CN109733248A - Pure electric automobile remaining mileage model prediction method based on routing information - Google Patents

Pure electric automobile remaining mileage model prediction method based on routing information Download PDF

Info

Publication number
CN109733248A
CN109733248A CN201910018307.7A CN201910018307A CN109733248A CN 109733248 A CN109733248 A CN 109733248A CN 201910018307 A CN201910018307 A CN 201910018307A CN 109733248 A CN109733248 A CN 109733248A
Authority
CN
China
Prior art keywords
model
speed
prediction
vehicle
remaining
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.)
Granted
Application number
CN201910018307.7A
Other languages
Chinese (zh)
Other versions
CN109733248B (en
Inventor
郭建华
王引航
刘纬纶
刘翠
石大排
刘昨非
刘康杰
初亮
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Jilin University
Original Assignee
Jilin University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Jilin University filed Critical Jilin University
Priority to CN201910018307.7A priority Critical patent/CN109733248B/en
Publication of CN109733248A publication Critical patent/CN109733248A/en
Application granted granted Critical
Publication of CN109733248B publication Critical patent/CN109733248B/en
Expired - Fee Related legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • 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

Landscapes

  • Electric Propulsion And Braking For Vehicles (AREA)

Abstract

The pure electric automobile remaining mileage model prediction method based on routing information that the invention discloses a kind of, comprising the following steps: driver history running data is analyzed, extraction path information, generate the state transition probability matrix for meeting Characteristics of drivers' behavior；Road information and corresponding state transition probability matrix based on Future Path generate prediction speed；Parameter estimation model is established, the driving parameters for influencing automobile energy consumption and remaining driving mileage are estimated；RDR computation model is established to predict vehicle remaining driving mileage, the driving parameters of prediction speed and parameter estimation model estimation that energy consumption prediction model is obtained using speed prediction model calculate vehicle energy consumption rate as mode input；Remaining energy model is for estimating Vehicular battery dump energy；Comprehensive vehicle energy expenditure rate and the i.e. predictable vehicle remaining driving mileage of battery remaining power, and shown by remaining driving mileage display model.

Description

Pure electric automobile remaining mileage model prediction method based on routing information
Technical field
The pure electric automobile remaining mileage model prediction method based on routing information that the present invention relates to a kind of, belongs to new energy Automobile technical field.
Background technique
Pure electric automobile (Battery Electric Vehicle, BEV) compares in traditional in terms of energy consumption and discharge Combustion engine automobile has apparent advantage, such as good dynamic property, and running noise is small, energy conservation and zero-emission etc..But due to by battery skill The limitation of art development, the continual mileage of electric car is also shorter and the charging time is longer.Pure electric automobile driver can worry Whether they can reach the destination under current remaining, this is referred to as " mileage anxiety ", and mileage anxiety is current limitation electricity One of the principal element of electrical automobile acceptance level.Obviously, high capacity cell is installed, quick charge and establishes more charging stations and is It is effectively relieved and solves the effective means of " mileage anxiety ", still, due to being limited by state-of-the art and fund condition, There is still a need for longer times to be just able to achieve for these methods.Another effective means is that " accurate remaining driving mileage is pre- Survey ", driver can judge that vehicle is by " remaining driving mileage " (Remaining the Driving Range, RDR) of prediction It is no to reach the destination, and stroke and charging place are planned in advance.In addition, accurately mileage prediction is also electric car The basis of energy management, according to remaining driving mileage, BEV Energy Management System can improve electricity with the use of reasonably optimizing electric energy The mileage travelled of electrical automobile, this can also alleviate driver " mileage anxiety ".
Currently, many researchers propose a variety of BEV energy consumptions and RDR prediction technique, these methods are substantially segmented into Two classes: the RDR prediction based on historical data and the RDR prediction based on model.RDR prediction technique based on historical data is current Common RDR prediction technique on the BEV of comercial operation.This method counts the historical energy consumption data of a period of time, false The fixed following energy consumption and current energy consumption are close, current energy expenditure rate are calculated, then according to battery charge state (State Of Charge, SOC) dump energy is estimated, finally obtain the remaining driving mileage of prediction.The advantages of this method is to calculate Simply, real-time is good and is easily achieved.So the RDR prediction of most of electric cars is all in this way.But this The shortcomings that kind method is: when the following operating condition has greatly changed, the error of this prediction can become larger or even prediction result is complete Trust completely without method.BEV energy consumption is affected by many factors, such as operating condition (speed), driver's driving behavior (driving style), slope Degree, temperature, battery SOC, cell health state (State of Health, SOH), wind speed, pavement conditions etc..Wherein, operating condition (speed) is to influence one of the main factor of energy consumption, under different types of operating condition, such as city, outskirts of a town and high speed, it is electronic There are huge difference for automobile energy consumption, it is clear that when working condition changes, the RDR prediction based on historical data will necessarily be lost Effect.
Summary of the invention
In order to solve the above problems existing in the present technology, the present invention provides a kind of pure electric automobile based on routing information Remaining mileage model prediction method.A certain number of driver history running datas are analyzed, extraction path information, is generated Meet the state transition probability matrix (Transition Probability Matrix, TPM) of Characteristics of drivers' behavior；Then Road information and corresponding TPM based on Future Path are based on Markov random theory, generate a kind of by future trajectory letter Cease the prediction operating condition (speed) of control.Next, being based on electric car performance test, the accurate energy consumption model of electric car is established, The model, which considers temperature, the gradient, battery charge state (SOC) etc. mainly, influences the factor of energy consumption and RDR.From vehicle-mounted sensing Device, weather forecast system, to acquisite approachs information in electronic map, and model estimation is carried out to the relevant parameter in auto model, The prediction speed that the first step generates is input in the energy consumption model, realizes the Accurate Prediction of BEV energy consumption and remaining driving mileage.
The purpose of the present invention is what is be achieved through the following technical solutions:
A kind of pure electric automobile remaining mileage model prediction method based on routing information, comprising the following steps:
Step 1: establishing speed prediction model to generate Future Path prediction speed: to a certain number of driver histories Running data is analyzed, extraction path information, generates the state transition probability matrix for meeting Characteristics of drivers' behavior；Based on not Come the road information and corresponding state transition probability matrix in path, be based on Markov random theory, generates by the following road The prediction speed of road information control；
Step 2: establishing parameter estimation model, the driving parameters for influencing automobile energy consumption and remaining driving mileage are estimated Meter；
Step 3: establishing RDR computation model to predict vehicle remaining driving mileage: RDR computation model includes: energy consumption prediction Model, remaining energy model and remaining driving mileage display model；Energy consumption prediction model is obtained with speed prediction model Prediction speed and parameter estimation model estimation driving parameters as mode input, calculate vehicle energy consumption rate；It is remaining Energy predicting model is for estimating Vehicular battery dump energy；Comprehensive vehicle energy expenditure rate and battery remaining power are i.e. predictable Vehicle remaining driving mileage, and shown by remaining driving mileage display model.
The beneficial effects of the present invention are:
(1) present invention combines history operating condition and Future Path information, while considering driver's driving characteristics and road Diameter information characteristics carry out controllable random operating condition prediction.This method real-time is good, and accuracy is high, Characteristics of drivers' behavior adaptability It is good.
(2) it is the Markov indexed that driver history floor data, which becomes after processing with driving style and road type, Probability transfer matrix, amount of storage is small, convenience of calculation, and can real-time update.Increase with mileage travelled, probability transfer matrix is to driving Member's driving behavior representativeness enhancing, but amount of storage remains unchanged；When the road type of storage increases, forecasting accuracy is big Width improves, and amount of storage only slightly increases, and adapts to onboard system requirement.
(3) the BEV energy consumption model proposed considers the road informations such as temperature, the gradient, battery charge state (SOC) to energy The influence of consumption, and model parameter is accurately estimated.The model uses reverse modeling method, is tested based on vehicle performance, comprehensive Conjunction considers vehicle driving energy consumption, dynamical system transmission loss, and auxiliary system energy consumption and regenerative braking recover energy, model essence Degree is high, and calculation amount is small, and real-time is good.
Detailed description of the invention
A specific embodiment of the invention will be described in detail below by connected applications example.
Fig. 1 is BEV and Energy Management System hardware configuration；
Fig. 2 is remaining driving mileage prediction algorithm framework；
Fig. 3 is automobile stress balance figure；
Fig. 4 is different rotating speeds lower transmission system wasted power and power input to machine relationship trial curve；
Fig. 5 is battery open circuit voltage and SOC relationship trial curve；
Fig. 6 is speed generating algorithm flow chart；
Fig. 7 is certain urban road test routine and routing information；
Fig. 8 is certain urban road test speed and routing information；
Fig. 9 is city " two classes " road condition segment；
Figure 10 is the acceleration of city " two classes " road and decelerating phase；
Figure 11 is the gridding of boost phase vehicle speed data and generation TPMs schematic diagram；
Figure 12 is the acceleration of city " two classes " road and decelerating phase TPMs；
Figure 13 is urban road with reference to operating condition and prediction speed；
Figure 14 is operating condition section generating algorithm schematic diagram；
Figure 15 is different driver's urban road energy consumption actual measurements and prediction curve；
Figure 16 is urban road RDR reality and prediction curve.
Specific embodiment
The present invention will be further described with reference to the accompanying drawing.Following instance will be helpful to those skilled in the art into one Step understands the present invention, but the invention is not limited in any way.
Fig. 1 is BEV remaining driving mileage forecasting system hardware configuration.BEV dynamical system in this example is by motor, motor control The composition such as device (Motor Control Unit, MCU) battery, battery management unit (BMU) and retarder processed.In order to realize residue Mileage forecast function is equipped with vehicle mounted GPS guidance system (GVNS), intelligent transportation system (ITS), geography information system on the BEV Unite (GIS) and weather forecast system (Weather Report System, WRS) etc..The function of use processing device is Required routing information is obtained from above system, and data acquisition, storage, cleaning and format are carried out to these information and are aligned Deng being fused into the different types of routing information of different-format can be by energy RDR prediction algorithm proposed by the present invention in energy (EMU) is run in administrative unit, and the effect of the unit is estimated BEV energy consumption, calculates remaining driving mileage and BEV energy Management.EMU is communicated by CAN line with BMU and MCU, coordinates and optimization BEV energy uses.
Fig. 2 is remaining driving mileage prediction algorithm framework, which includes three parts:
First part (Part1) RDR (remaining driving mileage) computation model, the function of the model are to calculate BEV energy to disappear Consumption rate and battery remaining power, and remaining driving mileage is calculated, remaining driving mileage prediction knot is finally shown according to traveling demand Fruit.The part include 3 submodels: energy consumption prediction model (auto model), remaining energy model (battery model) and remain Remaining mileage travelled display model.
Second part (Part2) parameter estimation model, the department pattern be to driving parameters needed for RDR computation model, Such as complete vehicle quality, road gradient and atmospheric density are estimated.It include the estimation model of above-mentioned parameter in the department pattern.
Part III (Part3) speed prediction model, also known as operating condition prediction model.Speed is parameter estimation model and RDR The input of prediction model.Speed prediction model is based on Markov according to Future Path information and driver history running data Random theory generates Future Path and predicts speed.
The driving parameters for prediction speed and the parameter estimation model estimation that speed prediction model obtains are input to RDR to calculate In model, the predicted value of RDR is finally obtained, and is shown on the dash panel, accurate RDR prediction can be effectively relieved driver's " mileage anxiety ".
Successively introduce above-mentioned three parts model by way of example below.
Part 1RDR computation model
(1) energy consumption prediction model (auto model)
Target vehicle in this example is a small-sized pure electric car, kerb weight 1060kg, max. speed 130km/h, battery capacity 91Ah, under the conditions of 69km/h drives at a constant speed, continual mileage is up to 228km.The bassinet structure is such as Shown in attached drawing 1, in vehicle travel process, energy consumption can be divided into three parts: first part is motor and transmission system Energy loss, as shown in figure 1 shown in A-B point；Second part is the energy for overcoming running resistance to consume, as shown in figure 1 shown in C point；The Three parts are auxiliary system energy consumption, as shown in figure 1 shown in D point.In addition, it is also necessary to remove the energy of regeneration brake system recycling.It adopts Energy consumption prediction model is established with reverse modeling method, i.e. the input of model is speed, is exported as cell output Pbat(W), i.e.,
Wherein, FwIt (N) is Automobile drive power, as shown in the automobile stress balance figure in Fig. 6, with automobile running resistance phase Deng i.e. Fw=Fr+Faero+Fg+Fm；V (m/s) is Vehicle Speed, is obtained by the speed prediction model of Part3；Ppt_loss(W) For BEV transmission system wasted power；PauxIt (W) is electric accessory horsepower.
Rolling resistance Fr(N) it can be calculated by following formula
Fr=frmvgcos(αslop) (1.2)
Wherein frIt for coefficient of rolling resistance, is influenced by environment temperature and road surface types, which will be by the rolling in Part2 Dynamic resistance coefficient estimate model obtains；Mv(kg) it is BEV complete vehicle quality, is obtained by the complete vehicle quality appraising model in Part2；g (m/s2) it is acceleration of gravity, take 9.81m/s2；αslop(rad) it is road gradient, is obtained by the road gradient appraising model of Part2 It arrives.
Air drag Faero(N) it is
Wherein ρ (kg/m3) is atmospheric density, is taken as 1.29kg/m3；Af(m2) be automobile front face area, target in this example The front face area of vehicle is 1.97m2；CdIt is coefficient of air resistance, is taken as 0.3 in this example；Vwin(m/s) it is wind in driving direction Speed is obtained by weather forecast system.
Grade resistance Fg(N) it is
Fg=mvgsin(αslop) (1.4)
Acceleration resistance Fm(N) it is
Here Jw(kg·m2) it is vehicle wheel rotation inertia, it is 0.75kgm2；Jm(kg·m2) it is motor rotary inertia, be 0.0384kgm2；R (m) is tire radius, is 0.278m；igIt is transmission ratio, is 8.654；dv/dt(m/s2) it is longitudinal Pickup has speed differential to obtain, and speed is obtained by the speed prediction model of Part3.
System loss power Ppt_loss(W) it can be tested and be measured by dynamometer machine, value is output power of motor Pelec(W) and turn Drum mechanical output Pmec(W) difference
Ppt_loss=Pelec-Pmec (1.6)
Fig. 4 is target vehicle different rotating speeds lower transmission system wasted power and power input to machine relationship trial curve.For Simplified calculating, using fit equation is described transmission system wasted power.Using Matlab parameter Estimation tool box pair The trial curve of Fig. 4 is fitted, and obtains fitting formula and optimization obtains fitting parameter.Due to drive mode and regenerative braking mould The fitting formula structure of formula is different, is fitted so empirical equation is respectively adopted, i.e.,
Wherein, motor torque Tm(Nm) it is
Tm=Fwr/ig (1.8)
Motor speedFor
PcIt (W) is motor idling loss power, empirical equation is
Pc=c1·v3+c2·v2+c3·v+c4 (1.10)
Wherein, ai(i=1~4), bi(i=1~2), ci(i=1~4) are fitting coefficient.
Target vehicle transmission system wasted power fitting formula is in this example
In the mode of giving it the gun
In braking mode
Wherein:
Pc=0.06v3-4.85v2+116.93v+170 (1.13)
Electric attachment energy consumption Paux(W) there is very big randomness, be averaged in this example using attachment electric under a variety of state of cyclic operation Energy consumption is as electric attachment energy consumption, i.e.,
Paux=Paux_avg (1.14)
Wherein, Paux_avg(W) it is accessory system average energy consumption electric under a variety of state of cyclic operation, is taken as 210W.
Finally obtain BEV energy expenditure rate eavg(kW/km), i.e.,
Wherein Sr(m) it is path length, is obtained from navigation system.
(2) remaining energy model (battery model)
Since battery is complicated electro-chemical systems, there are great differences for meeting under different operating conditions for battery remaining power. In remaining energy model, the present invention considers cell health state (state of health, SoH) and battery temperature The influence to battery remaining power such as degree.Battery remaining power Erue(kWh) it can be calculated by following formula
Erue=Q0·SoH·CtempUt,nom·(SOC-SOCend,nom) (1.16)
Wherein, Q0(Ah) it is new battery rated capacity, is taken as 91Ah；CtempFor battery temperature correction factor, by battery behavior Test determines；Ut,nomIt (V) is battery volume terminal voltage；SOCEnd, nomFor minimum battery discharge SOC；SoH is cell health state, It is defined as
Wherein, QbatIt (Ah) is present battery rated capacity.SoH is related with battery charging and discharging cycle-index, can be tried by battery The relationship of determining SoH Yu charge and discharge cycles number are tested, and obtains the SoH of present battery from battery management system.
Estimate that battery open circuit voltage is using battery SOC of the simple internal resistance of cell model to formula (1.16)
Voc=Vout+IR (1.18)
Wherein, VoutIt (V) is cell output voltage；I (A) is cell output current；R (Ω) is the internal resistance of cell.Battery open circuit The relation curve of voltage and SOC determines by battery testing, as shown in Figure 5.Relationship between internal resistance of cell R and discharge current I can It is tested and is determined by battery charging and discharging, fitting formula is
R=d1|I|3+d2|I|2+d3|I|+d4 (1.19)
Wherein, di(i=1~4) are that fitting coefficient is optimized according to trial curve by Matlab parameter Estimation tool box It arrives, i.e.,
R=-3.84 × 10-7|I|3+2.04×10-5|I|2-3.7×10-3|I|+0.41 (1.20)
Cell output Pbat(W) it is
Pbat=VoutI=VocI-I2R (1.21)
SOC is estimated using ampere-hour method, i.e.,
(3) remaining driving mileage display model
It calculates separately to obtain energy expenditure rate e from energy consumption prediction model and remaining energy modelavgAnd remaining battery ENERGY ErueAfterwards, current time t can be calculated by following formula2Remaining driving mileage RDRcal(km), i.e.,
The calculation method of another remaining driving mileage is
Wherein, RDRcal(t1) it is in past t1The RDR prediction result that the use formula (1.23) at moment is calculated；ΔLcum (t1,t2) it is from t1To t2The actual range that moment runs over is integrated to obtain by actual vehicle speed.
The RDR obtained by formula (1.23)calThe energy state and vehicle energy of reaction current vehicle that can be strictly according to the facts consume feelings Condition.It can make quick response to the variation of driving test cycle.However, in operating condition suddenly change, using this side The RDR result of method prediction will appear biggish jump, and this violent variation can cause the anxiety influence driving experience of driver. The RDR being calculated by formula (1.24)cumIt is continuous gentle variation, still, it can not react operating condition variation to the shadow of RDR It rings, biggish error will occur in the final result of prediction.For the in summary prediction technique advantage of two kinds of RDR and it is overcome to lack Point, the RDR prediction result RDR for finally showingdis(km) it can be calculated by following formula
RDRdis(t2)=wdisRDRcal(t2)+(1-wdis)RDRcum(t2) (1.25)
Wherein, wdisFor weight coefficient, value range is [0,1].Designer can show demand choosing according to specific RDR Select the value of the coefficient.
2 parameter estimation model of Part
In the RDR computation model of Part1, some running car parameters, such as coefficient of rolling resistance, road gradient and vehicle Quality etc. is calculated or estimated, and this part illustrates calculating or the estimation model of above-mentioned parameter.
(1) coefficient of rolling resistance initial estimate model
In order to calculate rolling resistance Fr(N) (formula (1.2)) are needed to coefficient of rolling resistance frIt is estimated, frWith environment temperature Degree, tyre temperature, speed and road surface types etc. are related.F of the inventionrThere are two appraising models: frInitial estimate model and dynamic State estimates model.Initial estimate model is used for frRough estimate, and provide initial value for dynamic estimation model.This evaluation method Calculation amount is small, can also be separately as f under conditions of required precision is not highrEstimation.Studies have shown that tyre temperature and vehicle Speed is to frInfluence be much smaller than the influence of road surface types and environment temperature to it.Therefore, in this example only consider environment temperature and Two factors of road surface types.Under the conditions of different road surfaces and temperature, experiment (Coast down is slided using target vehicle Tests), coefficient of rolling resistance, environment temperature and road surface types relationship trial curve are obtained.Coefficient of rolling resistance initial value fr0Fitting Formula is
Wherein, ei(i=1~3) are fitting coefficient, kiFor road surface types correction factor.
In this example, it carries out sliding experiment on 2~28 DEG C of temperature range, 5 kinds of different types of road surfaces, be fitted frExpression formula is
Wherein, super expressway k1=1；Smooth urban road k2=1.05；Smooth backroad k3=1.15；Coarse rural area Road k4=1.35；Belgium (road surface) road k5=1.40.
(2) road gradient computation model
Under the premise of known to the path, road can be calculated by GIS-Geographic Information System (GIS) and the path GPS longitude and latitude Face gradient aslop(rad), i.e.,
Wherein, Δ h (m) is the difference in height between two continuous measurement points.The altitude information of Future Path is obtained by generalized information system , such as SRTM (Shuttle Radar Topography Mission) system that the generalized information system in this example is NASA, road surface is high 30m is divided between journey sampled point.
(3) complete vehicle quality and coefficient of rolling resistance dynamic estimation model
BEV complete vehicle quality mvChange with the difference for loading number of occupants and cargo, in BEV energy consumption and remaining traveling Journey generates large effect.But complete vehicle quality is difficult directly to measure when vehicle travels, therefore, it is necessary to whole to when driving Vehicle quality is estimated.Meanwhile coefficient of rolling resistance can also change with tyre temperature and pavement conditions, rolling resistance above-mentioned Force coefficient initial estimate model is simply possible to use in precision of prediction occasion of less demanding.
Therefore, in order to accurately estimate the complete vehicle quality and coefficient of rolling resistance when vehicle driving, the present invention uses a kind of base In the m of recursive least-squares (Recursive Least-Squares, RLS) algorithm for estimatingvAnd frDynamic estimation model.
RLS algorithm for estimating is based on longitudinal vehicle dynamic model, in vehicle travel process, output power of motor Pm(W) For
Wherein, FfricIt (N) is the transmission system frictional force at wheel, which test can be freely rotated by wheel and obtained, this In example, the F of target vehiclefricValue is 15N.
Formula (2.4) are unfolded and the standard type for Linear Estimation of being write as is
Wherein,
Selection classics RLS method keeps following formula minimum
Its recursive solution is
Wherein
The initial value of P is set as 1, and signal sampling frequencies 25Hz, complete vehicle quality initial value is set as 1250kg, rolling resistance system Number initial value is determined by formula (2.1).RLS evaluation method need the regular hour could stable convergence, it is therefore, not converged in predicted value Before, RDR prediction model uses the initial value of complete vehicle quality and coefficient of rolling resistance.
3 speed prediction model of Part
Speed-time history curve is also known as driving pattern operating condition, abbreviation operating condition.It is the input of RDR prediction model, It is one of BEV energy consumption and the most important influence factor of RDR.Speed-time history is a typical markoff process.Cause This, is a discrete Markov Chain (Markov Chain), so-called discrete Markov Chain after speed-time history is sampled Refer to the sequence of random variables X with markoff process feature1,X2,X3,...Xn..., i.e., in given current state, it with Past state is conditional sampling, is expressed as
Since speed-time history is Markov Chain, it can be based on Markov random theory to speed history Data are counted and generate a random speed-time history.In the sufficiently long time or run enough numbers Under the premise of, the speed curves and history speed curves of generation are with uniformity on statistical nature.Discrete Markov Chain generates Prediction speed maintains randomness, and is able to reflect the driving style of driver.But the speed that this method generates has Very big randomness, when number of run is less, prediction speed generated and actual measurement speed can have very big error.Cause This, the present invention proposes a kind of random speed prediction method based on routing information, obtains following path by GPS, ITS and believes Breath, and Markov approach is combined, generate a kind of controllable random speed.Its advantage is that any one random speed generated Statistical nature can be close with actual measurement speed statistical nature, and maintains the randomness for generating speed.
Fig. 6 speed generating algorithm flow chart.Speed generating algorithm includes " generating transition probability matrix (Transition Probability Matrices, TPMs) " and " generating prediction speed " two parts.First part is mainly according to driver's History vehicle speed data and routing information generate transition probability matrix of the driver on different road surfaces.Second part is to be based on Following routing information combination TPMs generates prediction speed.
(1) transition probability matrix is generated
The acquisition of step 1 floor data is extracted with routing information
The acquisition of history vehicle speed data is divided into two stages: the acquisition of real vehicle working condition tests data and user's real-time working condition data Acquisition.
In the first stage, vehicle factor or system manufacturer should use matching sample car to carry out vehicle road test, test vehicle GPS, electronic map, GIS and CAN data collection system should be equipped with.The data of required acquisition have the speed time (v-t) to go through (path length, road type, signal location and traffic lights become for number of passes evidence, vehicle driving trace (GPS coordinate), road information Change the time, angular position and knuckle radius, deceleration strip position, traffic flow data etc.), (steering wheel turns driver's driving data Angle, pedal opening, gear etc.) and vehicle operation data (battery SOC, battery temperature, motor torque, battery terminal voltage, electricity Pond end electric current, electric attachment status and consumption power etc.).Actual road test should be driven sample car not by the driver of different driving styles It is completed on the road of same type, and it is enough to test mileage travelled.The test data of the phase acquisition is mainly used for RDR calculating The basic TPMs database of the initial calibration and generation of model and parameter estimation model.
In order to generate the TPMs of different driving style drivers, need to evaluate driving style and classified.Driver Driving style index J on certain type road id(i) it is
Jd(i)=w1·eavg(i)+w2vm(i)+w3vmax(i)+w4aam(i)+w5abm(i) (3.2)
Wherein, eavgThe average energy consumption rate for being the driver on the type road, (kW/km)；vmFor average speed, (km/h)；vmaxFor maximum speed, (km/h)；aamFor average acceleration, (m/s2)；；abmFor average retardation rate (absolute value), (m/ s2)；w1~5For weight coefficient.
After acquiring the sample car road data of several drivers, each driver is calculated all types of by formula (3.2) J on roadd(i) and clustering is carried out.The road data of same class will merge, as such driver Vehicle road test data.J at cluster centredIt (i) is quasi-representative driving style index Jdnor(i).It, will in this example Driving style is divided into three classes: plain edition Jdnor1(i), radical type Jdnor2(i) and mild Jdnor3(i).Relative to plain edition, swash Into type driver under identical driving environment, energy consumption, average speed, max. speed and average acceleration-deceleration with higher, Mild is then opposite.
In second stage, there is still a need for the driving data for collecting user, specific data for the prediction of BEV energy consumption and management system It is essentially identical with the first stage.User's driving data of the phase acquisition is mainly used for RDR computation model and parameter estimation model On-line calibration and generate the distinctive TPMs database of the user.
1 road type of table and operating condition segmentation method
In this example, working condition tests data are obtained using first stage actual road test mode.In certain state typical case's public way On, actual measurement actual road test is carried out using target vehicle, floor data total kilometrage is 600km, data processing is carried out to floor data, Such as filtering, dissimilarity rejecting and alignment of data etc..And data are classified according to road type, to the speed of same link type Data are analyzed, and are carried out data fitting after obtaining the distribution of the speed frequency, are obtained " maximum " the speed v of the type roadmax(packet Containing the vehicle speed value at 80% vehicle speed data), " minimum " speed vmin(vehicle speed value comprising 20% vehicle speed data) and " average " vehicle Fast vnom(vehicle speed value comprising 50% vehicle speed data), (note that " maximum ", " minimum " and " average " here and ordinary meaning are not Together, they are distributed from the speed frequency) as shown in table 1.State's road type is divided into " city ", " outskirts of a town " and " high speed " by this example Three classes, and " city " road is subdivided into " residential quarter ", " three classes ", " two classes " and " trunk roads ".
Fig. 7 is path and the routing information schematic diagram for carrying out actual road test in certain city using target vehicle.It is shown in figure The path of this test is shown, which includes " residential quarter ", " three classes " and " major trunk roads " three kinds of urban road types, from electronics The type and length of the available above-mentioned road of map.Path corner (+) and the position traffic lights () are had also shown in figure, with And current wind direction etc..By above-mentioned routing information with certain in the test speed Data Integration on the road together with, such as Fig. 8 institute Show.The max speed v of three kinds of road types is also shown in figuremax, minimum speed vminWith average speed vnom。
Step 2 divides operating condition section according to road type
From figure 8, it is seen that driving cycle (speed) was made of operating condition segment.In the present invention, operating condition segment is fixed Justice is 2 " minimum " speed vminBetween speed course.Whole operating condition is divided into operating condition section according to this definition, method is Second dervative is asked to speed course, the point that derivative is zero is speed course point of inflexion on a curve, and then speed is less than most in inflection point Small speed vminBe segment discontinuous point vb, i.e.,
The segment discontinuous point of Fig. 8 operating condition is as shown in " * " number in figure.Whole measurement condition is divided into number according to segment discontinuous point A operating condition section.And according to road surface types locating for operating condition section, operating condition section is classified.Fig. 9 is the operating condition piece of " two classes " road in Fig. 8 Section.
Step 3 segmentation accelerates and the decelerating phase
Each operating condition section is made of a boost phase and a decelerating phase.Sorted operating condition segment also needs Continue to be divided into boost phase and decelerating phase and calculate acceleration section distance than.Its stage discontinuous point is the maximum of each operating condition section It is worth point, as shown in " * " in Fig. 9.Figure 10 is by " two classes " road condition fragment segmentation of Fig. 9 into boost phase and deceleration rank Section, and cluster in a figure.Accelerating sections distance ratio rdaIt is defined as
Wherein, n is operating condition section number；Sda(i) (km) is the length that i-th of operating condition section accelerates segment；Sd(i) (km) is the The length of i operating condition section.
The vehicle speed data gridding of step 4 stage
Step 1~3 are repeated, until obtaining enough test speed data, then by same driver on similar road Accelerating sections and braking section vehicle speed data concentrate in together, and to above-mentioned vehicle speed data again gridding and interpolation.In this example, Former vehicle speed data parasang be km, speed unit be km/s, in gridding by unit conversion be m and m/s.Figure 11 is to accelerate Stage vehicle speed data gridding schematic diagram.In this example, the sampling interval of distance is 1m, and the sampling interval of speed is 0.1m/s.Vehicle The method of fast data gridding is sampled according to distance interval 1m to stage vehicle speed data, if vehicle speed data is not in grid On point, need to carry out rounding to vehicle speed data.
Step 5 statistic behavior shifts number
Stage vehicle speed data after gridding is scanned, statistic behavior shifts number, as shown in figure 11.Firstly, generating TPMs table, the ordinate of table are current state speed, and abscissa is NextState speed.It is divided between the speed of scanning 0.1m/s, from minimum speed to max. speed successively scanning mode speed.Such as Figure 11, if the speed state of Current Scan is 5.0m/s has 3 transfering states: 5.1m/s, 5.2m/s and 5.3m/s in figure.Wherein 5.0m/s- > 5.1m/s and 5.3m/s transfer Number is 1,5.0m/s- > 5.2m/s and 5.2 transfer numbers are 2.The state not shifted, transfer number 0.By above-mentioned transfer Number successively fills TPMs table.It repeats the above steps, complete the scanning of all stage vehicle speed datas and obtains stateful turn of institute It moves number (frequency).
Step 6 generates transition probability matrix
Transition probability PijIt is calculated by following formula
Wherein, nijFor from viTo vjState shift the frequency, s be total transfering state number.
All transition probability P in TPMs table are calculated according to formula (3.4)ijThe TPMs that v-v form can be obtained, by acceleration A=Δ v/ Δ t, and Δ t=1s, obtain a=Δ v, the TPMs of v-v form can be converted to the TPMs of v-a form.Figure 12 is city " two classes " road accelerates and decelerating phase TPMs.The depth of gray scale represents the size of transition probability in figure.
It repeats the above steps, generates different driving styles (plain edition, radical type and mild), adding in different kinds of roads Speed and decelerating phase TPMs.And these TPMs classification storages into database and are indexed.
(2) prediction speed is generated
The process of prediction speed is generated as shown in the right side Fig. 6, which is substantially the inverse process that TPMs is generated.Specific step It is rapid as follows:
Step 1 obtains Future Path coordinate and routing information
After driver inputs destination in onboard navigation system, system obtains non-incoming road from GPS and electronic map Diameter coordinate (GPS coordinate) and routing information (path length, road type, signal location and traffic lights conversion time, corner position It sets and knuckle radius, deceleration strip position, traffic flow data etc.).System handles above-mentioned data, rejecting dissimilarity, and according to Resampling is carried out according to driving path length.
Step 2, which generates, refers to operating condition
A kind of mode operating condition is generated according to path coordinate and routing information, referred to as refers to operating condition.Below with shown in Fig. 7 For certain city path, illustrate the generation method of mode operating condition.Path in Fig. 7 includes road surface types in 3, and path is saved by path Point (traffic lights, corner and deceleration strip etc.) is divided into multiple sections.Generally, driver may subtract in passage path node Speed or parking, then further accelerate traveling.Therefore, it may be wrapped comprising an acceleration and decelerating phase between 2 nodes Containing an operating condition section.Road type, the latitude lat of node and longitude lon can be obtained from electronic map and navigation system.
A, the distance L between two node of BABIt can be calculated by node coordinate, i.e.,
In formula, R (m) is earth radius, latAAnd latBThe latitude value of respectively A point and B point, lonAAnd lonBRespectively A The longitude of point and B point.
When the corner radius on path is larger, or at the parting of the ways on can exist there are when traffic lights, at this it is multiple Node.At this point, multiple nodes should be merged into an aggregators.As the distance L between 2 nodesAB<LminWhen, choose 2 nodes Position of the middle position as aggregators.
When node is only traffic lights (vehicle straight trip), due to there is certain probability to be shown as green light, driver will be direct Pass through the node.By ITS system, the available traffic lights traffic lights duration, and calculate green light probability of occurrence, i.e.,
One 0~1 random number n is generated by Matalb random functionlgIf nlg< plg, then the traffic light node will be taken Disappear and (be considered green light), otherwise, the traffic light node will be retained.
Above-mentioned node is arranged according to position, and the average speed of node the type of same link type is connected Form " with reference to operating condition " of the Future Path, as shown in figure 13.With reference to display of regime road type, length, average vehicle Speed and node location etc..It is " skeleton " for the prediction operating condition to be generated, and is an operating condition segment between 2 nodes.
Step 3TPMs type matching
After generation " mode operating condition ", system identifies the driving style of current vehicle driver and matches corresponding The TPMs of type.
When mileage travelled is less than a certain threshold value S to vehicle on certain road typer1When (such as 50km), system can not be obtained enough Driver's running data, at this point, system using " plain edition " driving style TPMs.
When mileage travelled is more than Sr1When, system can identify the driving style of the driver.It is counted according to formula (3.2) Calculate the driving style index J being driven on road type id(i), and calculate and Jdnor1(i)、Jdnor2(i) and Jdnor3(i) poor Absolute value.The smallest driving style type of absolute value of the difference is the driving style of the driver.Then use the driving style TPMs of the TPMs as the driver on the type road.It repeats the above steps, identifies the driving on all types of roads The driving style of member, and match TPMs.
When mileage travelled of the vehicle on the type road is more than Sr2The driver can be generated in (such as 100km), system TPM on such road, generation method are same as above.System can expand TPMs, and use the distinctive TPMs of the driver.
Therefore, driver history floor data becomes after counting using driving style and road type as rope in the present invention The TPMs drawn, amount of storage is small, convenience of calculation, and can real-time update.Increase with mileage travelled, probability transfer matrix drives driver Sailing behavioural characteristic representativeness will enhance, but memory capacity increases less；When the road type and driving style TPMs of storage increase When, forecasting accuracy will greatly improve, and amount of storage only slightly increases, and adapt to onboard system requirement.
Step 4 generates operating condition section
According to " referring to operating condition ", the operating condition section on all types of roads is sequentially generated.Below on Figure 13 " two classes " road Illustrate the algorithm that operating condition section generates for first operating condition section.Figure 14 is operating condition section generating algorithm schematic diagram.It is located at Figure 13 " two There are an operating condition section, the two nodes between first node A (corner) and second node B (corner) on class " road Distance LABIt is calculated by formula (3.5), and by result rounding to 1m.Such as L in Figure 14AB=10m；Accelerating sections distance is than by formula (3.3) Statistics obtains, if rda=0.6；Then accelerating segment length is 6m, and deceleration segment length is 4m.
If being divided into 1m between generating speed point, 0.1m/s is divided between velocity amplitude.The starting point speed v of the operating condition section0It is upper The end point speed of one operating condition section, v in this example0=5.0m/s.Then next meter of speed will be by the acceleration TPMs of the road type The transition probability of corresponding state point determines, in this example, speed 5.0m/s there are three state 5.1m/s, the 5.2m/s that can shift and 5.3m/s, transition probability 0.25,0.5 and 0.25.Using Matlab random number generation function Round () generate one 0~ Random number between 1 determines transfering state, i.e.,
As shown in figure 14, if the random number generated is 0.54, according to formula (3.7), NextState v1=5.2m/s.It repeats The step sequentially generates the speed point of the accelerating sections, until meeting the acceleration segment length.Then with the accelerating sections last A speed point vendAs the starting point of braking section, the deceleration TPMs of the type road is selected, according to (Figure 14) identical as accelerating sections Algorithm, generate braking section speed point.Until meeting deceleration segment length.
The integration of step 5 operating condition section and filtering
Step 4 is repeated, other operating condition section speeds are sequentially generated, until meeting the length in the type section.It generates Non-intersection speed needs progress smooth and resampling, right using Butterworth filter (Butterworth filter) in this example Speed is generated to carry out smoothly.
Step 6 generates prediction speed curves
It when road Change of types, needs to select corresponding TPMs, each non-intersection speed curve is sequentially generated, until pre- measuring car Until fast distance and Future Path equal length.Prediction speed curves in Figure 13 are the pre- measuring car generated on the city path Speed.
According to the mathematical model of remaining driving mileage prediction algorithm framework and three parts, using Matlab/Simulink Establish the electric car remaining driving mileage predictive simulation model based on routing information.With urban road operating condition shown in Fig. 7 into Row emulates and verifies the validity and precision of model.In emulation, the actual measurement row of 2 driving style difference drivers has been respectively adopted Data are sailed, and generate TPMs of the two drivers under the city operating condition.Figure 15 is 2 drivers on urban road Energy consumption actual measurement and prediction curve.It can be seen that driver 2 is higher than the energy consumption of driver 1, the two difference 8% or so, it was demonstrated that no Large effect can be generated to energy consumption with driving style.Energy consumption is predicted using model proposed in this paper, it can from Figure 15 To see, the prediction of energy consumption of 2 drivers can match with respective actual measurement energy consumption well, illustrate that the algorithm proposed can To adapt to influence of the different driving styles to energy consumption.Figure 16 is RDR reality and prediction curve of the driver 1 on urban road, The two can be good at coincideing.In order to verify RDR precision of forecasting model proposed in this paper, using endpoint relative error (TRE) and Root-mean-square value error (RMSE) is evaluated.
The definition of TRE is
Wherein, PtIt (measures the power consumption values of stroke end point in advance for energy consumption for prediction curve endpoint value, RDR is predicted Take the RDR value of starting point)；MtFor actual curve endpoint value.The value characterizes the accumulation prediction error of entire stroke.
The definition of RMSE is
Wherein, i is curve sampled point；N is sampled point total number；PiFor the predicted value at curve sampled point i；MiFor curve Measured value at sampled point i.The goodness of fit of the value characterization prediction curve and measured value.
Since the prediction curve that this method obtains is randomly generated, the prediction curve that each run obtains not fully phase Together.Therefore, Prediction program is run multiple times, calculates prediction error of the driver 1 on urban road, the results are shown in Table 2.It can be with See that energy consumption prediction TRE is 0.86%, RDR 1.1%, illustrates accumulated error very little.In addition, it is also smaller from RMSE error, in advance The goodness of fit for surveying curve and measured value is preferable.
Energy consumption and RDR predict error under 2 urban road operating condition of table

Claims (6)

1. a kind of pure electric automobile remaining mileage model prediction method based on routing information, which is characterized in that including following step It is rapid:
Step 1: establishing speed prediction model to generate Future Path prediction speed: being travelled to a certain number of driver histories Data are analyzed, extraction path information, generate the state transition probability matrix for meeting Characteristics of drivers' behavior；Based on non-incoming road The road information of diameter and corresponding state transition probability matrix generate the prediction speed controlled by future trajectory information；
Step 2: establishing parameter estimation model, the driving parameters for influencing automobile energy consumption and remaining driving mileage are estimated；
Step 3: establishing RDR computation model to predict vehicle remaining driving mileage: RDR computation model includes: energy consumption prediction mould Type, remaining energy model and remaining driving mileage display model；Energy consumption prediction model is obtained with speed prediction model Prediction speed and the driving parameters of parameter estimation model estimation calculate vehicle energy consumption rate as mode input；Residual energy Amount prediction model is for estimating Vehicular battery dump energy；Comprehensive vehicle energy expenditure rate and the i.e. predictable vehicle of battery remaining power Remaining driving mileage, and shown by remaining driving mileage display model.
2. a kind of pure electric automobile remaining mileage model prediction method based on routing information as described in claim 1, special Sign is, the step 1 establish speed prediction model to generate Future Path prediction speed specifically includes the following steps:
1) according to the history vehicle speed data and routing information of driver, transition probability square of the driver on different road surfaces is generated Battle array:
1.1) floor data acquisition is extracted with routing information, carries out the acquisition of real vehicle floor data respectively and driver's floor data is adopted Collection, evaluates and classifies to driver's driving style, driving style index J of the driver on certain type road id(i) are as follows:
Jd(i)=w1·eavg(i)+w2vm(i)+w3vmax(i)+w4aam(i)+w5abm(i)
Wherein, eavgThe average energy consumption rate for being the driver on the type road, (kW/km)；vmFor average speed, (km/h)； vmaxFor maximum speed, (km/h)；aamFor average acceleration, (m/s2)；abmFor average retardation rate (absolute value), (m/s2)；w1~5 For weight coefficient；
1.2) driving cycle section is divided according to road type；
1.3) sorted operating condition segment continue to be divided into boost phase and decelerating phase and calculate acceleration section distance than；
1.4) stage floor data gridding: acceleration of the same driver on similar road is connect into section and deceleration connects a section speed number According to concentrating in together, and to above-mentioned vehicle speed data again gridding and interpolation；
1.5) the stage floor data after gridding is scanned, statistic behavior shifts number；
1.6) transition probability matrix is generated；
2) prediction speed is generated based on following routing information combination TPMs:
2.1) vehicle Future Path coordinate and routing information are obtained from onboard system；
2.2) it is generated according to path coordinate and routing information and refers to operating condition, with reference to display of regime road type, length, be averaged Speed and node location are an operating condition segment between 2 nodes；
2.3) after generating with reference to operating condition, system identifies the driving style of current vehicle driver and matches respective type Transition probability matrix；
2.4) according to operating condition is referred to, the operating condition section on all types of roads is sequentially generated；
2.5) integration of operating condition section and filtering are carried out；
2.6) prediction speed curves are generated: when road Change of types, selecting corresponding transition probability matrix, sequentially generates each road Section speed curves, until predicting speed distance with Future Path equal length.
3. a kind of pure electric automobile remaining mileage model prediction method based on routing information as described in claim 1, special Sign is that the step 2 parameter estimation model carries out estimation to driving parameters and specifically includes:
1) coefficient of rolling resistance initial estimate:
Coefficient of rolling resistance initial value fr0Fitting formula is
Wherein, ei(i=1~3) are fitting coefficient, kiFor road surface types correction factor；
2) road gradient calculates:
Road gradient a can be calculated by GIS-Geographic Information System and the path GPS longitude and latitudeslop(rad), i.e.,
Wherein, Δ h (m) is the difference in height between two continuous measurement points；
3) complete vehicle quality and coefficient of rolling resistance dynamic estimation:
Based on Recursive Least Squares Estimation algorithm dynamic estimation complete vehicle quality mvWith coefficient of rolling resistance fr,
In vehicle travel process, output power of motor Pm(W) it is
Wherein, FfricIt (N) is the transmission system frictional force at wheel；
It is by the standard type that above formula is write as Linear Estimation
Wherein,
Keep following formula minimum:
Its recursive solution is
Wherein,
4. a kind of pure electric automobile remaining mileage model prediction method based on routing information as described in claim 1, special Sign is, the establishment process of energy consumption prediction model in the step 3 are as follows:
Energy consumption prediction model is established using reverse modeling method, the input of model is speed, is exported as cell output Pbat (W), it may be assumed that
Wherein, v (m/s) is Vehicle Speed, is obtained by the speed prediction model；
Rolling resistance Fr(N) it is calculated by following formula:
Fr=frmvgcos(αslop)
In formula, frFor coefficient of rolling resistance, MvIt (kg) is complete vehicle quality, αslop(rad) it is road gradient, is estimated by the parameter Meter model is calculated；g(m/s2) it is acceleration of gravity；
Air drag Faero(N) it is calculated by following formula:
In formula, ρ (kg/m3) is atmospheric density；Af(m2) be automobile front face area；CdIt is coefficient of air resistance；Vwin(m/s) it is Wind speed in driving direction；
Grade resistance Fg(N) it is calculated by following formula:
Fg=mvgsin(αslop)
Acceleration resistance Fm(N) it is calculated by following formula:
In formula, Jw(kg·m2) it is vehicle wheel rotation inertia；Jm(kg·m2) it is motor rotary inertia；R (m) is tire radius；igIt is Transmission ratio；dv/dt(m/s2) it is longitudinal pickup；
Ppt_lossIt (W) is vehicle drive system wasted power；
PauxIt (W) is electric accessory horsepower.
5. a kind of pure electric automobile remaining mileage model prediction method based on routing information as described in claim 1, special Sign is, in the step 3 can remaining energy model establishment process are as follows:
Battery remaining power Erue(kWh) it can be calculated by following formula:
Erue=Q0·SoH·CtempUt,nom·(SOC-SOCend,nom)
Wherein, Q0It (Ah) is new battery rated capacity；CtempFor battery temperature correction factor；Ut,nomIt (V) is the specified end electricity of battery Pressure；SOCEnd, nomFor minimum battery discharge SOC；SoH is cell health state, is defined as:
In formula, QbatIt (Ah) is present battery rated capacity；
SOC is estimated using ampere-hour method:
In formula, I (A) is cell output current.
6. a kind of pure electric automobile remaining mileage model prediction method based on routing information as described in claim 1, special Sign is, the calculation method of remaining driving mileage in the step 3 are as follows:
It calculates separately to obtain energy expenditure rate e from the energy consumption prediction model and the remaining energy modelavgAnd battery Dump energy ErueAfterwards, current time t can be calculated by following formula2Remaining driving mileage RDRcal(km), i.e.,
Further calculated by following formula:
Wherein, RDRcal(t1) it is in past t1The RDR prediction result at moment；ΔLcum(t1,t2) it is from t1To t2Moment runs over Actual range, integrate to obtain by actual vehicle speed；
The RDR prediction result RDR finally showndis(km) it can be calculated by following formula:
RDRdis(t2)=wdisRDRcal(t2)+(1-wdis)RDRcum(t2)
Wherein, wdisFor weight coefficient, value range is [0,1].
CN201910018307.7A 2019-01-09 2019-01-09 Pure electric vehicle remaining mileage model prediction method based on path information Expired - Fee Related CN109733248B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910018307.7A CN109733248B (en) 2019-01-09 2019-01-09 Pure electric vehicle remaining mileage model prediction method based on path information

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910018307.7A CN109733248B (en) 2019-01-09 2019-01-09 Pure electric vehicle remaining mileage model prediction method based on path information

Publications (2)

Publication Number Publication Date
CN109733248A true CN109733248A (en) 2019-05-10
CN109733248B CN109733248B (en) 2020-07-24

Family

ID=66363985

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910018307.7A Expired - Fee Related CN109733248B (en) 2019-01-09 2019-01-09 Pure electric vehicle remaining mileage model prediction method based on path information

Country Status (1)

Country Link
CN (1) CN109733248B (en)

Cited By (55)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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
CN110222906A (en) * 2019-06-17 2019-09-10 北京嘀嘀无限科技发展有限公司 Electric vehicle energy consumption prediction technique, computer readable storage medium and electronic equipment
CN110281812A (en) * 2019-06-27 2019-09-27 一汽解放汽车有限公司 A kind of course continuation mileage estimating system based on operating mode's switch
CN110370933A (en) * 2019-07-10 2019-10-25 一汽解放汽车有限公司 A kind of course continuation mileage estimating system based on driving style identification
CN110569550A (en) * 2019-08-09 2019-12-13 北汽福田汽车股份有限公司 method and system for estimating endurance mileage and automobile
CN110610260A (en) * 2019-08-21 2019-12-24 南京航空航天大学 Driving energy consumption prediction system, method, storage medium and equipment
CN110716527A (en) * 2019-09-09 2020-01-21 北京航空航天大学 Vehicle energy consumption analysis method and analysis system based on kinematic segments
CN110962691A (en) * 2019-11-19 2020-04-07 北京新能源汽车技术创新中心有限公司 Power battery thermal management control system
CN111044070A (en) * 2020-01-02 2020-04-21 北京理工大学 Vehicle navigation method and system based on energy consumption calculation
CN111038334A (en) * 2019-12-31 2020-04-21 华人运通(江苏)技术有限公司 Method and device for predicting driving range of electric automobile
CN111216730A (en) * 2020-01-15 2020-06-02 山东理工大学 Method, device, storage medium and equipment for estimating remaining driving range of electric automobile
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
CN111301426A (en) * 2020-03-13 2020-06-19 南通大学 Method for predicting energy consumption in future driving process based on GRU network model
CN111469785A (en) * 2020-04-09 2020-07-31 深圳市鹏巨术信息技术有限公司 Data processing method and device, electronic equipment and storage medium
CN111483322A (en) * 2020-04-27 2020-08-04 中国第一汽车股份有限公司 Method and device for determining remaining mileage of vehicle and vehicle
CN111546941A (en) * 2020-04-27 2020-08-18 中国第一汽车股份有限公司 Method and device for determining remaining mileage of vehicle, vehicle and storage medium
CN111563976A (en) * 2020-04-26 2020-08-21 浙江吉利新能源商用车集团有限公司 Method and device for determining remaining driving mileage of commercial vehicle
CN111731151A (en) * 2020-05-06 2020-10-02 华人运通(江苏)技术有限公司 Endurance mileage display method and device, vehicle and storage medium
CN111753377A (en) * 2020-07-06 2020-10-09 吉林大学 Pure electric vehicle energy consumption optimal path planning method based on road information
CN111983480A (en) * 2020-08-19 2020-11-24 华晟（青岛）智能装备科技有限公司 AGV electric quantity state prediction method and system based on Mahalanobis process
CN112009308A (en) * 2019-05-31 2020-12-01 长城汽车股份有限公司 Electric vehicle endurance mileage calculation method and device
CN112035942A (en) * 2019-06-03 2020-12-04 上海汽车集团股份有限公司 Energy consumption simulation method and device based on driving behaviors
CN112046335A (en) * 2020-09-09 2020-12-08 北京航空航天大学 Method for calculating remaining driving mileage of electric automobile based on driving energy consumption model
CN112078431A (en) * 2020-08-19 2020-12-15 大众问问(北京)信息科技有限公司 Vehicle energy consumption prediction and energy supplement method and related equipment
CN112172600A (en) * 2019-07-01 2021-01-05 汉能移动能源控股集团有限公司 Monitoring system and method for electric vehicle
CN112200932A (en) * 2020-09-03 2021-01-08 北京蜂云科创信息技术有限公司 Method and equipment for evaluating energy consumption of heavy-duty diesel vehicle
CN112560186A (en) * 2020-10-15 2021-03-26 吉林大学 Method, device and equipment for predicting travel energy consumption of electric automobile and storage medium
CN112613680A (en) * 2020-12-29 2021-04-06 华人运通(江苏)技术有限公司 Endurance mileage estimation method, device, equipment and storage medium
CN112824196A (en) * 2019-11-19 2021-05-21 通用汽车环球科技运作有限责任公司 Driver model estimation, classification, and adaptation for range prediction
CN113095558A (en) * 2021-04-01 2021-07-09 天津大学 Iterative optimization multi-scale fusion vehicle speed prediction algorithm for intelligent networked automobile
CN113159435A (en) * 2021-04-29 2021-07-23 浙江吉利控股集团有限公司 Method and system for predicting remaining driving mileage of new energy vehicle
CN113406513A (en) * 2021-06-08 2021-09-17 北京理工大学 Voltage estimation method based on new energy automobile data interpolation
CN113424067A (en) * 2021-05-12 2021-09-21 华为技术有限公司 Method and device for estimating remaining battery capacity
CN113515794A (en) * 2021-06-04 2021-10-19 上海中科深江电动车辆有限公司 Road surface resistance energy consumption prediction evaluation method, device and processor influencing generated power of range extender and computer readable storage medium thereof
WO2021227086A1 (en) * 2020-05-15 2021-11-18 华为技术有限公司 Method and apparatus for acquiring vehicle rolling resistance coefficient
CN113744430A (en) * 2021-08-11 2021-12-03 佛山仙湖实验室 Method for predicting required power of automobile running based on road change
CN113815423A (en) * 2021-09-30 2021-12-21 华人运通(江苏)技术有限公司 Energy feedback control method and device of electric automobile and vehicle
CN113859054A (en) * 2021-11-11 2021-12-31 广东汉合汽车有限公司 Fuel cell vehicle control method, system, device, and medium
CN113895307A (en) * 2021-11-24 2022-01-07 中国第一汽车股份有限公司 Remaining mileage determination method and device, electric vehicle and medium
CN114043948A (en) * 2021-10-18 2022-02-15 岚图汽车科技有限公司 Vehicle energy consumption measuring and calculating method and device, electronic equipment and storage medium
CN114212004A (en) * 2021-12-30 2022-03-22 重庆长安新能源汽车科技有限公司 Estimation method for cruising mileage of fuel cell vehicle
CN114366466A (en) * 2021-12-10 2022-04-19 上海工程技术大学 Walking-replacing nursing robot integrating health information monitoring and prediction
US11326892B2 (en) * 2018-10-10 2022-05-10 Hyundai Motor Company Route guide apparatus and method for electric vehicle
CN114655074A (en) * 2021-11-16 2022-06-24 吉林大学 Electric automobile actual driving energy consumption estimation method based on Bayesian regression
CN114943485A (en) * 2022-07-21 2022-08-26 启东市航新实用技术研究所 New energy battery endurance mathematical model evaluation method based on Internet of vehicles
CN115171394A (en) * 2022-09-08 2022-10-11 南京智慧交通信息股份有限公司 Real-time safety monitoring system and method for new energy public transport vehicle
CN115195530A (en) * 2022-08-11 2022-10-18 深圳职业技术学院 Battery cooling management control system of electric automobile
CN115257407A (en) * 2022-08-16 2022-11-01 中联重科股份有限公司 Energy management method, terminal and computer storage medium for extended range electric vehicle
CN115782595A (en) * 2022-12-06 2023-03-14 东南大学 Electric bus instantaneous energy consumption estimation method based on energy recovery state
CN115952899A (en) * 2022-12-27 2023-04-11 大连理工大学 Electric bus energy consumption estimation method based on actual working condition extraction
CN116054638A (en) * 2023-02-03 2023-05-02 郑州大学 Quick-response permanent magnet synchronous motor control system for new energy automobile
WO2023098828A1 (en) * 2021-12-01 2023-06-08 北京罗克维尔斯科技有限公司 Vehicle speed combination method and apparatus, and electronic device and storage medium
CN116424154A (en) * 2023-03-06 2023-07-14 合众新能源汽车股份有限公司 Electric vehicle energy consumption estimation method, system, equipment and medium
CN117172031A (en) * 2023-10-27 2023-12-05 北京航空航天大学 Method for estimating available energy of battery system of aerocar based on vehicle speed planning
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

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103234544A (en) * 2013-04-27 2013-08-07 北京交通大学 Methods for building power consumption factor model and estimating following-up driving range of electric car
US20140188304A1 (en) * 2012-12-28 2014-07-03 International Business Machines Corporation Optimal electric vehicle battery recommendation system
WO2015094807A1 (en) * 2013-12-16 2015-06-25 Contour Hardening, Inc. System and method for control of an electric vehicle
CN106458049A (en) * 2014-03-24 2017-02-22 密执安州立大学董事会 Prediction of battery power requirements for electric vehicles
US20170144560A1 (en) * 2015-11-24 2017-05-25 Samsung Electronics Co., Ltd. Method and apparatus for battery management
WO2018061449A1 (en) * 2016-09-30 2018-04-05 日立オートモティブシステムズ株式会社 Battery control device, battery system, and vehicle
CN108806021A (en) * 2018-06-12 2018-11-13 重庆大学 Electric vehicle target road section energy consumption prediction technique based on physical model and roadway characteristic parameter

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140188304A1 (en) * 2012-12-28 2014-07-03 International Business Machines Corporation Optimal electric vehicle battery recommendation system
CN103234544A (en) * 2013-04-27 2013-08-07 北京交通大学 Methods for building power consumption factor model and estimating following-up driving range of electric car
WO2015094807A1 (en) * 2013-12-16 2015-06-25 Contour Hardening, Inc. System and method for control of an electric vehicle
CN106458049A (en) * 2014-03-24 2017-02-22 密执安州立大学董事会 Prediction of battery power requirements for electric vehicles
US20170144560A1 (en) * 2015-11-24 2017-05-25 Samsung Electronics Co., Ltd. Method and apparatus for battery management
WO2018061449A1 (en) * 2016-09-30 2018-04-05 日立オートモティブシステムズ株式会社 Battery control device, battery system, and vehicle
CN108806021A (en) * 2018-06-12 2018-11-13 重庆大学 Electric vehicle target road section energy consumption prediction technique based on physical model and roadway characteristic parameter

Cited By (84)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11326892B2 (en) * 2018-10-10 2022-05-10 Hyundai Motor Company Route guide apparatus and method for electric vehicle
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
CN112009308A (en) * 2019-05-31 2020-12-01 长城汽车股份有限公司 Electric vehicle endurance mileage calculation method and device
CN112035942A (en) * 2019-06-03 2020-12-04 上海汽车集团股份有限公司 Energy consumption simulation method and device based on driving behaviors
CN110222906A (en) * 2019-06-17 2019-09-10 北京嘀嘀无限科技发展有限公司 Electric vehicle energy consumption prediction technique, computer readable storage medium and electronic equipment
CN110281812A (en) * 2019-06-27 2019-09-27 一汽解放汽车有限公司 A kind of course continuation mileage estimating system based on operating mode's switch
CN112172600A (en) * 2019-07-01 2021-01-05 汉能移动能源控股集团有限公司 Monitoring system and method for electric vehicle
CN110370933A (en) * 2019-07-10 2019-10-25 一汽解放汽车有限公司 A kind of course continuation mileage estimating system based on driving style identification
CN110569550A (en) * 2019-08-09 2019-12-13 北汽福田汽车股份有限公司 method and system for estimating endurance mileage and automobile
CN110569550B (en) * 2019-08-09 2021-12-10 北汽福田汽车股份有限公司 Method and system for estimating endurance mileage and automobile
CN110610260B (en) * 2019-08-21 2023-04-18 南京航空航天大学 Driving energy consumption prediction system, method, storage medium and equipment
CN110610260A (en) * 2019-08-21 2019-12-24 南京航空航天大学 Driving energy consumption prediction system, method, storage medium and equipment
CN110716527A (en) * 2019-09-09 2020-01-21 北京航空航天大学 Vehicle energy consumption analysis method and analysis system based on kinematic segments
CN112824196A (en) * 2019-11-19 2021-05-21 通用汽车环球科技运作有限责任公司 Driver model estimation, classification, and adaptation for range prediction
CN110962691A (en) * 2019-11-19 2020-04-07 北京新能源汽车技术创新中心有限公司 Power battery thermal management control system
CN111038334A (en) * 2019-12-31 2020-04-21 华人运通(江苏)技术有限公司 Method and device for predicting driving range of electric automobile
CN111044070A (en) * 2020-01-02 2020-04-21 北京理工大学 Vehicle navigation method and system based on energy consumption calculation
CN111044070B (en) * 2020-01-02 2021-11-05 北京理工大学 Vehicle navigation method and system based on energy consumption calculation
CN111216730B (en) * 2020-01-15 2021-11-16 山东理工大学 Method, device, storage medium and equipment for estimating remaining driving range of electric automobile
CN111216730A (en) * 2020-01-15 2020-06-02 山东理工大学 Method, device, storage medium and equipment for estimating remaining driving range of electric automobile
CN111301172B (en) * 2020-02-12 2022-05-10 浙江吉利汽车研究院有限公司 Estimation method, device, equipment and storage medium of driving range
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
CN111301426A (en) * 2020-03-13 2020-06-19 南通大学 Method for predicting energy consumption in future driving process based on GRU network model
CN111469785A (en) * 2020-04-09 2020-07-31 深圳市鹏巨术信息技术有限公司 Data processing method and device, electronic equipment and storage medium
CN111469785B (en) * 2020-04-09 2022-04-15 深圳市鹏巨术信息技术有限公司 Data processing method and device, electronic equipment and storage medium
CN111563976A (en) * 2020-04-26 2020-08-21 浙江吉利新能源商用车集团有限公司 Method and device for determining remaining driving mileage of commercial vehicle
CN111483322A (en) * 2020-04-27 2020-08-04 中国第一汽车股份有限公司 Method and device for determining remaining mileage of vehicle and vehicle
CN111546941A (en) * 2020-04-27 2020-08-18 中国第一汽车股份有限公司 Method and device for determining remaining mileage of vehicle, vehicle and storage medium
CN111483322B (en) * 2020-04-27 2021-10-15 中国第一汽车股份有限公司 Method and device for determining remaining mileage of vehicle and vehicle
WO2021218313A1 (en) * 2020-04-27 2021-11-04 中国第一汽车股份有限公司 Method and device for determining remaining mileage of vehicle and vehicle
CN111731151A (en) * 2020-05-06 2020-10-02 华人运通(江苏)技术有限公司 Endurance mileage display method and device, vehicle and storage medium
CN111731151B (en) * 2020-05-06 2021-06-29 华人运通(江苏)技术有限公司 Endurance mileage display method and device, vehicle and storage medium
WO2021227086A1 (en) * 2020-05-15 2021-11-18 华为技术有限公司 Method and apparatus for acquiring vehicle rolling resistance coefficient
CN111753377A (en) * 2020-07-06 2020-10-09 吉林大学 Pure electric vehicle energy consumption optimal path planning method based on road information
CN111753377B (en) * 2020-07-06 2022-09-30 吉林大学 Pure electric vehicle energy consumption optimal path planning method based on road information
CN111983480A (en) * 2020-08-19 2020-11-24 华晟（青岛）智能装备科技有限公司 AGV electric quantity state prediction method and system based on Mahalanobis process
CN112078431A (en) * 2020-08-19 2020-12-15 大众问问(北京)信息科技有限公司 Vehicle energy consumption prediction and energy supplement method and related equipment
CN112200932A (en) * 2020-09-03 2021-01-08 北京蜂云科创信息技术有限公司 Method and equipment for evaluating energy consumption of heavy-duty diesel vehicle
CN112046335A (en) * 2020-09-09 2020-12-08 北京航空航天大学 Method for calculating remaining driving mileage of electric automobile based on driving energy consumption model
CN112560186A (en) * 2020-10-15 2021-03-26 吉林大学 Method, device and equipment for predicting travel energy consumption of electric automobile and storage medium
CN112613680B (en) * 2020-12-29 2023-12-05 华人运通(江苏)技术有限公司 Method, device, equipment and storage medium for estimating endurance mileage
CN112613680A (en) * 2020-12-29 2021-04-06 华人运通(江苏)技术有限公司 Endurance mileage estimation method, device, equipment and storage medium
CN113095558A (en) * 2021-04-01 2021-07-09 天津大学 Iterative optimization multi-scale fusion vehicle speed prediction algorithm for intelligent networked automobile
CN113095558B (en) * 2021-04-01 2022-05-24 天津大学 Iterative optimization multi-scale fusion vehicle speed prediction method for intelligent networked automobile
CN113159435A (en) * 2021-04-29 2021-07-23 浙江吉利控股集团有限公司 Method and system for predicting remaining driving mileage of new energy vehicle
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
CN113424067A (en) * 2021-05-12 2021-09-21 华为技术有限公司 Method and device for estimating remaining battery capacity
CN113515794B (en) * 2021-06-04 2023-04-07 上海中科深江电动车辆有限公司 Road surface resistance energy consumption prediction evaluation method, device and processor influencing generated power of range extender and computer readable storage medium thereof
CN113515794A (en) * 2021-06-04 2021-10-19 上海中科深江电动车辆有限公司 Road surface resistance energy consumption prediction evaluation method, device and processor influencing generated power of range extender and computer readable storage medium thereof
CN113406513A (en) * 2021-06-08 2021-09-17 北京理工大学 Voltage estimation method based on new energy automobile data interpolation
CN113406513B (en) * 2021-06-08 2022-11-15 北京理工大学 Voltage estimation method based on new energy automobile data interpolation
CN113744430A (en) * 2021-08-11 2021-12-03 佛山仙湖实验室 Method for predicting required power of automobile running based on road change
CN113744430B (en) * 2021-08-11 2023-06-06 佛山仙湖实验室 Automobile driving demand power prediction method based on road change
CN113815423A (en) * 2021-09-30 2021-12-21 华人运通(江苏)技术有限公司 Energy feedback control method and device of electric automobile and vehicle
CN113815423B (en) * 2021-09-30 2023-10-20 华人运通(江苏)技术有限公司 Energy feedback control method and device for electric automobile and vehicle
CN114043948A (en) * 2021-10-18 2022-02-15 岚图汽车科技有限公司 Vehicle energy consumption measuring and calculating method and device, electronic equipment and storage medium
CN114043948B (en) * 2021-10-18 2023-10-20 岚图汽车科技有限公司 Vehicle energy consumption measuring and calculating method and device, electronic equipment and storage medium
CN113859054A (en) * 2021-11-11 2021-12-31 广东汉合汽车有限公司 Fuel cell vehicle control method, system, device, and medium
CN113859054B (en) * 2021-11-11 2023-11-17 广东汉合汽车有限公司 Fuel cell vehicle control method, system, equipment and medium
CN114655074B (en) * 2021-11-16 2024-01-30 吉林大学 Electric vehicle actual running energy consumption estimation method based on Bayesian regression
CN114655074A (en) * 2021-11-16 2022-06-24 吉林大学 Electric automobile actual driving energy consumption estimation method based on Bayesian regression
CN113895307A (en) * 2021-11-24 2022-01-07 中国第一汽车股份有限公司 Remaining mileage determination method and device, electric vehicle and medium
WO2023098828A1 (en) * 2021-12-01 2023-06-08 北京罗克维尔斯科技有限公司 Vehicle speed combination method and apparatus, and electronic device and storage medium
CN114366466B (en) * 2021-12-10 2023-11-24 上海工程技术大学 Walking-assist nursing robot integrating health information monitoring and prediction
CN114366466A (en) * 2021-12-10 2022-04-19 上海工程技术大学 Walking-replacing nursing robot integrating health information monitoring and prediction
CN114212004A (en) * 2021-12-30 2022-03-22 重庆长安新能源汽车科技有限公司 Estimation method for cruising mileage of fuel cell vehicle
CN114943485A (en) * 2022-07-21 2022-08-26 启东市航新实用技术研究所 New energy battery endurance mathematical model evaluation method based on Internet of vehicles
CN114943485B (en) * 2022-07-21 2022-09-23 启东市航新实用技术研究所 New energy battery endurance mathematical model evaluation method based on Internet of vehicles
CN115195530A (en) * 2022-08-11 2022-10-18 深圳职业技术学院 Battery cooling management control system of electric automobile
CN115195530B (en) * 2022-08-11 2023-03-10 深圳职业技术学院 Battery cooling management control system of electric automobile
CN115257407A (en) * 2022-08-16 2022-11-01 中联重科股份有限公司 Energy management method, terminal and computer storage medium for extended range electric vehicle
CN115171394B (en) * 2022-09-08 2022-11-18 南京智慧交通信息股份有限公司 Real-time safety monitoring system and method for new energy public transport vehicle
CN115171394A (en) * 2022-09-08 2022-10-11 南京智慧交通信息股份有限公司 Real-time safety monitoring system and method for new energy public transport vehicle
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
CN115782595B (en) * 2022-12-06 2024-04-02 东南大学 Electric bus instantaneous energy consumption estimation method based on energy recovery state
CN115782595A (en) * 2022-12-06 2023-03-14 东南大学 Electric bus instantaneous energy consumption estimation method based on energy recovery state
CN115952899B (en) * 2022-12-27 2023-09-29 大连理工大学 Electric bus energy consumption estimation method based on actual condition extraction
CN115952899A (en) * 2022-12-27 2023-04-11 大连理工大学 Electric bus energy consumption estimation method based on actual working condition extraction
CN116054638A (en) * 2023-02-03 2023-05-02 郑州大学 Quick-response permanent magnet synchronous motor control system for new energy automobile
CN116054638B (en) * 2023-02-03 2023-08-15 郑州大学 Quick-response permanent magnet synchronous motor control system for new energy automobile
CN116424154B (en) * 2023-03-06 2024-02-13 合众新能源汽车股份有限公司 Electric vehicle energy consumption estimation method, system, equipment and medium
CN116424154A (en) * 2023-03-06 2023-07-14 合众新能源汽车股份有限公司 Electric vehicle energy consumption estimation method, system, equipment and medium
CN117172031B (en) * 2023-10-27 2024-01-19 北京航空航天大学 Method for estimating available energy of battery system of aerocar based on vehicle speed planning
CN117172031A (en) * 2023-10-27 2023-12-05 北京航空航天大学 Method for estimating available energy of battery system of aerocar based on vehicle speed planning

Also Published As

Publication number Publication date
CN109733248B (en) 2020-07-24

Similar Documents

Publication Publication Date Title
CN109733248A (en) Pure electric automobile remaining mileage model prediction method based on routing information
CN110126841B (en) Pure electric vehicle energy consumption model prediction method based on road information and driving style
CN111753377B (en) Pure electric vehicle energy consumption optimal path planning method based on road information
CN103914985B (en) A kind of hybrid power passenger car following speed of a motor vehicle trajectory predictions method
Yavasoglu et al. Implementation of machine learning based real time range estimation method without destination knowledge for BEVs
CN105946857B (en) Parallel plug-in hybrid electric vehicle (PHEV) energy management method based on intelligent transportation system
US10829065B1 (en) Vehicle power management system
CN103213504B (en) A kind of electronlmobil continual mileage evaluation method
CN111873804B (en) Electric automobile working condition data generation method and energy consumption evaluation working condition generation method
CN111806240B (en) China working condition-based electric automobile driving range prediction method
Zhang et al. Real-time energy-efficient control for fully electric vehicles based on an explicit model predictive control method
CN110135632A (en) PHEV adaptive optimal energy management method based on routing information
CN102052926B (en) Reduce the method for fuel consumption and cost
Ganji et al. A study on look-ahead control and energy management strategies in hybrid electric vehicles
Guo et al. A novel energy consumption prediction model with combination of road information and driving style of BEVs
CN103745111A (en) Method of predicting driving range of all-electric passenger vehicles
CN114802189A (en) Energy consumption control method and device for vehicle, vehicle and storage medium
CN113642768A (en) Vehicle running energy consumption prediction method based on working condition reconstruction
Deng et al. A novel real‐time energy management strategy for plug‐in hybrid electric vehicles based on equivalence factor dynamic optimization method
Amirkhani et al. Electric vehicles driving range and energy consumption investigation: A comparative study of machine learning techniques
Ruan et al. A modularized electric vehicle model-in-the-loop simulation for transportation electrification modeling and analysis
Li et al. A comparative study of energy-oriented driving strategy for connected electric vehicles on freeways with varying slopes
Das et al. Eco-routing navigation systems in electric vehicles: A comprehensive survey
Zhang et al. Predictive energy management strategy for fully electric vehicles based on hybrid model predictive control
Unni et al. Energy Consumption Analysis for the Prediction of Battery Residual Energy in Electric Vehicles

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
GR01 Patent grant
GR01 Patent grant
CF01 Termination of patent right due to non-payment of annual fee

Granted publication date: 20200724

Termination date: 20210109

CF01 Termination of patent right due to non-payment of annual fee