Method for calculating remaining driving range of new energy vehicle

Abstract

The invention discloses a new energy vehicle remaining driving mileage calculation method which comprises the following steps: updating the driving mileage and the driving time of a whole vehicle once every L2 mileage or every specific T1 time, and initializing and reading the remaining driving mileage stored in an EEPROM (Electrically Erasable Programmable Read-Only Memory); calculating current driving cycle consumed electric quantity Ccyc, battery remaining electric quantity CRem and initial power consumption, average power consumption and instantaneous power consumption of the whole vehicle per hundred kilometers; the instantaneous corrected power consumption per kilometer of the automobile is obtained by combining user driving habit portrait correction, operation scene correction, lateral wind correction, environmental factor dynamic compensation correction and SOC correction; calculating the power consumption assignment and the remaining driving range RRem of the whole vehicle; according to the invention, the endurance prediction precision can be improved to be within + / -2%.

Description

Technical Field

[0001] This invention relates to the field of new energy vehicle technology, and in particular to a method for calculating the remaining driving range of a new energy vehicle. Background Technology

[0002] "Range anxiety" is one of the core pain points commonly faced by users of new energy vehicles (especially pure electric vehicles). Inaccurate remaining driving range displayed on the instrument panel often leads to deviations in users' charging plans, and in severe cases, may cause the car to break down. Currently, many car models only focus on the actual power consumption in the current calculation period when calculating the remaining driving range, ignoring the impact of the external operating environment on energy consumption, which can lead to fluctuations in power consumption and inaccurate energy consumption predictions.

[0003] Patent CN115257461A only focuses on the actual power consumption of the current period, which may be affected by fluctuations in power consumption due to instantaneous road conditions; Patent CN 115352282A only focuses on the mileage of the previous and current periods, and the historical data referenced is too short, which cannot avoid the fluctuations in power consumption caused by instantaneous road conditions; Patent CN115100756A relies too much on driving conditions, requiring a lot of condition testing and condition identification, which will lead to increased workload and computational complexity; Patent CN115503489A calculates the historical average energy consumption by averaging the energy consumption over the past N mileage ranges, which may cause the energy consumption to not be updated for a long time when the car is driving slowly or in congestion. Summary of the Invention

[0004] To address the problems existing in the prior art, the purpose of this invention is to provide a method for calculating the remaining driving range of new energy vehicles. This invention combines historical and current power consumption, and uses user driving habit models, operating scenario models, crosswind correction models, and dynamic environmental factor compensation models to correct the driving range of new energy vehicles, upgrading it from "vehicle self-perception" to "full-scenario collaborative prediction". This can improve the accuracy of range prediction to within ±2%, and accurately calculate the remaining driving range of new energy vehicles.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is: a method for calculating the remaining driving range of a new energy vehicle, comprising the following steps:

[0006] Step 1: Update the vehicle's mileage and driving time every L2 mileage or every specific T1 time, and initialize the reading of the EEPROM to store the remaining driving range;

[0007] Step 2: Calculate the electricity consumption C of the current driving cycle. cyc Remaining battery power C Rem And the initial energy consumption, average energy consumption, and instantaneous energy consumption per 100 kilometers of the whole vehicle;

[0008] Step 3: Combining user driving habit profile correction, operating scenario correction, crosswind correction, dynamic compensation correction for environmental factors, and SOC correction, the instantaneous corrected energy consumption per kilometer of the vehicle is obtained. ;

[0009] Step 4: Calculate and assign the vehicle's energy consumption value. and remaining driving range R Rem .

[0010] As a further improvement of the present invention, in step 1, the initialization of reading the EEPROM to store the remaining driving range is as follows:

[0011] The remaining driving range R calculated after each power-off of the controller Rem Stored to EEPROM; during the controller initialization time T2, the remaining driving range is read from the EEPROM; after the total vehicle mileage is greater than or equal to the calibration parameter L1 kilometers, if the remaining driving range read from the EEPROM is 0 during the vehicle controller initialization time T2, then the initial remaining mileage for the current driving cycle is calculated as: Initial Remaining Mileage = Announced Driving Range Value * SOC Init / 100*SOH / 100 Rewrite to EEPROM for storage backup; where SOC Init The initial SOC for this driving cycle is shown, and the SOH is the real-time battery health.

[0012] As a further improvement of the present invention, in step 2, the current driving cycle consumes electricity C. cyc It equals the actual battery voltage U multiplied by the actual battery current I and the integral over time, i.e. Remaining battery power C Rem =C Total *SOC Init / 100*SOH / 100-C cyc , where C Total The total capacity of the battery pack, SOC Init The initial SOC for this driving cycle; the initial average energy consumption Eint for real-time energy consumption per 100 kilometers of the vehicle, calibrated according to vehicle configuration and test conditions; the average energy consumption E per 100 kilometers within the first specific L1 mileage is calculated. av-L 1 This is the ratio of the total energy consumption within a specific L1 mileage to the energy consumption within L1, divided by 100. Instantaneous power consumption per kilometer .

[0013] As a further improvement to the present invention, in step 3, the user driving habit profile is modified as follows:

[0014] By analyzing driver accelerator pedal depth, braking frequency, cruise control duration, and historical energy consumption data from the cloud, a personalized energy consumption model is established using a clustering algorithm. This model categorizes drivers into aggressive and moderate driving groups. Using moderate driving groups as the baseline, the instantaneous energy consumption (E) per kilometer for aggressive driving users is calculated based on an instantaneous energy consumption calculation method. a Instantaneous energy consumption per kilometer for mild driving users (E) b ;

[0015] Define the energy consumption difference coefficient C fmode :C fmode =(E a -E b ) / E b ×100%;

[0016] For user driving habit profiles, the real-time energy consumption Ei calculation method is as follows: E i =E b ×(1+C fmode ×K i ); where: K i It is a coefficient of the user's driving aggression level, which is assigned different weights based on different parameters such as accelerator pedal depth, braking frequency, and cruise ratio.

[0017] Utilizing cloud data and user profiles based on cloud acceleration, the maximum percentage of users exhibiting aggressive accelerator pedal use is determined based on these profiles. and minimum value Calculate the current user's accelerator pedal user profile collection coefficient. Weighting coefficients: ;

[0018] Utilizing cloud data and user profiles based on cloud-based braking frequency, the maximum aggressive braking force is determined based on these profiles. and minimum value Calculate the user profile collection coefficient for the current user's brake pedal. Weighting coefficients: ;

[0019] Utilizing cloud data, user profiles for cloud-based cruise control based on highway conditions are generated, and the maximum feature value used during cruise control is fed back based on these profiles. and minimum value Calculate the current user's cruise user profile collection coefficient. Weighting coefficients: ;

[0020] Different behaviors have varying degrees of impact on aggression; therefore, a weighted factor based on the accelerator pedal is introduced. Brake and accelerator pedals Cruise weighting factor , , , , Calibration is based on the vehicle's powertrain configuration;

[0021] When K i When =0, E i =E b This represents the gentlest driving state; when K i When =1, E i = E b ×(1+C fmode This represents a typical aggressive driving state.

[0022] As a further improvement to the present invention, the specific modification of the operating scenario in step 3 is as follows:

[0023] Combining high-precision map location information, traffic light signals, and dynamic traffic conditions, the LSTM algorithm is used to classify driving scenarios into congestion, urban areas, highways, mountain roads, and normal driving conditions. Using the normal driving condition as the baseline coefficient 1, different correction coefficients C are assigned based on the different scenario classifications. fScene ;

[0024] The specific crosswind correction is as follows:

[0025] When the angle A between the wind direction and the vehicle's direction of travel is within the range of A1 to A2, it is considered a crosswind; based on wind direction and wind speed... and vehicle speed Calculate the effective lateral wind speed Based on vehicle speed Effective lateral wind speed The crosswind correction coefficient C was calculated by building a correction model using a fuzzy control algorithm. fwind .

[0026] As a further improvement of the present invention, in step 3, the dynamic compensation and correction of environmental factors is specifically as follows:

[0027] Establish battery temperature - Battery actual capacity decay model, temperature-humidity-air conditioning energy consumption model, calculate the dynamic compensation coefficient C for environmental factors. fEvn ; ;

[0028] When the battery temperature Within the battery's high-efficiency temperature range, the battery temperature-battery capacity degradation coefficient is... The value is 1; when the battery temperature is... Below or higher If necessary, calibration should be performed based on the actual low-temperature capacity test data of the battery;

[0029] The temperature-humidity-air conditioning energy consumption model calculates the air conditioning temperature and humidity compensation coefficient based on ambient temperature T and relative humidity Rh. Where k1 is the temperature influence coefficient, k2 is the humidity influence coefficient, k3 is the temperature and humidity influence coefficient, and k4 is the cutoff term. k1, k2, and k3 are obtained based on test fitting, and k4 is obtained through calibration.

[0030] As a further improvement of the present invention, in step 3, the SOC correction is calibrated based on the vehicle driving mode set by different users and the current SOC, and then the SOC correction coefficient C is obtained by looking up a table. fSoc This allows us to determine the instantaneous corrected energy consumption per kilometer of the vehicle. =Ei C fScene C fwind C fEvn C fSoc .

[0031] As a further improvement of the present invention, in step 4, the vehicle power consumption is assigned a value. The calculation method is as follows:

[0032] If the total mileage of the vehicle is less than a specific L1 mileage, then the vehicle's corrected energy consumption will be adjusted. Equal to the initial value of the real-time energy consumption calibration average energy consumption per 100 kilometers. int ;

[0033] After the total mileage of the vehicle is greater than or equal to a specific L1 mileage: if the vehicle speed is 0, the calculated energy consumption E of the vehicle is calculated. Real Calculate the average energy consumption E per 100 kilometers within a specific L1 mileage range. av-L 1 When the vehicle speed is not 0, the total vehicle power consumption E Real =Weighted coefficient for power consumption per kilometer WC1 E av-L 1 +Historical power consumption weighting factor WC2 Among them, WC1 and WC2 are dynamically determined based on the road condition model within the L1 mileage range.

[0034] As a further improvement of the present invention, in step 4, the remaining driving range R Rem The calculation method is as follows:

[0035] The remaining driving range is stored in the EEPROM during the controller initialization time T2; if the controller initialization time is longer than T2, the remaining driving range R... Rem =Battery remaining capacity / Total vehicle energy consumption = C Rem / .

[0036] As a further improvement of the present invention, it also includes: power consumption and cruising range display, storage, and cloud uploading, specifically including:

[0037] The cockpit domain controller controls the instrument panel to display the vehicle's power consumption and remaining driving range in real time;

[0038] The intelligent driving domain controller uploads the vehicle's power consumption and remaining driving range to the cloud and the background monitoring platform;

[0039] When the vehicle is powered off, the total vehicle power consumption and remaining driving range are stored in the EEPROM;

[0040] The charging plan is based on the location of charging stations and queuing information from the vehicle-road coordination system, and the driver is prompted on the central control screen.

[0041] The beneficial effects of this invention are:

[0042] 1. This invention updates and calculates power consumption once per kilometer or per specific T1 time period, and selects the average power consumption of the previous specific L1 kilometers as the historical power consumption. Combined with the current actual power consumption, it can avoid instantaneous road condition power consumption fluctuations and avoid distortion, thus calculating power consumption more accurately.

[0043] 2. The driving range calculated by this invention is corrected based on user driving habit model, operating scenario model, crosswind correction model and environmental factor dynamic compensation model, which can more accurately reflect the actual vehicle range;

[0044] 3. This invention combines meteorological information, high-precision maps, and vehicle-road coordination systems to calculate the remaining driving range, upgrading the driving range of new energy vehicles from "vehicle self-perception" to "full-scenario collaborative prediction", which can improve the accuracy of range prediction to within ±2%. Attached Figure Description

[0045] Figure 1 This is a flowchart of an embodiment of the present invention;

[0046] Figure 2 This is a system architecture diagram of an embodiment of the present invention. Detailed Implementation

[0047] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0048] Example

[0049] Explanation of relevant terms:

[0050] Battery health: An important indicator for measuring battery performance and aging, usually expressed as a percentage (e.g., 80%, 90%), reflecting the ratio of the battery's current capacity to its rated capacity in its brand-new state. A higher value indicates that the battery performance is closer to its initial state; a lower value indicates more severe battery aging, with a corresponding decrease in energy storage capacity and range.

[0051] Remaining driving range: The maximum distance an electric vehicle (such as an electric car or electric bicycle) is expected to travel under certain driving conditions (such as road conditions and driving habits) in its current battery state. It is an important reference indicator for users to judge whether the vehicle can complete the current journey, and is usually displayed in kilometers (km) on the vehicle's dashboard or in-vehicle system.

[0052] Real-time power consumption: The amount of electricity consumed per unit distance traveled by an electric vehicle (such as an electric car or an electric bicycle) under current driving conditions is a core dynamic indicator reflecting the vehicle's energy efficiency. It is usually displayed on the vehicle's instrument panel or central control screen in kWh / km or Wh / km.

[0053] SOC (State of Charge) describes the percentage of a battery's rated capacity remaining at a given moment.

[0054] EEPROM (Electrically Erasable Programmable Read-Only Memory) is a type of non-volatile memory (NVM) that has the characteristics of not losing data after power failure and being able to be erased and reprogrammed by electrical signals.

[0055] like Figure 1 As shown, a method for calculating the remaining driving range of a new energy vehicle is described in this embodiment. This method can be based on, for example... Figure 2 The system framework implementation shown includes the following steps:

[0056] S1: Calculate the unit driving distance, total driving range, and driving time;

[0057] Real-time calculation of vehicle mileage and driving time; vehicle mileage M is based on wheel speed data collected by wheel speed sensors. Integral with time The mileage for a single driving cycle is M1; the total vehicle mileage and driving time are updated every L2 km or every specific T1 time; both L2 and T1 can be calibrated.

[0058] S2: Initialize and read the EEPROM to store the remaining driving range;

[0059] The remaining driving range R calculated by the controller after each power-off Rem Store the remaining driving range in the EEPROM during controller initialization time T2;

[0060] Once the total mileage of the vehicle is greater than or equal to L1 (calibration parameter) kilometers, to prevent data loss from the EEPROM, if the remaining driving range read from the EEPROM is 0 during the vehicle controller initialization time T2, then the initial remaining mileage for the current driving cycle is calculated as: mileage announcement value (calibration value) * SOC. Init / 100*SOH / 100 Rewrite to EEPROM for storage backup; SOC Init - Initial SOC for this driving cycle; SOH - Real-time battery health.

[0061] S3: Calculate the power consumption C of the current driving cycle. cyc ;

[0062] C cyc It equals the actual battery voltage U multiplied by the actual battery current I and the integral over time. .

[0063] S4: Calculate the remaining battery power C Rem ;

[0064] The controller uses the SOC, SOH, and C values ​​sent by the power battery to... Total Calculate the remaining battery power;

[0065] C Rem =C Total *SOC Init / 100*SOH / 100-C cyc , where: C Total - Total battery pack capacity, SOC Init - Initial SOC and SOH for this driving cycle - Real-time battery health, C cyc - The current driving cycle consumes battery power.

[0066] S5: Calculate the initial energy consumption, average energy consumption, and instantaneous energy consumption per 100 kilometers for the entire vehicle;

[0067] 5.1 Initial value of average energy consumption E for real-time energy consumption calibration per 100 kilometers of the whole vehicle int The calibration is performed based on the vehicle configuration and the vehicle test status.

[0068] 5.2 Calculation of average energy consumption E per 100 kilometers within a specific L1 mileage range av-L 1 This is the ratio of the total energy consumption within a specific L1 mileage to the energy consumption within L1, divided by 100. ;

[0069] 5.3 Instantaneous power consumption per kilometer .

[0070] S6: Instantaneous Correction of Vehicle Energy Consumption ;

[0071] 6.1 User driving habit profile correction:

[0072] By analyzing data such as driver accelerator pedal depth, braking frequency, cruise duration, and historical energy consumption in the cloud, a personalized energy consumption model is established using a clustering algorithm. Users are divided into two groups: aggressive drivers (A) and moderate drivers (B). Based on the moderate driver group, an instantaneous energy consumption calculation method based on S5 is used. Calculate the instantaneous energy consumption E per kilometer for aggressive driving users. a Instantaneous energy consumption per kilometer for mild driving users (E) b ;

[0073] Define the energy consumption difference coefficient C fmode :C fmode =(E a -E b ) / E b ×100%;

[0074] For user driving habit profiles, the real-time energy consumption Ei calculation method is as follows: E i =E b ×(1+C fmode ×K i ); where: K i It is the user's driving aggression level coefficient (0~1), which is assigned different weights based on different parameters such as accelerator pedal depth, braking frequency, and cruise ratio;

[0075] Utilizing cloud data and user profiles based on cloud acceleration, the maximum percentage of users exhibiting aggressive accelerator pedal use is determined based on these profiles. and minimum value Calculate the current user's accelerator pedal user profile collection coefficient. Weighting coefficients: ;

[0076] Utilizing cloud data and user profiles based on cloud-based braking frequency, the maximum aggressive braking force is determined based on these profiles. and minimum value Calculate the user profile collection coefficient for the current user's brake pedal. Weighting coefficients: ;

[0077] Utilizing cloud data, user profiles for cloud-based cruise control based on highway conditions are generated, and the maximum feature value used during cruise control is fed back based on these profiles. and minimum value Calculate the current user's cruise user profile collection coefficient. Weighting coefficients: ;

[0078] Different behaviors have varying degrees of impact on aggression; therefore, a weighted factor based on the accelerator pedal is introduced. Brake and accelerator pedals Cruise weighting factor , , , , Calibration is based on the vehicle's powertrain configuration;

[0079] When K i When =0, E i =E b This represents the gentlest driving state; when K i When =1, E i =Eb×(1+C fmode This represents a typical aggressive driving state.

[0080] 6.2 Operational Scenario Correction:

[0081] Combining high-precision map location information, traffic light signals, and dynamic traffic conditions, the LSTM algorithm is used to classify driving scenarios into congestion, urban areas, highways, mountain roads, and normal driving conditions. Using the normal driving condition as the baseline coefficient 1, different correction coefficients C are assigned according to the different scenario classifications. fScene .

[0082] 6.3 Crosswind Correction:

[0083] When the angle A between the wind direction and the vehicle's direction of travel is within the range of A1 to A2, it can be considered a crosswind. This is based on wind direction and wind speed. and vehicle speed Calculate the effective lateral wind speed Based on vehicle speed Effective lateral wind speed The crosswind correction coefficient C was calculated by building a correction model using a fuzzy control algorithm. fwind .

[0084] 6.4 Dynamic compensation for environmental factors:

[0085] Establish battery temperature - Battery actual capacity decay model, temperature-humidity-air conditioning energy consumption model, calculate the dynamic compensation coefficient C for environmental factors. fEvn ; ;

[0086] When the battery temperature If the battery is within the high-efficiency temperature range T1-T2, then the battery temperature-battery capacity decay coefficient is... The value is 1. When the battery temperature... Below or higher If necessary, calibration should be performed based on the actual low-temperature capacity test data of the battery;

[0087] The temperature-humidity-air conditioning energy consumption model calculates the air conditioning temperature and humidity compensation coefficient based on ambient temperature T and relative humidity Rh. Wherein: ambient temperature (°C); Rh - ambient relative humidity (%); k1 - temperature influence coefficient; k2 - humidity influence coefficient; k3 - temperature and humidity influence coefficient; k4 - cutoff term; where k1, k2, and k3 are derived from test fitting; k4 - cutoff term is calibrated.

[0088] 6.5 SOC Correction Factor:

[0089] The SOC correction factor is calibrated based on the vehicle driving mode (normal, economy, sport) set by different users and the current SOC, and then C is obtained by looking up a table. fSoc .

[0090] 6.6 Calculation of Instantaneous Energy Consumption Per Kilometer for Vehicles:

[0091] By combining user driving habit profile correction, operating scenario correction, crosswind correction, dynamic compensation correction for environmental factors, and SOC correction, the instantaneous corrected energy consumption per kilometer of the vehicle is obtained. =Ei C fScene C fwind C fEvn C fSoc .

[0092] S7: Calculate and assign values ​​to the vehicle's total energy consumption. ;

[0093] 7.1 If the total mileage of the vehicle is less than a specific L1 mileage, then the vehicle's corrected energy consumption will be adjusted. Equal to the initial value of the real-time energy consumption calibration average energy consumption per 100 kilometers. int ;

[0094] 7.2 When the total mileage of the vehicle is greater than or equal to a specific L1 mileage:

[0095] 7.2.1 If the vehicle speed is 0, then the calculated energy consumption E of the whole vehicle is... Real Calculate the average energy consumption E per 100 kilometers within a specific L1 mileage range. av-L 1 ;

[0096] 7.2.2 When the vehicle speed is not 0, the total vehicle power consumption E Real =Weighted coefficient for power consumption per kilometer WC1 E av-L 1 +Historical power consumption weighting factor WC2 Among them, WC1 and WC2 are dynamically determined based on the road condition model within the L1 mileage range.

[0097] S8: Calculate remaining driving range R Rem ;

[0098] The remaining driving range is stored in the EEPROM during the controller initialization time T2; if the controller initialization time is longer than T2, the remaining driving range R... Rem =Battery remaining capacity / Total vehicle energy consumption = C Rem / ;

[0099] 8.1 Considering the low-power operation on long slopes, a maximum driving range limit is added. All calculated remaining driving ranges must be less than or equal to the maximum driving range. The maximum driving range = the initial value of the remaining driving range (calibrated value), which is generally the maximum driving range value P2 required by the state (industry recognized). The minimum driving range limit is 0.

[0100] S9: Display, storage and cloud access of power consumption and cruising range;

[0101] 9.1 The cockpit domain controller controls the instrument panel to display the vehicle's power consumption and remaining driving range in real time;

[0102] 9.2 The intelligent driving domain controller uploads the vehicle's total power consumption and remaining driving range to the cloud and the back-end monitoring platform;

[0103] 9.3 When the vehicle is powered off, store the total vehicle power consumption and remaining driving range in the EEPROM;

[0104] 9.4 Combine the location of charging stations and queuing information of the vehicle-road coordination system to plan charging and display the information to the driver on the central control screen.

[0105] The embodiments described above are merely illustrative of specific implementations of the present invention, and while the descriptions are detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A new energy vehicle remaining range calculation method, characterized in that, Comprising the following steps: Step 1, update the vehicle mileage and driving time once every L2 mileage or every specific T1 time, and initialize reading EEPROM to store the remaining range; Step 2, calculate the current driving cycle consumption C cyc , battery remaining power C Rem , and the initial power consumption, average power consumption and instantaneous power consumption per 100 kilometers Step 3, combined with user driving habit image correction, running scene correction, side wind correction, environmental factor dynamic compensation correction, SOC correction, the instantaneous correction power consumption of each kilometer of the car is obtained ; Step 4, calculate the whole vehicle electricity consumption assignment value and the remaining driving range R Rem .

2. The method of claim 1, wherein, In step 1, the initialization reading EEPROM to store the remaining range is as follows: R is the remaining range calculated by the controller after each power-off Rem The remaining range R calculated by the controller after each power-off is stored to the EEPROM; the controller reads the remaining range stored in the EEPROM within the initialization time T2; when the total driving range of the vehicle is greater than or equal to the calibration parameter L1 kilometers, if the remaining range read from the EEPROM is 0 within the initialization time T2 of the vehicle controller, the initial remaining range of the current driving cycle is equal to the announced value of the remaining range * SOC Init / 100*SOH / 100 is re-written to the EEPROM for storage backup; wherein, SOC Init is the initial SOC of the current driving cycle, and SOH is the real-time health degree of the battery.

3. The method of claim 2, wherein, In step 2, the current driving cycle consumes the power C cyc equals the actual battery voltage U multiplied by the actual battery current I and integrated over time, i.e. ; the remaining battery power C Rem = C Total * SOC Init / 100 * SOH / 100 - C cyc , wherein C Total is the total battery power, SOC Init is the initial SOC of the current driving cycle; the real-time energy consumption per 100 km of the whole vehicle is calibrated to obtain the initial average energy consumption Eint, which is calibrated according to the vehicle configuration and the vehicle test state; the average energy consumption E av-L 1 per 100 km within a certain L1 mileage is calculated, which is the ratio of the total energy consumption within the current certain L1 mileage to L1, i.e. ; the instantaneous energy consumption per km is calculated .

4. The method according to claim 3, wherein, In step 3, the user driving habit portrait correction is as follows: The driver's accelerator pedal depth, brake frequency, cruise time length and cloud history energy consumption data are analyzed, a personalized energy consumption model is established through a clustering algorithm, and is divided into an aggressive driving user group and a moderate driving user group, and based on the moderate driving user as a benchmark, the instantaneous energy consumption E of the aggressive driving user per kilometer is calculated based on an instantaneous energy consumption calculation method a and the instantaneous energy consumption E of the moderate driving user per kilometer b ; Definition of energy consumption difference coefficient C fmode : C fmode = (E a - E b ) / E b x 100%; For the user driving habit portrait, the real-time correction energy consumption Ei calculation method is: i =E b ×(1+C fmode ×K i ); wherein: K i is the driving aggressiveness coefficient of the user, different parameters are given different weights based on the accelerator pedal depth, brake frequency, cruise ratio. Utilize cloud data, accelerate based on cloud acceleration user portrait, based on portrait feedback accelerator pedal radical occupation ratio maximum value And minimum value , Calculate the acceleration pedal user portrait acquisition coefficient of the current user Weight coefficient: ; Utilizing cloud data, a user portrait based on cloud braking frequency, a maximum value of aggressive feedback to the brake pedal based on the portrait and a minimum value , a braking pedal user portrait acquisition coefficient of the current user is calculated weight coefficient: ; Utilize cloud data, portrait of cloud cruise user based on highway condition, maximum eigenvalue of cruise use based on portrait feedback With minimum value , calculate the cruise user portrait collection coefficient of the current user Weight coefficient: ; The different behaviors have different influences on the degree of aggressiveness, and a throttle pedal weighting factor is introduced , brake throttle pedal , cruise weighting factor , , , , Calibration is based on vehicle power configuration; When K i = 0, E i = E b , representing the most moderate driving state; when K i = 1, E i = E b x (1 + C fmode ), representing a typical aggressive driving state.

5. The method of claim 4, wherein, In step 3, the running scene correction is as follows: In combination with high-precision map position information, traffic light signals and dynamic traffic states, the driving scene is classified into congestion, urban area, highway, mountain road and ordinary working condition by using the LSTM algorithm, and ordinary working condition is taken as a reference coefficient 1, and different running scene correction coefficients C are calibrated according to different scene classification fScene ; The lateral wind correction is as follows: When the angle A between the wind direction and the vehicle driving direction is within the range of A1-A2, it is considered as a side wind; according to the wind direction, wind speed and vehicle speed , the effective side wind speed is calculated, and based on the vehicle speed , the effective side wind speed , the side wind correction coefficient C is calculated by using a fuzzy control algorithm fwind .

6. The method of claim 5, wherein, In step 3, the environmental factor dynamic compensation correction is as follows: Establishing battery temperature Battery actual capacity attenuation model, temperature-humidity-air conditioning energy consumption model, calculate environmental factor dynamic compensation coefficient C fEvn ; ; When the battery temperature is in the battery high-efficiency zone temperature range, the battery temperature-battery capacity decay coefficient is 1; when the battery temperature is lower than or higher than , the calibration is performed according to the battery low-temperature actual capacity test data; The temperature-humidity-air conditioning energy consumption model is based on the ambient temperature T and the relative humidity Rh to calculate an air conditioning temperature and humidity supplement coefficient ; wherein k1 is a temperature influence coefficient, k2 is a humidity influence coefficient, k3 is a temperature and humidity influence coefficient, and k4 is a cutoff term, k1, k2, and k3 are obtained based on testing and fitting, and k4 is obtained through calibration.

7. The method according to claim 6, wherein, In step 3, the SOC correction is calibrated based on the SOC at present and the vehicle driving mode set by different users to obtain the SOC correction coefficient C fSoc Thus, the instantaneous correction electric consumption per kilometer of the automobile is obtained =Ei C fScene C fwind C fEvn C fSoc .

8. The method according to claim 7, wherein, In step 4, the whole vehicle electricity consumption is assigned The calculation method is specifically as follows: The total driving mileage of the whole vehicle is less than a specific L1 mileage, and the whole vehicle is corrected for electricity consumption equal to the average energy consumption initial value E of the real-time electricity consumption per 100 kilometers int ; When the total driving mileage of the whole vehicle is greater than or equal to a specific L1 mileage: if the vehicle speed is 0, the whole vehicle calculates the electricity consumption E Real For the calculation of the average electricity consumption per 100 kilometers within the specific L1 mileage av-L 1 When the vehicle speed is not 0, the whole vehicle calculates the electricity consumption E Real = the electricity consumption per kilometer weighted coefficient WC1 E av-L 1 + the historical electricity consumption weighted coefficient WC2 ; wherein WC1 and WC2 are dynamically determined according to the road condition model within the L1 mileage range.

9. The method of claim 8, wherein, In step 4, the remaining range R Rem The calculation method is as follows: The controller initializes the remaining cruising range within T2 time as the remaining cruising range stored in the EEPROM; the controller initializes the time greater than T2, and the remaining cruising range R Rem = battery remaining power / vehicle power consumption assignment = C Rem / .

10. The method of claim 1-9, wherein, Also comprising: Electricity consumption and cruising range display, storage and cloud, specifically including: The cabin domain controller controls the instrument to display the real-time vehicle electricity consumption and remaining range; The intelligent driving domain controller uploads the vehicle electricity consumption and remaining range to the cloud and the background monitoring platform; When the vehicle is powered off, the vehicle electricity consumption and remaining range are stored in the EEPROM; Combined with the charging pile distribution position and queuing information of the vehicle-road coordination system, the charging plan is made, and the driver is prompted on the central control large screen.

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