CN111452659A - Intelligent determination method for electric charging time - Google Patents

Intelligent determination method for electric charging time Download PDF

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Publication number
CN111452659A
CN111452659A CN202010258535.4A CN202010258535A CN111452659A CN 111452659 A CN111452659 A CN 111452659A CN 202010258535 A CN202010258535 A CN 202010258535A CN 111452659 A CN111452659 A CN 111452659A
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driver
charging
anxiety
driving
early warning
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CN111452659B (en
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郭栋
李明辉
郑文欣
邹志远
李超超
李春栋
闫伟
郝玉娇
张同庆
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Zibo Billion Electron Co ltd
Shandong University of Technology
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Zibo Billion Electron Co ltd
Shandong University of Technology
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    • 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
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/68—Off-site monitoring or control, e.g. remote control
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • A61B5/163—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state by tracking eye movement, gaze, or pupil change
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • 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
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • 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
    • B60W40/08—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 related to drivers or passengers
    • 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
    • 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
    • B60L2260/54—Energy consumption estimation
    • 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
    • B60W40/08—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 related to drivers or passengers
    • B60W2040/0872—Driver physiology
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
    • 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
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/12—Electric charging stations
    • 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
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/16—Information or communication technologies improving the operation of electric vehicles

Abstract

The invention discloses an intelligent judgment method for electric charging time, and belongs to the field of electric automobiles. It includes S1: determining an early warning coefficient omega in real time according to the personal attribute of a driver and the driving data of the electric automobile; s2: obtaining the residual driving range S of the electric automobile in real timeremainAnd the distance S between the electric vehicle and the target charging stationarrive(ii) a S3: judgment Sremain‑SarriveAnd if the total driving distance is less than the early warning threshold value, sending a charging alarm prompt, wherein the early warning threshold value is determined according to the total driving distance of the electric automobile and the early warning coefficient omega. According to the invention, the personal attribute of the driver, the energy consumption condition of the vehicle, the real-time road condition of the road and other information are considered, through the classification of the mileage anxiety of the driver, the charging early warning and the charging station reservation are carried out on the drivers with different mileage anxiety at proper time according to the mileage anxiety degree and the actual charging requirement of the driver, the electric automobile can be charged timely and accurately, the mileage anxiety of the driver is effectively relieved, and the traveling quality of the driver of the electric automobile is improved.

Description

Intelligent determination method for electric charging time
Technical Field
The invention relates to the field of electric automobiles, in particular to an intelligent judgment method for electric charging time.
Background
The electric automobile has the characteristics of low pollution and high efficiency, and is the main direction of development of new energy automobiles. However, limited driving range and low charging station coverage level are major bottlenecks in the development of current electric vehicles, and these two major problems result in range anxiety of electric vehicle users. In the actual traveling process, part of drivers encounter the situation that the vehicle road is broken due to charging misjudgment, or the battery is overdischarged due to failure of timely charging, so that the performance is seriously reduced, and in the situation, the vehicle owners often worry that the existing electric quantity is not enough to reach the destination, so that 'mileage anxiety' is generated. Driver range anxiety is one of the major problems affecting the popularity and application of electric vehicles.
The existing travel path planning software combines technologies such as GPS, GIS and mobile communication, and according to statistics, more than 80% of travelers can use the services in unfamiliar areas, but the services only consider road information such as road condition information, origin-destination distance and road type, the charging requirement of an electric vehicle is not considered when travel path assistant decision is provided, and the mileage anxiety degree of different drivers is not considered. And even if the charging requirement of the electric automobile is considered, the electric automobile only sends out early warning when the electric quantity of the electric automobile is reduced to a set value, and a user is reminded to charge. However, the system can only provide the same early warning service for all drivers, but different drivers have different tolerance degrees to mileage anxiety, do not finish the judgment of the mileage anxiety state of the driver by combining the static personal attribute indexes and the dynamic vehicle characteristics of the driver, and cannot provide personalized early warning for different drivers, for example, some people worry that the residual electric quantity cannot reach the charging station when the battery electric quantity is more, need early warning, and some people worry that the residual electric quantity cannot reach the charging station only when the battery electric quantity is less, and can remind later. Therefore, providing the same warning to all drivers does not meet the actual demand.
Disclosure of Invention
In order to solve the technical problems, the invention provides an intelligent judgment method for electric charging time, which is used for carrying out charging early warning and charging station reservation on drivers with different mileage anxiety at proper time, effectively relieving the mileage anxiety of the drivers and improving the traveling quality of the drivers of electric vehicles.
The technical scheme provided by the invention is as follows:
an intelligent determination method for electric charging time, the method comprising:
s1: determining an early warning coefficient omega in real time according to the personal attribute of a driver and the driving data of the electric automobile;
s2: obtaining the residual driving range S of the electric automobile in real timeremainAnd the distance S between the electric vehicle and the target charging stationarrive；
S3: judgment Sremain-SarriveAnd if the total driving distance is less than the early warning threshold value, sending a charging alarm prompt, wherein the early warning threshold value is determined according to the total driving distance of the electric automobile and the early warning coefficient omega.
Further, the personal attributes include sex, age, driving age, education level of the driver, and the driving data of the electric vehicle includes driving speed, acceleration, and acceleration distribution rate.
Further, the S1 includes:
s11: acquiring personal attributes of a driver, and acquiring anxiety values of the driver according to a pre-established anxiety level estimation model, wherein the anxiety level estimation model reflects the relationship between the personal attributes of the driver and the anxiety values of the driver;
s12: acquiring driving data of the electric automobile in real time, and correcting the anxiety value of the driver through the driving data of the electric automobile;
s13: clustering the anxiety values by a fuzzy clustering method, grading the mileage anxiety degree of the driver, and respectively setting early warning coefficients according to the grades of the mileage anxiety degree.
Further, the anxiety level prediction model is determined by the following method:
s1': selecting a certain number of driver samples to carry out investigation through a multidimensional driving style scale, and determining personal attributes reflecting the mileage anxiety of the driver through principal component analysis and result inspection;
s2': a simulated driving experiment and an actual road experiment are developed, physiological characteristics of a driver are collected in the experiment, mileage anxiety is quantized into state intervals of different levels through the physiological characteristics, and meanwhile, an anxiety level estimation model is determined by combining personal attributes of the driver.
Further, the physiological characteristics of the driver comprise heart rate data, electroencephalogram data and eye movement data, and the heart rate data, the electroencephalogram data and the eye movement data are acquired through an electrocardiograph, an electroencephalograph and an eye movement instrument.
Further, the S12 includes:
the method comprises the steps of collecting driving data of the electric automobile in real time, carrying out coordinate change and data filtering processing on the driving data to obtain driving characteristic index values, and correcting anxiety values of a driver through the driving characteristic index values.
Further, the method for intelligently determining the electric charging time further includes:
s4: and sending charging reservation request information to a charging station management platform, wherein the charging reservation request information comprises vehicle information and real-time road condition information, the charging station management platform finishes the determination of the charging starting moment and the reservation duration according to the charging reservation request information, and finishes the selection of a charging station and a charging mode according to the position of a target charging station, the type of a battery and the capacity of the battery.
Further, the method for intelligently determining the electric charging time further includes:
s5: and planning an optimal route to the target charging station according to the position of the target charging station and by combining the real-time road condition information.
Further, in S3, the early warning threshold values are 0.25 ω l and 0.05 ω l, where l is the total driving range of the electric vehicle; when S isremain-SarriveWhen the voltage is less than 0.25 omega l, a first charging alarm prompt is sent out, and when S is less than 0.25 omega l, the first charging alarm prompt is sent outremain-SarriveAnd when the current is less than 0.05 omega l, sending a second charging alarm prompt and prompting the driver to charge in time.
The invention has the following beneficial effects:
according to the invention, the personal attribute of the driver, the energy consumption condition of the vehicle, the real-time road condition of the road and other information are considered, through the classification of the mileage anxiety of the driver, the charging early warning and the charging station reservation are carried out on the drivers with different mileage anxiety at proper time according to the mileage anxiety degree and the actual charging requirement of the driver, the electric automobile can be charged timely and accurately, the mileage anxiety of the driver is effectively relieved, and the traveling quality of the driver of the electric automobile is improved.
Drawings
Fig. 1 is a flowchart of an electric charging time intelligent determination method according to the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings and specific embodiments. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
When an electric vehicle is running, a vehicle owner often worrys about the anxiety of mileage caused by insufficient available electricity to reach a destination. Relevant researches show that the mileage anxiety easily causes negative emotions such as fear and irritability of a driver, obstructs the normal cognitive process of the driver, and causes a series of problems such as dangerous driving, and therefore the mileage anxiety of the driver is one of the main problems influencing the popularization and application of the electric automobile.
The driver is prompted to go to the charging station for charging, a reasonable driving route is planned for the driver to go to the charging station, and the driver mileage anxiety can be greatly eliminated. However, as described in the background, different drivers are not tolerant to range anxiety, and therefore, it is necessary to provide the charging warning to different drivers at different times, which is the method of the present invention.
The embodiment of the invention provides an intelligent judgment method for an electric charging moment, which comprises the following steps of:
s1: and determining an early warning coefficient omega in real time according to the personal attribute of the driver and the driving data of the electric automobile.
The personal attributes include sex, age, driving age, education course, etc. of the driver, and the driving data of the electric vehicle includes driving speed, acceleration, and acceleration distribution rate. The mileage anxiety of the driver is influenced by the personal attribute of the driver and the current driving state (driving data of the electric automobile), the personal attribute of the driver is a fixed influence factor and is not changed within a long period of time, and the driving data of the electric automobile is a variable influence factor and is changed in real time in the driving process of each time.
The personal attribute and the current driving state of the driver influence the mileage anxiety degree of the driver, the mileage anxiety degree of the driver influences the timing of the charging early warning, if the mileage anxiety degree is high, the worry degree of the driver is high, the charging early warning needs to be carried out in advance, if the mileage anxiety degree is low, the worry degree of the driver is low, and the charging early warning can be carried out in a slightly delayed manner. Therefore, the early warning coefficient is required to be determined in real time according to the personal attributes of the driver and the driving data of the electric automobile, and a foundation is provided for subsequent early warning.
S2: obtaining the residual driving range S of the electric automobile in real timeremainAnd the distance S between the electric vehicle and the target charging stationarrive。
The remaining driving range of the electric vehicle refers to the driving range of the electric vehicle when the electric quantity is lower than 100% in the actual driving process, and the remaining driving range of the electric vehicle can be determined according to the battery SOC and the real-time road condition information.
S3: judgment Sremain-SarriveAnd if the total driving distance is less than the early warning threshold value, sending a charging alarm prompt, wherein the early warning threshold value is determined according to the total driving distance of the electric automobile and the early warning coefficient omega.
Sremain-SarriveThe distance that the electric vehicle can still run after the electric vehicle reaches the target charging station is referred to, namely the remaining continuous running of the electric vehicle at the moment when the electric vehicle reaches the target charging stationAnd (4) mileage. The remaining range acceptable to different drivers has different values, a driver with high range anxiety degree needs to have a longer remaining range to leave enough insurance, and a driver with low range anxiety degree can accept a shorter remaining range.
The early warning threshold value is determined according to the total driving range of the electric automobile and the early warning coefficient omega, and the threshold value is related to the range anxiety degree of the driver. That is to say, different early warning threshold values are set according to the level of the anxiety degree of the mileage, the threshold values correspond to the remaining driving range values at the charging time acceptable for different drivers, when the remaining driving range of the electric vehicle reaching the target charging station is smaller than the early warning threshold value, a charging alarm prompt is provided for the driver, and meanwhile, the charging station reservation can be carried out according to the early warning degree.
According to the invention, the early warning coefficient is determined according to the personal attribute of the driver and the driving data of the electric automobile, the early warning threshold value is determined according to the early warning coefficient, when the remaining driving range of the electric automobile reaching the target charging station is less than the early warning threshold value, the charging alarm prompt is sent out, the charging alarm prompt adaptive to the driver and the driving data is provided for the drivers with different range anxiety degrees, the different drivers are helped to realize charging automatic early warning at different occasions, the electric automobile can be charged timely and accurately, the range anxiety of the drivers is effectively relieved, and the traveling quality of the drivers of the electric automobiles is improved.
S1 of the present invention includes:
s11: the method comprises the steps of obtaining personal attributes of a driver, obtaining anxiety values of the driver according to a pre-established anxiety level estimation model, wherein the anxiety level estimation model reflects the relationship between the personal attributes of the driver and the anxiety values of the driver.
The method comprises the step of quantifying the anxiety degree of the driver according to the personal attributes of the driver to obtain the anxiety value of the driver. The anxiety level prediction model is a pre-established relationship between the personal attribute of the driver and the anxiety value of the driver.
S12: the driving data of the electric automobile is collected in real time, and the anxiety value of the driver is corrected through the driving data of the electric automobile.
Specifically, in order to improve the accuracy of estimating the mileage anxiety value, the driving data, which is an online index, is used as a dynamic correction factor, and the mileage anxiety value of the driver is corrected according to the real-time running state of the vehicle, so that the real-time evaluation of the driving state of the driver is realized.
S13: clustering the anxiety values by a fuzzy clustering method, grading the mileage anxiety degree of the driver, and respectively setting early warning coefficients according to the grades of the mileage anxiety degree.
The range anxiety value is an objective reflection of the remaining driving range value at different acceptable charging moments by the driver, and therefore the range anxiety value can be divided into different anxiety levels according to the value. Specifically, the anxiety values are clustered through a fuzzy clustering method, the mileage anxiety degrees of drivers are graded, and the different mileage anxiety degrees of different drivers are graded to serve as an early warning coefficient omegaiAnd i is the driver's serial number.
Then comparing the distance S to the target charging station in real timearriveAnd the remaining driving range SremainThe different early warning conditions are set for drivers with different mileage anxiety degrees, and the early warning threshold value of the user with high mileage anxiety degree is larger.
The anxiety level prediction model is determined by the following method:
s1': a certain number of driver samples are selected to carry out investigation through a multidimensional driving style scale, and personal attributes reflecting the mileage anxiety of the driver are determined through principal component analysis and result inspection.
In the step, the psychological state of mileage anxiety of the electric vehicle travelers in the process of using the electric vehicle is investigated by using a questionnaire survey mode, wherein the main content of the questionnaire survey comprises personal basic attribute information of the respondents and cognitive conditions of the electric vehicle. Based on relevant data collected by the questionnaire, SPSS software is utilized, through effectiveness degree analysis, irrelevant factors are removed, factors influencing mileage anxiety are determined based on principal component analysis, and a judgment result is determined through fuzzy comprehensive evaluation, so that the anxiety score of the mileage anxiety of the driver is obtained.
S2': a simulated driving experiment and an actual road experiment are developed, physiological characteristics of a driver are collected in the experiment, mileage anxiety is quantized into state intervals of different levels through the physiological characteristics, and meanwhile, an anxiety level estimation model is determined by combining personal attributes of the driver.
The physiological characteristics of the driver comprise heart rate data, electroencephalogram data, eye movement data and the like, and the heart rate data, the electroencephalogram data and the eye movement data are acquired through an electrocardiograph, an electroencephalograph and an eye movement instrument.
The method comprises the steps of quantifying mileage anxiety degree, wherein the mileage anxiety degree is characterized through collected physiological characteristic data in a quantified mode, specifically, a simulated driving experiment and an actual road experiment need to be carried out to obtain data, and wearable devices such as an electrocardiograph, an EEG (electrocardiograph), an eye tracker and the like are used for recording various physiological characteristics of a driver in the driving process in real time so as to analyze actual reaction mechanisms of driver groups covering different personal attributes and cognitive differences to the mileage anxiety.
The experimental process comprises the following steps:
1) and collecting heart rate data. In the experimental process, firstly, the heart rate data of a driver in a 5min static state are acquired, then the heart rate increase rate is used as the physiological change degree of the driver, and the calculation formula is as follows:
Figure BDA0002438384500000071
wherein N isiRepresenting the heart rate increase rate of the driver at a certain moment in the driving process; n isiA value representing the driver's heart rate at a certain moment in the driving process, in bpm;
Figure BDA0002438384500000072
representing the heart rate in bpm for a stationary driver.
Calculated heart rate growth rate NiProportional to the severity of the driver's range anxiety, so heart rate increase rate quantification may be usedThe mileage anxiety degree is characterized.
2) The EEG technology is used for monitoring the brain waves of a driver in real time in an experiment, a sensor is worn on the head of the driver, the change data of the brain waves of the driver are stored and displayed through an instrument, β wave bands in the brain waves are analyzed, when the driver is in mileage anxiety, the wave bands can be in an obvious growth trend, and the data quantification of β wave bands can be adopted to represent the mileage anxiety degree.
3) And (4) acquiring eye movement data. The eye tracker is used for capturing the eye movement of the driver in real time, so that the general eye movement rule of the driver in the driving process is summarized, and the mileage anxiety analysis characteristic information of the driver is further improved.
The continuous watching time of the driver on the residual electric quantity on the instrument panel is mainly used as an index for analysis, when the time exceeds 100ms, the driver can be regarded as watching once, and the longer the watching time of the driver on the residual electric quantity is, the higher the mileage anxiety degree is.
According to the physiological characteristic indexes of the driver measured in the experimental process, a relation model between the physiological indexes of the driver and the mileage anxiety is established by utilizing a fuzzy neural network, the mileage anxiety degree of the driver is predicted by the indexes through the model, and meanwhile, the anxiety level of the driver is predicted by combining the personal attributes of the driver.
In the present invention, S12 includes:
the method comprises the steps of collecting driving data of the electric automobile in real time, carrying out coordinate change and data filtering processing on the driving data to obtain driving characteristic index values, and correcting anxiety values of a driver through the driving characteristic index values.
Specifically, the driving data mainly comprises dynamic driving characteristics such as driving speed, acceleration and acceleration distribution rate, a series of online indexes such as corresponding acceleration, angular speed and angle are obtained according to time sequence by means of a sensor at a moving end, and a determined dynamic driving characteristic index value is obtained through data processing processes of coordinate change and data filtering, and the driving characteristic index value can be used for correcting mileage anxiety value obtained according to personal attributes in real time.
After the charging early warning is sent out, the driver agrees to enter a charging appointment stage:
s4: and sending charging reservation request information to a charging station management platform, wherein the charging reservation request information comprises vehicle information and real-time road condition information, the charging station management platform completes the determination of the charging starting moment and the reservation duration according to the charging reservation request information, and simultaneously completes the selection of a charging station and a charging mode according to the position of a target charging station, the type of a battery and the capacity of the battery to assist a driver in completing charging reservation service.
After assisting the driver to accomplish the reservation of the charging device that is vacant nearby, still include:
s5: and planning an optimal route for a driver to go to the target charging station by calling a map according to the position of the target charging station and combining the real-time road condition information.
Specifically, based on road characteristics and road condition characteristics under real-time road condition information, when a driver goes to a reserved charging station, the driver can select a driving route with optimal time, optimal distance or optimal comprehensive mode.
In the invention, the early warning can be carried out for a plurality of times, as a specific example: in S3, the early warning threshold may be 0.25 ω l and 0.05 ω l, where l is the total driving range of the electric vehicle; when S isremain-SarriveWhen the voltage is less than 0.25 omega l, a first charging alarm prompt is sent out, and when S is less than 0.25 omega l, the first charging alarm prompt is sent outremain-SarriveAnd when the current is less than 0.05 omega l, sending a second charging alarm prompt and prompting the driver to charge in time.
Namely, the early warning is divided into a mild early warning and a severe early warning, and the mild early warning condition is 0.25 l: 25% of total driving range, and the condition of heavy early warning is 0.05 l: 5% of the total driving range.
Specifically, firstly, carrying out mild early warning cycle monitoring, and when S is detectedremain-Sarrive<When the power is 0.25 omega l, an early warning instruction is sent to the early warning module, and a voice and light early warning prompt is provided to prompt a driver to turn off high-energy-consumption equipment such as an air conditioner; secondly, carrying out severe early warning cyclic monitoring, wherein the triggering precondition is that mild early warning conditions are met, and when S is reachedremain-Sarrive<0.05ωAnd during the period I, sending an early warning instruction to the early warning module, providing voice and light early warning prompts, and prompting a driver to charge in time when the electric quantity is insufficient.
In conclusion, the method and the device consider the personal attributes of the driver, the energy consumption condition of the vehicle, the real-time road conditions of the road and other information, and perform charging early warning and charging station reservation on the drivers with different mileage anxiety at proper time according to the mileage anxiety degree and the actual charging requirement of the driver by classifying the mileage anxiety of the driver, so that the electric automobile can be charged timely and accurately, the mileage anxiety of the driver is effectively relieved, and the traveling quality of the driver of the electric automobile is improved.
It should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.

Claims (9)

1. An electric charging time intelligent determination method is characterized by comprising the following steps:
s1: determining an early warning coefficient omega in real time according to the personal attribute of a driver and the driving data of the electric automobile;
s2: obtaining the residual driving range S of the electric automobile in real timeremainAnd the distance S between the electric vehicle and the target charging stationarrive；
S3: judgment Sremain-SarriveIf the current value is less than the early warning threshold value, if so, the charge is sent outAnd (4) electric alarm prompt, wherein the early warning threshold value is determined according to the total driving range of the electric automobile and the early warning coefficient omega.
2. The intelligent determination method for electric charging time according to claim 1, wherein the personal attributes include sex, age, driving age, education level of the driver, and the driving data of the electric vehicle includes driving speed, acceleration, and acceleration distribution rate.
3. The method according to claim 2, wherein S1 includes:
s11: acquiring personal attributes of a driver, and acquiring anxiety values of the driver according to a pre-established anxiety level estimation model, wherein the anxiety level estimation model reflects the relationship between the personal attributes of the driver and the anxiety values of the driver;
s12: acquiring driving data of the electric automobile in real time, and correcting the anxiety value of the driver through the driving data of the electric automobile;
s13: clustering the anxiety values by a fuzzy clustering method, grading the mileage anxiety degree of the driver, and respectively setting early warning coefficients according to the grades of the mileage anxiety degree.
4. The method for intelligently determining an electric charging time according to claim 3, wherein the anxiety level prediction model is determined by:
s1': selecting a certain number of driver samples to carry out investigation through a multidimensional driving style scale, and determining personal attributes reflecting the mileage anxiety of the driver through principal component analysis and result inspection;
s2': a simulated driving experiment and an actual road experiment are developed, physiological characteristics of a driver are collected in the experiment, mileage anxiety is quantized into state intervals of different levels through the physiological characteristics, and meanwhile, an anxiety level estimation model is determined by combining personal attributes of the driver.
5. The method for intelligently determining the power-driven charging time according to claim 4, wherein the physiological characteristics of the driver include heart rate data, brain electrical data and eye movement data, and the heart rate data, the brain electrical data and the eye movement data are acquired through an electrocardiograph, an electroencephalograph and an eye movement instrument.
6. The method according to claim 3, wherein the step S12 includes:
the method comprises the steps of collecting driving data of the electric automobile in real time, carrying out coordinate change and data filtering processing on the driving data to obtain driving characteristic index values, and correcting anxiety values of a driver through the driving characteristic index values.
7. The electric-powered charging time intelligent determination method according to any one of claims 1 to 6, characterized by further comprising:
s4: and sending charging reservation request information to a charging station management platform, wherein the charging reservation request information comprises vehicle information and real-time road condition information, the charging station management platform finishes the determination of the charging starting moment and the reservation duration according to the charging reservation request information, and finishes the selection of a charging station and a charging mode according to the position of a target charging station, the type of a battery and the capacity of the battery.
8. The electric charging time intelligent determination method according to claim 7, further comprising:
s5: and planning an optimal route to the target charging station according to the position of the target charging station and by combining the real-time road condition information.
9. The method according to claim 7, wherein in S3, the warning thresholds are 0.25 ω l and 0.05 ω l, where l is a total driving range of the electric vehicle; when S isremain-SarriveLess than 0.25 omega l, a first charging alarm prompt is sentWhen S isremain-SarriveAnd when the current is less than 0.05 omega l, sending a second charging alarm prompt and prompting the driver to charge in time.
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