CN111806240B - China working condition-based electric automobile driving range prediction method - Google Patents
China working condition-based electric automobile driving range prediction method Download PDFInfo
- Publication number
- CN111806240B CN111806240B CN202010724748.1A CN202010724748A CN111806240B CN 111806240 B CN111806240 B CN 111806240B CN 202010724748 A CN202010724748 A CN 202010724748A CN 111806240 B CN111806240 B CN 111806240B
- Authority
- CN
- China
- Prior art keywords
- vehicle
- model
- driving
- future
- working condition
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
- B60L3/12—Recording operating variables ; Monitoring of operating variables
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/0097—Predicting future conditions
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2260/00—Operating Modes
- B60L2260/40—Control modes
- B60L2260/44—Control modes by parameter estimation
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2260/00—Operating Modes
- B60L2260/40—Control modes
- B60L2260/50—Control modes by future state prediction
- B60L2260/52—Control modes by future state prediction drive range estimation, e.g. of estimation of available travel distance
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0001—Details of the control system
- B60W2050/0019—Control system elements or transfer functions
- B60W2050/0028—Mathematical models, e.g. for simulation
- B60W2050/0029—Mathematical model of the driver
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0001—Details of the control system
- B60W2050/0019—Control system elements or transfer functions
- B60W2050/0028—Mathematical models, e.g. for simulation
- B60W2050/0031—Mathematical model of the vehicle
- B60W2050/0034—Multiple-track, 2D vehicle model, e.g. four-wheel model
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2510/00—Input parameters relating to a particular sub-units
- B60W2510/24—Energy storage means
- B60W2510/242—Energy storage means for electrical energy
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/10—Longitudinal speed
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/10—Longitudinal speed
- B60W2520/105—Longitudinal acceleration
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/80—Technologies aiming to reduce greenhouse gasses emissions common to all road transportation technologies
- Y02T10/84—Data processing systems or methods, management, administration
Landscapes
- Engineering & Computer Science (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Automation & Control Theory (AREA)
- Human Computer Interaction (AREA)
- Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Sustainable Development (AREA)
- Sustainable Energy (AREA)
- Power Engineering (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
The invention provides a method for predicting the driving range of an electric automobile based on Chinese working conditions, which comprises the following steps: establishing a user driving habit model and a power battery discharge model according to the vehicle running condition in the past set historical time period, and calculating historical energy consumption; obtaining future traffic environment information in a set future time period through intelligent internet of vehicles prediction, establishing a future traffic environment model according to the future traffic environment information, and determining a future driving condition model according to the future traffic environment model and the user driving habit model; establishing a Chinese working condition empirical model by taking a Chinese automobile working condition-light automobile (CLTC-P) as a reference, and comparing the historical energy consumption with the energy consumption corresponding to the Chinese working condition empirical model to obtain a working condition correction model; and calculating the driving mileage of the vehicle according to the working condition correction model and the future driving working condition model. The method and the device can improve the accuracy of the prediction of the driving mileage of the vehicle and improve the use experience of a driver and the quality of the vehicle.
Description
Technical Field
The invention relates to the technical field of mileage prediction of new energy automobiles, in particular to a method for predicting driving mileage of an electric automobile based on China working conditions.
Background
Compared with the traditional fuel oil automobile, the electric automobile has advantages in driving economy and environmental friendliness, but the difference between the power energy supply time and the convenience still exists. Therefore, the 'mileage anxiety' of the general consumers is an important factor for reducing the purchase willingness and the use confidence of the electric automobile, and one of the reasons for the 'mileage anxiety' is that the current electric automobile has a large gap between the displayed driving range and the actual remaining driving range. The current industry mostly calculates the endurance mileage according to a historical time domain, namely, the historical time domain is used for estimating according to the current battery residual capacity and the average energy consumption of a past time domain, but the method has the risk that the prediction is not consistent with the reality, the method does not consider the influence of the driving habits of users, and the future driving condition is not predicted. Meanwhile, the difficulty of accurate prediction lies in prediction of future automobile running conditions, future energy consumption and future battery dischargeable electric quantity, and due to the fact that the prediction of the future automobile running conditions has great uncertainty, under the condition that no reference working condition exists, the defects of large calculation capacity, high cost, poor robustness and the like can be caused, and therefore the method has great significance in accurate calculation of the future time-domain cruising mileage.
Disclosure of Invention
The invention provides an electric automobile driving range prediction method based on Chinese working conditions, which solves the problems that the existing electric automobile driving range is inaccurate in prediction, the prediction is not consistent with the reality easily, and a user cannot effectively manage the charging and the travel arrangement of an electric automobile, can improve the accuracy of the vehicle driving range prediction, and improves the use experience of a driver and the quality of the vehicle.
In order to achieve the above purpose, the invention provides the following technical scheme:
a method for predicting the driving range of an electric automobile based on Chinese working conditions comprises the following steps:
establishing a user driving habit model and a power battery discharge model according to the vehicle running condition in the past set historical time period, and calculating historical energy consumption;
obtaining future traffic environment information in a set future time period through intelligent internet of vehicles prediction, establishing a future traffic environment model according to the future traffic environment information, and determining a future driving condition model according to the future traffic environment model and the user driving habit model;
establishing a Chinese working condition empirical model by taking a Chinese automobile working condition-light automobile (CLTC-P) as a reference, and comparing the historical energy consumption with the energy consumption corresponding to the Chinese working condition empirical model to obtain a working condition correction model;
Calculating the energy consumption in a future set time domain according to the working condition correction model and the future driving working condition model to obtain the future energy consumption;
and calculating the driving mileage of the vehicle according to the future energy consumption and the power battery discharge model.
Preferably, the method further comprises the following steps:
obtaining future vehicle state information, establishing a future vehicle state model, and calculating to obtain future discharge capacity according to the future vehicle state model, the future driving condition model and the power battery discharge model;
and calculating the driving mileage of the vehicle according to the future discharge capacity.
Preferably, the establishing of the driving habit model of the user comprises:
establishing a historical traffic environment model f (t), a historical driving condition model D (u, t) and a historical vehicle state model g (u) of the vehicle according to the driving condition of the vehicle;
inputting the historical traffic environment model f (t) into the historical driving condition model D (u, t) through a formula D (u, t) ═ f (t) g (u), and analyzing the driving habits of the user so as to score the driving habits of the user and form the driving habit model of the user.
Preferably, the establishing of the historical traffic environment model of the vehicle comprises:
acquiring a driving route, a traffic load rate, a temperature and a gas pressure change curve of the vehicle in the set historical time period;
And forming vehicle historical traffic data according to the driving routes, the traffic load ratios and the temperature and pressure change curves.
Preferably, the establishing of the historical driving condition model of the vehicle includes:
acquiring the average speed and the average acceleration of the vehicle in the set historical time period;
and determining the operating conditions of the vehicle in each period according to the average vehicle speed and the average acceleration, and forming a corresponding relation between the vehicle conditions and the vehicle speed change.
Preferably, establishing the historical vehicle state model of the vehicle comprises:
acquiring the driving mode selection of the vehicle within the set historical time period, and the vehicle load quality, the vehicle energy management and the vehicle operation data of the vehicle-mounted electronic equipment;
and forming a corresponding relation between the vehicle state and the driving according to the vehicle operation data so as to establish the historical vehicle state model.
Preferably, the comparing the historical energy consumption with the energy consumption corresponding to the empirical model of the chinese working condition to obtain a working condition correction model includes:
according to the calculation of historical energy consumption and the comparison of the empirical model of the Chinese working condition, a correction coefficient equation C is obtainedh＝H(Dh)C0Wherein, ChEnergy consumption for the historical time domain, C0Energy consumption in China's operating mode, D hOperating conditions in the historical time domain, H (D)h) Correcting a function for the working condition;
obtaining a driving condition correction function H (D) through data analysis and deep learningh)；
Obtaining future working condition D through user driving habit model predictionfAccording to the driving correction coefficient equation and the future working condition DfTo obtain the energy consumption C of the future time domainf＝H(Df)C0。
Preferably, the comparing the historical energy consumption with the energy consumption corresponding to the empirical model of the chinese working condition to obtain a working condition correction model further includes:
evaluating the working state difference of the vehicle-mounted electronic equipment under the standard test conditions of the future driving working condition and the Chinese working condition, and calculating the energy consumption error caused by inconsistency through the working power of each part to obtain a vehicle state correction function L (W);
according to the vehicle state correction function L (W) and the future working condition DfTo obtain the energy consumption C of the future time domainf＝H(Df)L(W)C0。
Preferably, the energy consumption calculation of the vehicle comprises:
according to the formula:
and calculating to obtain the average energy consumption value of the vehicle, wherein C is the average energy consumption value, E is the energy consumption value, D is the driving mileage, U is the terminal voltage of the power battery, and I is the output current of the power battery.
Preferably, the method further comprises the following steps:
predicting the vehicle running route, the traffic load rate, the average vehicle speed and the average acceleration of the set future time period according to the future running condition model;
And predicting the vehicle driving mode, the vehicle load quality, the vehicle energy management and the vehicle-mounted electronic equipment state of the set future time period according to the user driving habit model.
The invention provides an electric automobile driving range prediction method based on Chinese working conditions, which is characterized in that a user driving habit model and a future driving working condition model are established and are compared with a Chinese working condition experience model to obtain a working condition correction model, and then the vehicle driving range is predicted according to the working condition correction model and the residual electric quantity of a power battery.
Drawings
In order to more clearly describe the specific embodiments of the present invention, the drawings to be used in the embodiments will be briefly described below.
FIG. 1 is a schematic diagram of a method for predicting the driving range of an electric vehicle based on the Chinese working conditions.
FIG. 2 is a flowchart of a method for predicting the driving range of an electric vehicle based on the Chinese working conditions.
Detailed Description
In order to make the technical field of the invention better understand the scheme of the embodiment of the invention, the embodiment of the invention is further described in detail with reference to the drawings and the implementation mode.
Aiming at the problem that the displayed driving range and the actual remaining driving range of the current electric automobile have large difference, the invention provides a method for predicting the driving range of the electric automobile based on the Chinese working condition.
As shown in fig. 1 and 2, a method for predicting driving range of an electric vehicle based on chinese working conditions includes:
s1: and establishing a user driving habit model and a power battery discharge model according to the vehicle running condition in the past set historical time period, and calculating historical energy consumption.
S2: the method comprises the steps of obtaining future traffic environment information in a set future time period through intelligent internet of vehicles prediction, establishing a future traffic environment model according to the future traffic environment information, and determining a future driving condition model according to the future traffic environment model and a user driving habit model.
S3: establishing a Chinese working condition empirical model by taking a Chinese automobile working condition-light automobile (CLTC-P) as a reference, and comparing the historical energy consumption with the energy consumption corresponding to the Chinese working condition empirical model to obtain a working condition correction model.
S4: and calculating the energy consumption in a future set time domain according to the working condition correction model and the future driving working condition model to obtain the future energy consumption.
S5: and calculating the driving mileage of the vehicle according to the future energy consumption and the power battery discharge model.
Specifically, the running condition of the Chinese automobile is issued by the Ministry of industry and trust, and domestic experts develop three years of deep research to actually acquire the running data of the automobile exceeding 5000 km, so that the running condition of the automobile conforming to the Chinese condition is formed, and further the working condition of the Chinese automobile, namely a light automobile (CLTC-P), is formed. And (3) analyzing the driving routes, the traffic load rate, the driving speed and the acceleration data of the vehicle in the past time domain to obtain the economic score of the driving habits of the user. It is assumed that the average energy consumption obtained by driving vehicles by different users is different under the same traffic environment (driving route, traffic load rate, temperature, air pressure and other factors) and vehicle state (driving mode setting, air conditioner and other equipment working conditions), and the reason is the driving habits of different users. Therefore, a driving habit model of the user is established, and driving habits of the user such as acceleration, braking, speed control, steering amplitude preference and the like are evaluated. And inputting a historical traffic environment model into the historical driving condition model to analyze the driving habits of the user, and grading the economical efficiency of the driving habits of the user. The traffic environment model of a future time domain is predicted through an intelligent network technology, a driving condition model of the future time domain is obtained through prediction by combining a formed user driving habit model which is updated in real time, and the future vehicle state model can be updated instantly along with the change of the setting of a vehicle by a user, such as adjusting air volume of an air conditioner, starting a radio, increasing passengers and the like. The Chinese working condition empirical model comprises the following components: the model of the running speed curve under the Chinese working condition, the average speed and the average acceleration of a single Chinese working condition. Velocity standard deviation and acceleration standard deviation. The Chinese working condition test conditions comprise: environmental conditions (temperature, wind speed and air pressure) and vehicle state information (air conditioner and electric device service conditions, load quality and the like) of the Chinese working condition test. The empirical model of energy consumption under the Chinese working conditions of the vehicle comprises energy consumption test values under single Chinese working conditions of the vehicle.
In one embodiment, the prediction process is as follows:
1. and obtaining a historical traffic environment model and a historical driving condition model, and obtaining the economic score of the driving habits of the user by analyzing the driving route, the traffic load rate, the vehicle driving speed and the acceleration data in the past period of time.
2. The historical vehicle state model, the historical traffic environment model and the historical driving condition model in the historical driving system in the past period of time are analyzed with the discharge voltage and the discharge current of the power battery in the past period of time to obtain the discharge models of the power battery in different vehicle states, traffic environments and driving conditions.
3. The traffic environment model of a future time domain is predicted through an intelligent network technology, a driving condition model of the future time domain is obtained through prediction by combining a formed user driving habit model which is updated in real time, and the future vehicle state model can be updated instantly along with the change of the setting of a vehicle by a user, such as adjusting air volume of an air conditioner, starting a radio, increasing passengers and the like.
4. The energy consumption analysis system calculates the historical driving system energy consumption in a past time domain.
5. And comparing the calculated energy consumption information with the Chinese working condition empirical model.
6. And obtaining a correction model on the basis of the 4 and 5, wherein the correction model is in different vehicle states, traffic environments and driving conditions, the obtained energy consumption result is in a relation equation set with the energy consumption under the standard test condition of the Chinese working condition, the dependent variable of the equation set is the energy consumption, the independent variable is the vehicle state, the traffic environments and the driving conditions, and the significance of the correction model is that a correction coefficient equation set of the dependent variable and the independent variable is obtained through the analysis of a historical time domain.
7. Through prediction of a future driving system, energy consumption in a future time domain can be predicted by inputting the future driving system information into the correction model.
8. Through prediction of a future driving system, information of the future driving system is input into a power battery discharging model, and the future dischargeable electric quantity can be predicted.
9. The driving range can be obtained through 7 and 8 calculations.
Therefore, through the analysis of the vehicle driving in the historical time domain, the future energy consumption prediction method suitable for the vehicle is obtained by combining the standard test conditions of the Chinese working conditions with the energy consumption value comparison, and the driving habits of the user are considered. The driving range can be scientifically and effectively predicted based on the future driving condition, and the driving range is more suitable for the actual driving of a user. The forecasting method carries out comparative analysis and calculation by taking the Chinese working condition as a reference, greatly reduces the calculation force requirement of the forecasting algorithm, improves the timeliness and the accuracy, and adopts the Chinese working condition method in 2021 and later according to the national driving range mandatory test requirement of the electric automobile, and the test data of the Chinese working condition is existing for automobile manufacturers, so the method is more suitable for mass production practice in application. The method obtains the future energy consumption prediction method suitable for the vehicle by combining the standard test conditions of the Chinese working conditions and the driving habits of the user, and can improve the accuracy of the prediction of the endurance mileage of the vehicle.
The method further comprises the following steps:
s6: obtaining the future vehicle state information, establishing a future vehicle state model, and calculating to obtain the future discharge capacity according to the future vehicle state model, the future driving condition model and the power battery discharge model.
S7: and calculating the driving mileage of the vehicle according to the future discharge capacity.
Specifically, the SOC of the power battery of the current vehicle is predicted through a battery management system BMS, and a future battery voltage and current relation graph is predicted through a future driving model, so that the predicted discharging electric quantity is obtained. Through the prediction of future driving, the energy consumption of a future time domain can be predicted by inputting the future prediction information into the correction model, and the future dischargeable electric quantity can be predicted through the power battery discharge model.
Further, the establishing of the driving habit model of the user comprises:
and establishing a historical traffic environment model f (t), a historical driving condition model D (u, t) and a historical vehicle state model g (u) of the vehicle according to the driving condition of the vehicle.
Inputting the historical traffic environment model f (t) into the historical driving condition model D (u, t) through a formula D (u, t) ═ f (t) g (u), and analyzing the driving habits of the user so as to score the driving habits of the user and form the driving habit model of the user.
Specifically, it is assumed that the average energy consumption obtained by driving the vehicle by different users is different under the same traffic environment (driving route, traffic load rate, temperature and air pressure, etc.) and vehicle state (driving mode setting, air conditioner and other equipment working conditions), and the reason is the driving habits of different users. Therefore, driving habits of the user such as acceleration, braking, speed control, steering amplitude preference and the like are evaluated by establishing a driving habit model of the user. And inputting a historical traffic environment model into the historical driving condition model to analyze the driving habits of the user, and grading the economical efficiency of the driving habits of the user. By the formula D (u, t) ═ f (t) g (u), D (u, t) can be a working condition curve, g (u) is a historical vehicle state model, u is a user driving habit (acceleration, braking, speed control, steering amplitude, etc.), f (t) is a traffic environment model, and t is a traffic environment parameter (traffic route speed limit value, traffic load rate, ambient temperature, etc.). And g, (u) and f (t) are identified by analyzing data of a historical time domain, performing big data analysis and deep learning, and forming a driving habit model of the user.
Establishing the historical traffic environment model of the vehicle comprises: acquiring a driving route, a traffic load rate, a temperature and a gas pressure change curve of the vehicle in the set historical time period; and forming vehicle historical traffic data according to the driving routes, the traffic load ratios and the temperature and pressure change curves.
Specifically, the driving route of the vehicle in the past time domain, the traffic load rate of the vehicle in the past time domain, and the temperature and air pressure changes of the vehicle in the past time domain all become part of the historical traffic environment data.
Establishing the historical driving condition model of the vehicle, comprising: acquiring the average speed and the average acceleration of the vehicle in the set historical time period; and determining the operating conditions of the vehicle in each period according to the average vehicle speed and the average acceleration, and forming a corresponding relation between the vehicle conditions and the vehicle speed change.
Specifically, the historical driving condition model includes: the average vehicle speed of the vehicle in the past period, the average acceleration of the vehicle in the past period, the standard deviation of the vehicle speed of the vehicle in the past period, and the standard deviation of the acceleration of the vehicle in the past period.
Establishing the historical vehicle state model of the vehicle, comprising: acquiring the driving mode selection of the vehicle within the set historical time period, and the vehicle load quality, the vehicle energy management and the vehicle operation data of the vehicle-mounted electronic equipment; and forming a corresponding relation between the vehicle state and the driving according to the vehicle operation data so as to establish the historical vehicle state model.
Specifically, the historical vehicle state model includes: vehicle driving mode selection, vehicle comfort function operation, vehicle load mass, and vehicle energy management. The selection of the driving mode is generally divided into sport, standard and economy, with the degree of power recovery increasing in sequence. Vehicle comfort function behavior: air conditioner, radio and bluetooth music etc.. Vehicle load mass: vehicles, including drivers, carry the mass of passengers and other items. Vehicle energy management: power battery thermal management and high-voltage component thermal management system energy consumption conditions.
Likewise, the future driving prediction may be obtained through a future traffic environment model, a future driving condition model, and a future vehicle state model. Wherein the future traffic environment model comprises: the method comprises the steps of traffic load rate in a future section of time domain, driving route prediction in a future section of time domain and temperature and air pressure changes in a future section of time domain. The future driving condition is predicted by driving a user driving habit model through a future traffic environment model, and the future driving condition model comprises: the average vehicle speed of the vehicle over a future time period, the average acceleration of the vehicle over the future time period, the standard deviation of the vehicle speed of the vehicle over the future time period, and the standard deviation of the acceleration of the vehicle over the future time period. The future vehicle state model includes: vehicle driving mode selection, vehicle comfort function operation, vehicle load mass, and vehicle energy management. The selection of the driving mode is generally divided into sport, standard and economy, with the degree of power recovery increasing in sequence. Vehicle comfort function behavior: air conditioner, radio and bluetooth music etc.. Vehicle load mass: vehicles, including drivers, carry the mass of passengers and other items. Vehicle energy management: power battery thermal management and high-voltage component thermal management system energy consumption conditions.
The step of comparing the historical energy consumption with the energy consumption corresponding to the experience model of the Chinese working condition to obtain a working condition correction model comprises the following steps:
according to the calculation of historical energy consumption and the comparison of the empirical model of the Chinese working condition, a correction coefficient equation C is obtainedh＝H(Dh)C0Wherein, ChEnergy consumption for the historical time domain, C0Energy consumption in China's operating mode, DhOperating conditions in the historical time domain, H (D)h) Is a work condition correction function.
Obtaining driving condition correction function through data analysis and deep learningH(Dh)。
Obtaining future working condition D through user driving habit model predictionfAccording to the driving correction coefficient equation and the future working condition DfTo obtain the energy consumption C of the future time domainf＝H(Df)C0。
The comparing the historical energy consumption with the energy consumption corresponding to the experience model of the Chinese working condition to obtain a working condition correction model further comprises:
and evaluating the working state difference of the vehicle-mounted electronic equipment under the standard test conditions of the future driving working condition and the Chinese working condition, and calculating the energy consumption error caused by inconsistency through the working power of each part to obtain a vehicle state correction function L (W).
According to the vehicle state correction function L (W) and the future working condition DfTo obtain the energy consumption C of the future time domainf＝H(Df)L(W)C0。
Specifically, a correction coefficient equation can be obtained according to the calculation of historical energy consumption and the comparison of the empirical model of the Chinese working condition. Comprises the following steps: and the driving condition correction coefficient equation corrects the difference between the real working condition and the Chinese working condition, and the working condition correction coefficient is related to the average speed, the average acceleration, the speed standard deviation and the acceleration standard deviation in a period of time. And the user driving habit correction coefficient equation is used for evaluating the influence of the user driving habits in a past period on the vehicle driving economy and is related to a vehicle brake pedal stroke curve, an accelerator pedal stroke curve and a steering wheel angular displacement curve. The vehicle state correction coefficient equation can evaluate the difference between the vehicle state and the test state of the Chinese working condition, and is related to the working state of an air conditioner, the working state of an entertainment system, the working state of a thermal management system and the like. By establishing the correction coefficient equation, when the input of a future vehicle system is obtained, the future energy consumption can be calculated through a Chinese working condition empirical model. The future energy consumption prediction method suitable for the vehicle is obtained by combining the standard test conditions of the Chinese working conditions and the energy consumption value comparison, the driving range can be scientifically and effectively predicted based on the future driving condition, and the method is more suitable for the actual use of the vehicle by users. The prediction method performs comparative analysis and calculation by taking the Chinese working condition as a reference, thereby greatly reducing the calculation force requirement of the prediction algorithm and improving the timeliness and the accuracy.
Further, the energy consumption calculation of the vehicle includes:
according to the formula:
and calculating to obtain the average energy consumption value of the vehicle, wherein C is the average energy consumption value, E is the energy consumption value, D is the driving mileage, U is the terminal voltage of the power battery, and I is the output current of the power battery.
The method further comprises the following steps:
s8: and predicting the vehicle running route, the traffic load rate, the average vehicle speed and the average acceleration of the set future time period according to the future driving condition model.
S9: and predicting the vehicle driving mode, the vehicle load quality, the vehicle energy management and the vehicle-mounted electronic equipment state of the set future time period according to the user driving habit model.
Therefore, the invention provides a method for predicting the driving range of an electric automobile based on Chinese working conditions, which comprises the steps of establishing a user driving habit model and a future driving working condition model, comparing the user driving habit model with a Chinese working condition experience model to obtain a corrected model, and predicting the driving range of the automobile according to the corrected model and the residual electric quantity of a power battery.
The construction, features and functions of the present invention have been described in detail with reference to the embodiments shown in the drawings, but the present invention is not limited to the embodiments shown in the drawings, and all equivalent embodiments modified or modified by the spirit and scope of the present invention should be protected without departing from the spirit of the present invention.
Claims (8)
1. A method for predicting the driving range of an electric automobile based on China working conditions is characterized by comprising the following steps:
establishing a user driving habit model and a power battery discharge model according to the vehicle running condition in the past set historical time period, and calculating historical energy consumption;
obtaining future traffic environment information in a set future time period through intelligent internet of vehicles prediction, establishing a future traffic environment model according to the future traffic environment information, and determining a future driving condition model according to the future traffic environment model and the user driving habit model;
establishing a Chinese working condition empirical model by taking a Chinese automobile working condition-light automobile (CLTC-P) as a reference, and comparing the historical energy consumption with the energy consumption corresponding to the Chinese working condition empirical model to obtain a working condition correction model;
The working condition correction model comprises the following steps: a driving condition correction coefficient equation is used for correcting the difference between the real working condition and the Chinese working condition; the driving habit correction coefficient equation of the user is used for evaluating the influence of the driving habits of the user in a past period of time on the driving economy of the vehicle; a vehicle state correction coefficient equation for evaluating the difference between the vehicle state and the test state of the Chinese working condition;
calculating the energy consumption in a future set time domain according to the working condition correction model and the future driving working condition model to obtain the future energy consumption;
calculating the driving mileage of the vehicle according to the future energy consumption and the power battery discharge model;
the step of comparing the historical energy consumption with the energy consumption corresponding to the experience model of the Chinese working condition to obtain a working condition correction model comprises the following steps:
according to the calculation of historical energy consumption and the comparison of the empirical model of the Chinese working condition, a correction coefficient equation C is obtainedh＝H(Dh)C0Wherein, ChEnergy consumption for the historical time domain, C0Energy consumption in China's operating mode, DhOperating conditions in the historical time domain, H (D)h) Correcting a function for the working condition;
by passingThe data analysis and the deep learning obtain a driving condition correction function H (D)h)；
Obtaining future working condition D through user driving habit model prediction fAccording to the driving correction coefficient equation and the future working condition DfTo obtain the energy consumption C of the future time domainf＝H(Df)C0；
Evaluating the working state difference of the vehicle-mounted electronic equipment under the standard test conditions of the future driving working condition and the Chinese working condition, and calculating the energy consumption error caused by inconsistency through the working power of each part to obtain a vehicle state correction function L (W);
according to the vehicle state correction function L (W) and the future working condition DfTo obtain the energy consumption C of the future time domainf＝H(Df)L(W)C0。
2. The China operating condition-based electric vehicle driving range prediction method according to claim 1, further comprising:
obtaining future vehicle state information, establishing a future vehicle state model, and calculating to obtain future discharge capacity according to the future vehicle state model, the future driving condition model and the power battery discharge model;
and calculating the driving mileage of the vehicle according to the future discharge capacity.
3. The China operating condition-based electric automobile driving range prediction method according to claim 2, wherein the establishing of the user driving habit model comprises:
establishing a historical traffic environment model f (t), a historical driving condition model D (u, t) and a historical vehicle state model g (u) of the vehicle according to the driving condition of the vehicle;
Inputting the historical traffic environment model f (t) into the historical driving condition model D (u, t) through a formula D (u, t) ═ f (t) g (u), and analyzing the driving habits of the user so as to score the driving habits of the user and form the driving habit model of the user.
4. The China operating condition-based electric automobile driving range prediction method according to claim 3, wherein establishing the historical traffic environment model of a vehicle comprises:
acquiring a driving route, a traffic load rate, a temperature and a gas pressure change curve of the vehicle in the set historical time period;
and forming vehicle historical traffic data according to the driving routes, the traffic load ratios and the temperature and pressure change curves.
5. The China operating mode-based electric automobile driving range prediction method according to claim 4, wherein the establishing of the historical driving condition model of the vehicle comprises:
acquiring the average speed and the average acceleration of the vehicle in the set historical time period;
and determining the operating conditions of the vehicle in each period according to the average vehicle speed and the average acceleration, and forming a corresponding relation between the vehicle conditions and the vehicle speed change.
6. The China operating condition-based electric automobile driving range prediction method according to claim 5, wherein the establishing of the historical vehicle state model of the vehicle comprises:
Acquiring the driving mode selection of the vehicle within the set historical time period, and the vehicle load quality, the vehicle energy management and the vehicle operation data of the vehicle-mounted electronic equipment;
and forming a corresponding relation between the vehicle state and the driving according to the vehicle operation data so as to establish the historical vehicle state model.
7. The China operating condition-based electric automobile driving range prediction method according to claim 6, wherein the energy consumption calculation of the vehicle comprises:
according to the formula:
calculating to obtain an average energy consumption value of the vehicleAnd C is the average energy consumption value, E is the energy consumption value, D is the driving mileage, U is the terminal voltage of the power battery, and I is the output current of the power battery.
8. The China operating condition-based electric vehicle driving range prediction method according to claim 7, further comprising:
predicting the vehicle running route, the traffic load rate, the average vehicle speed and the average acceleration of the set future time period according to the future running condition model;
and predicting the vehicle driving mode, the vehicle load quality, the vehicle energy management and the vehicle-mounted electronic equipment state of the set future time period according to the user driving habit model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010724748.1A CN111806240B (en) | 2020-07-24 | 2020-07-24 | China working condition-based electric automobile driving range prediction method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010724748.1A CN111806240B (en) | 2020-07-24 | 2020-07-24 | China working condition-based electric automobile driving range prediction method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111806240A CN111806240A (en) | 2020-10-23 |
| CN111806240B true CN111806240B (en) | 2022-04-05 |
Family
ID=72861129
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010724748.1A Active CN111806240B (en) | 2020-07-24 | 2020-07-24 | China working condition-based electric automobile driving range prediction method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111806240B (en) |
Families Citing this family (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112721661B (en) * | 2021-01-29 | 2022-06-14 | 重庆长安新能源汽车科技有限公司 | Estimation method and device for cruising mileage of fuel cell electric vehicle and storage medium |
| CN113159435B (en) * | 2021-04-29 | 2022-11-22 | 浙江吉利控股集团有限公司 | Method and system for predicting remaining driving mileage of new energy vehicle |
| CN113344278A (en) * | 2021-06-17 | 2021-09-03 | 北京百度网讯科技有限公司 | Electric quantity prediction method, apparatus, device, storage medium and program product |
| CN114103987B (en) * | 2021-11-30 | 2024-07-19 | 上海洛轲智能科技有限公司 | Vehicle endurance early warning method and device and electronic equipment |
| CN114407661B (en) * | 2022-02-10 | 2023-11-07 | 北京理工大学重庆创新中心 | Electric vehicle energy consumption prediction method, system, equipment and medium based on data driving |
| CN115188205B (en) * | 2022-07-04 | 2024-03-29 | 武汉理工大学 | Road information-based automobile driving condition correction method |
| WO2024082201A1 (en) * | 2022-10-20 | 2024-04-25 | 宁德时代新能源科技股份有限公司 | Cruising range estimation method and apparatus |
| WO2024144713A1 (en) * | 2022-12-27 | 2024-07-04 | Turkiye'nin Otomobili Girisim Grubu Sanayi Ve Ticaret Anonim Sirketi | A range estimation system |
| CN117325612B (en) * | 2023-09-28 | 2024-05-17 | 山东理工大学 | Hybrid electric vehicle thermal management control system and method |
| CN117705137A (en) * | 2023-12-14 | 2024-03-15 | 中科华芯(东莞)科技有限公司 | All-terrain vehicle endurance management method, system and electronic equipment |
| CN117755308B (en) * | 2023-12-27 | 2024-06-18 | 上海朗尚传感技术有限公司 | Vehicle state monitoring method based on vehicle-mounted sensor |
| CN117972644A (en) * | 2024-03-15 | 2024-05-03 | 东莞万博特科技有限公司 | Electric bicycle endurance evaluation system based on multisource data fusion |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103213504A (en) * | 2013-04-27 | 2013-07-24 | 北京交通大学 | Driving range estimation method of electric car |
| CN110126841A (en) * | 2019-05-09 | 2019-08-16 | 吉林大学 | EV Energy Consumption model prediction method based on road information and driving style |
| CN110329266A (en) * | 2019-07-15 | 2019-10-15 | 腾讯科技（深圳）有限公司 | Can course continuation mileage determine method, apparatus, terminal and storage medium |
| CN110667434A (en) * | 2019-09-11 | 2020-01-10 | 南京航空航天大学 | Working condition-adaptive pure electric vehicle driving mileage estimation method and system |
| CN111301172A (en) * | 2020-02-12 | 2020-06-19 | 浙江吉利汽车研究院有限公司 | Estimation method, device, equipment and storage medium of driving range |
-
2020
- 2020-07-24 CN CN202010724748.1A patent/CN111806240B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103213504A (en) * | 2013-04-27 | 2013-07-24 | 北京交通大学 | Driving range estimation method of electric car |
| CN110126841A (en) * | 2019-05-09 | 2019-08-16 | 吉林大学 | EV Energy Consumption model prediction method based on road information and driving style |
| CN110329266A (en) * | 2019-07-15 | 2019-10-15 | 腾讯科技（深圳）有限公司 | Can course continuation mileage determine method, apparatus, terminal and storage medium |
| CN110667434A (en) * | 2019-09-11 | 2020-01-10 | 南京航空航天大学 | Working condition-adaptive pure electric vehicle driving mileage estimation method and system |
| CN111301172A (en) * | 2020-02-12 | 2020-06-19 | 浙江吉利汽车研究院有限公司 | Estimation method, device, equipment and storage medium of driving range |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111806240A (en) | 2020-10-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111806240B (en) | China working condition-based electric automobile driving range prediction method | |
| CN109733248B (en) | Pure electric vehicle remaining mileage model prediction method based on path information | |
| CN110126841B (en) | Pure electric vehicle energy consumption model prediction method based on road information and driving style | |
| CN111806239A (en) | Electric vehicle endurance mileage measuring method, electronic device, and storage medium | |
| CN105383496B (en) | Route-based distance to empty calculation for a vehicle | |
| WO2022236751A1 (en) | Battery state of charge estimation method and device | |
| CN111452619B (en) | Online energy consumption prediction method and system for electric vehicle | |
| CN103364006B (en) | For determining the system and method for vehicle route | |
| US9759573B2 (en) | Route based energy consumption estimation using physical models | |
| Yavasoglu et al. | Implementation of machine learning based real time range estimation method without destination knowledge for BEVs | |
| US20170050590A1 (en) | System for assessing and/or optimising the operating behaviour | |
| CN114347793B (en) | Estimation method and system for automobile endurance | |
| CN106427589A (en) | Electric car driving range estimation method based on prediction of working condition and fuzzy energy consumption | |
| WO2021218313A1 (en) | Method and device for determining remaining mileage of vehicle and vehicle | |
| CN109109784A (en) | The system and method for vehicle residue driving information are provided according to user behavior | |
| CN102870270A (en) | System and method for range calculation in vehicles | |
| Vogel et al. | Improving hybrid vehicle fuel efficiency using inverse reinforcement learning | |
| US20160001658A1 (en) | Energy consumption rate in distance domain | |
| CN109720213A (en) | A kind of vehicle torque control method and device | |
| CN112406875B (en) | Vehicle energy consumption analysis method and device | |
| JP2024529003A (en) | Method and system for predicting vehicle energy consumption for vehicle movement along a defined route and for route planning - Patents.com | |
| CN114778140A (en) | Vehicle energy consumption bench test method and system | |
| CN115817183A (en) | Method and device for predicting driving range of pure electric vehicle | |
| Deng et al. | A novel real‐time energy management strategy for plug‐in hybrid electric vehicles based on equivalence factor dynamic optimization method | |
| CN116562405A (en) | Intelligent method, device and computer system for reserving charging of extended-range plug-in hybrid electric vehicle |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information |
Address after: No.176, Zhujiang Road, Hefei Economic and Technological Development Zone, Anhui Province Applicant after: Volkswagen (Anhui) Co.,Ltd. Address before: No.176, Zhujiang Road, Hefei Economic and Technological Development Zone, Anhui Province Applicant before: Jianghuai Volkswagen Co.,Ltd. |
|
| CB02 | Change of applicant information | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |