and the first SOC driving data is discarded, that is, the latest SOC driving data is always maintained, and the new unit energy consumption mileage is obtained based on the latest 5 SOC driving data, and the estimated value of the remaining cruising range under the current SOC is obtained in the same way; The vehicle driving force model established in step 1 is the following vehicle motion equation: Where F is the driving force of the vehicle, m is the mass of the vehicle, g is the acceleration of gravity, which is taken as 9.8, f is the rolling resistance coefficient, θ is the ramp angle, a is the driving acceleration, δ is the rotational inertia coefficient of the transmission system, and C D is the drag coefficient, A is the frontal area, and v is the vehicle speed; Step 1 also includes obtaining the initial driving data of the vehicle, which includes the real-time vehicle speed v, the driving motor speed n and the torque information T; estimating the current weight of the vehicle is to calculate the vehicle weight m by reverse calculation according to formula (1), which is specifically calculated by formula to obtain; The driving force F is given by the formula It is found that P is the motor power, η is the total efficiency of the actuator, V is the current vehicle speed, η is a constant, and the value η=95%; Where P is given by the formula It is found that n is the motor speed, T is the motor torque, and the other signals are obtained through the CAN bus network; The vehicle weight is finally calculated by the formula To obtain.

2. The method for dynamically predicting the cruising range of an electric vehicle according to claim 1, characterized in that: In step 1, the weight ranges of the vehicle when empty, lightly loaded and fully loaded stored in the system are calibrated after multiple loading experiments, wherein the weight ranges corresponding to the empty, lightly loaded and fully loaded weights are calculated using formula (1) based on the driving data during the experiment, and the weight ranges are stored in the system.

3. The method for dynamically predicting the cruising range of an electric vehicle according to claim 1, characterized in that: In the step 2, the corresponding data extracted by the vehicle monitoring platform from the database is pre-formed and stored in the following manner: the vehicle obtains driving data once every 10 km, and merges it with the vehicle VIN code into a data record and sends it to the vehicle monitoring platform. The vehicle monitoring platform saves the data record in a historical energy consumption data table in the database in chronological order, wherein the driving data includes the energy consumption, mileage, vehicle load status and unit energy consumption driving distance in the corresponding driving range; the data in the historical energy consumption data table is the original data for calculating the historical unit energy consumption mileage; wherein the energy consumption in the corresponding driving range is specifically the difference between the remaining battery power at the starting point and the end point of the corresponding driving range, wherein the remaining battery power is calculated by three parameters: battery SOC, battery voltage U and battery capacity.

4. The method for dynamically predicting the cruising range of an electric vehicle according to claim 1, characterized in that: In step 4, the initial estimated mileage is compared and corrected by a preset threshold value, including: Set the upper and lower thresholds, where Upper threshold = remaining mileage after the last SOC correction, Lower limit threshold = remaining range after the previous SOC correction - 2 * average unit mileage corresponding to the last 5 SOCs; like If the lower threshold value < the initial estimated remaining mileage of the current SOC < the upper threshold value, the remaining mileage after the current SOC correction = the initial estimated remaining mileage of the current SOC; like The initial estimated remaining mileage of the current SOC>=upper threshold Then, the remaining mileage after the current SOC correction = the upper limit threshold - ((the initial estimated remaining mileage of the current SOC / the upper limit threshold) - 1) * the average unit mileage corresponding to the last five SOCs; like If the initial estimated remaining mileage of the current SOC is less than or equal to the lower limit threshold, the remaining mileage after correction of the current SOC = the lower limit threshold + the average unit mileage corresponding to the last five SOCs.

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