CN109767619A - An intelligent network-connected pure electric vehicle driving condition prediction method - Google Patents

An intelligent network-connected pure electric vehicle driving condition prediction method Download PDF

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CN109767619A
CN109767619A CN201811635195.1A CN201811635195A CN109767619A CN 109767619 A CN109767619 A CN 109767619A CN 201811635195 A CN201811635195 A CN 201811635195A CN 109767619 A CN109767619 A CN 109767619A
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CN109767619B (en
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盘朝奉
顾喜薇
梁军
陈小波
梁岩岩
陶袁雪
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Hengshui Hengyin Enterprise Management Co.,Ltd.
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Jiangsu University
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Abstract

本发明公开了一种智能网联纯电动汽车行驶工况预测方法,视频检测系统获取路况信息、车载传感器获取车辆行驶信息、GPS卫星定位系统获取车辆位置信息,并传递给云端服务器;若道路当前为畅行路况,则根据马尔可夫滚动预测方法预测未来工况信息;若为拥堵路况,则对根据本车前后车辆的历史数据进行预测;若为缓行路况,则根据马尔可夫滚动预测方法和本车前后车辆的历史数据进行未来工况信息的预测,将预测结果传递给主控制器,求得预测值与实际值的误差作为下次预测的反馈输入。本发明采用智能网联的方法实时获取路况信息,提高了预测的准确性,保证预测的未来行驶工况信息更接近于实际工况信息。

The invention discloses a method for predicting the driving conditions of an intelligent network-connected pure electric vehicle. A video detection system acquires road condition information, an on-board sensor acquires vehicle driving information, and a GPS satellite positioning system acquires vehicle location information, and transmits it to a cloud server; For smooth road conditions, the future working condition information is predicted according to the Markov rolling prediction method; if it is a congested road condition, it is predicted based on the historical data of the vehicles before and after the vehicle; The historical data of the vehicle before and after the vehicle is used to predict the future working condition information, and the prediction result is transmitted to the main controller, and the error between the predicted value and the actual value is obtained as the feedback input for the next prediction. The invention adopts the method of intelligent network connection to acquire road condition information in real time, improves the accuracy of prediction, and ensures that the predicted future driving condition information is closer to the actual working condition information.

Description

A kind of intelligent network connection pure electric automobile driving cycle prediction technique
Technical field
The present invention relates to intelligent networks to join automobile technical field, and in particular to a kind of intelligent network connection pure electric automobile driving cycle Prediction technique.
Background technique
With being on the rise for environmental pollution and energy crisis, with energy conservation and environmental protection, no pollution, low noise pure electric vehicle vapour Vehicle becomes one of important development object.However pure electric automobile is single finite energy source, continual mileage is short to be become The main reason for limiting pure electric automobile development.In different zones, vehicle actual operating mode has very big difference.Vehicle is practical Driving cycle will have a direct impact on the energy consumption and continual mileage of pure electric automobile, can optimize pure electricity to the prediction of future travel operating condition The energy management control strategy of electrical automobile further increases the energy consumption and continual mileage performance of pure electric automobile.Currently, pure electric vehicle Operating condition prediction of the automobile running working condition prediction predominantly based on historical information, by carrying out statistical analysis, benefit to historical data It is predicted with statistics rule combination intelligent algorithm, however this method can not know current work condition environment, precision of prediction It is not high;The operating condition prediction that real-time ambient enviroment is combined with historical information is seemed very necessary thus.
Summary of the invention
The purpose of the present invention is to provide a kind of intelligent networks to join pure electric automobile driving cycle prediction technique, it is intended to utilize vehicle Networked system, it is contemplated that surrounding work condition environment ensure that the future travel work information of prediction closer to the following actual condition Information realizes that technical scheme is as follows:
A kind of intelligent network connection pure electric automobile driving cycle prediction technique, includes the following steps:
Step 1), video detection system acquisition road information are simultaneously sent to cloud server, and cloud server carries out it Processing obtains vehicle flowrate, vehicle and vehicle centroid position;
Step 2), car-mounted terminal obtains car speed and acceleration information, GPS satellite positioning system obtain vehicle location letter Breath, and the information of acquisition is transmitted to cloud server, cloud server stores data, analyzed and is calculated;
Step 3), cloud server according to the speed, acceleration of acquisition, position, vehicle, vehicle flowrate and vehicle centroid away from Operating condition prediction is carried out from information to calculate;If the road conditions that pass unimpeded, using markov rolling forecast method prediction future travel operating condition letter Breath;If congestion road conditions, then future travel information is predicted according to the historical information between Che-vehicle;If jogging road conditions, It then compares, takes and current shape by markov rolling forecast result and by the result that historical information between Che-vehicle is predicted State phase close values are as prediction result;
Prediction result is transmitted to master controller by step 4), and master controller obtains the mistake between prediction result and actual value Difference adjusts next prediction result.
Further, the markov rolling forecast method predicts future travel work information, specifically: when taking rolling history Between historical data in window T, state demarcation is carried out to speed-acceleration, the frequency converted between each state is calculated and constitutes transfer State matrix takes the product of maximum probability value and current state as predicted value, and according to Current traffic traffic information to prediction Value is modified:Wherein, vtIndicate present speed, atExpression is worked as Preacceleration, vt+1Indicate NextState speed, at+1Indicate NextState acceleration, KcIndicate the amendment system passed unimpeded under road conditions Number, KhIndicate the correction factor under jogging road conditions, maxPijIndicate the maximum probability that NextState is transferred to from state i;Described turn Shifting state matrix
Further, when the congestion road conditions, cloud server is gone the same way according to this vehicle with the history row of n vehicle after m forward Data are sailed, are predicted:Wherein μiIndicate fore-aft vehicle It whether is same type, L with this vehicleiIndicate length of the fore-aft vehicle centroid position apart from this vehicle centroid position, viIndicate fore-aft vehicle Rolling the average speed in historical time window T, aiIndicate that fore-aft vehicle is rolling the average acceleration in historical time window T; The μiWhen being 1, fore-aft vehicle and this vehicle are same types of vehicles, the μiWhen being 0, fore-aft vehicle and this vehicle are not same type vehicles ?.
Further, when the jogging road conditions, with current state phase close values according to vt+1=vt±min{|vt+1,c-vt|,| vt+1,m-vt|}、at+1=at±min{|at+1,c-at|,|at+1,m-at| prediction result is obtained, wherein vt+1,cIt indicates to pass through Che-vehicle The NextState speed of historical information prediction, vt+1,mIndicate the NextState speed obtained by markov rolling forecast, at+1,cIt indicates through Che-vehicle historical information prediction NextState acceleration, at+1,mExpression is obtained by markov rolling forecast The NextState acceleration arrived.
The invention has the benefit that
(1) Markov forecast techniques driving cycle is compared with being suitable for change the little steady operating condition that passes unimpeded, and in vehicle comparatively dense When, it can mutually be restricted between vehicle and vehicle, Markov forecast techniques driving cycle can generate biggish error.The present invention considers currently Traffic information, and different prediction modes is chosen according to current traffic information, to effectively improve the accuracy of prediction.
(2) present invention is by speed-acceleration error when the prediction result acquired and actual travel, as predicting next time Feed back input, to prediction constantly adjusted, to guarantee precision of prediction.
Detailed description of the invention
Fig. 1 is the hardware frame figure of the embodiment of the present invention;
Fig. 2 is the structural schematic diagram of the embodiment of the present invention;
Fig. 3 is the specific flow chart of the embodiment of the present invention.
Specific embodiment
Invention is further described in detail with reference to the accompanying drawings and detailed description.
As shown in Figure 1 be the hardware frame figure of the embodiment of the present invention: video detection system acquires road information and passes through nothing Line communication network passes to cloud server, obtains vehicle, vehicle flowrate and vehicle centroid position after cloud server processing, The vehicle position information that the vehicle traveling information and GPS satellite positioning system that onboard sensor obtains obtain passes to car-mounted terminal And master controller, network passes to cloud server to car-mounted terminal by wireless communication, cloud server handles data, Prediction result is back to master controller and compared with actual value by analysis and calculating.
Be illustrated in figure 2 the structural schematic diagram of the embodiment of the present invention: video detection system includes switching regulator sensor, data Acquisition Instrument and video camera;Camera arrangements carry out continuously recording at crossing, to traffic conditions, and switching regulator sensor is equally arranged in Crossing, the length is the overall width in lane, width is about the 70% of ordinary vehicle type vehicle commander;Video camera and sensor are collected Information send and be stored in data collecting instrument, network passes data to cloud service to data collecting instrument by wireless communication Device carries out information processing.The external minimum rectangle area of vehicle's contour is extracted in video camera acquired image data with preliminary It identifies vehicle, the centroid position of each vehicle is obtained by the detection zone of analysis switching regulator sensor composition, introduces and expands The trace model of kalman filtering, the frame number in region accurately differentiates type of vehicle to calculating vehicle profile target after testing, has Body is divided into carriage type, middle vehicle and large-scale vechicle, in addition, region vehicle after testing in the switching regulator sensor statistical unit period Number obtains vehicle flowrate data.Vehicle position information is obtained by GPS satellite positioning system, is handled first vehicle position data, The speed of service of different function grade road is obtained, then the road is calculated according to function path difference and vehicle flowrate data and exists Shared weight in the whole network provides the index index value for being converted to 0-10, index range is denoted as the road conditions that pass unimpeded for 0-2, by 2-6 Jogging road conditions are denoted as, 6-10 is denoted as congestion road conditions;L mass center length between vehicle in figure.
It is illustrated in figure 3 the specific flow chart of the embodiment of the present invention:
Step 1), video detection system acquires road information and network transfers information to cloud service by wireless communication Device, cloud server carry out digitized processing to information, obtain vehicle flowrate, vehicle and vehicle centroid position;
Step 2), car-mounted terminal obtain car speed, acceleration information and GPS satellite positioning system by onboard sensor System obtains vehicle position information, and the speed, acceleration of acquisition and location information are transmitted to cloud by network by wireless communication Server is held, cloud server stores data, analyzed and calculated;
Step 3), cloud server according to the speed, acceleration of acquisition, position, vehicle, vehicle flowrate and vehicle centroid away from Operating condition prediction is carried out from information to calculate;
If the road conditions that pass unimpeded, then future travel work information is predicted using markov rolling forecast method: taking rolling Historical data in historical time window T carries out state demarcation to speed-acceleration, calculates the frequency structure converted between each state At transfering state matrixTake the product of maximum probability value and current state as predicted value, and root Predicted value is modified according to Current traffic traffic information:
Wherein, vtIndicate present speed, atIndicate current acceleration, vt+1Indicate NextState speed, at+1Indicate next shape State
Acceleration, KcIndicate the correction factor to pass unimpeded under road conditions, KhIndicate the correction factor under jogging road conditions, maxPijTable Show the maximum probability that NextState is transferred to from state i.
If congestion road conditions, due to mutually restricting between vehicle, driving cycle is roughly the same, then according between Che-vehicle Historical information (being stored in car networking) predicts that future travel information, cloud server is according to vehicle location, vehicle, vehicle Away from, speed and acceleration information, obtains and predicted with this vehicle history running data of n vehicle after m forward of going the same way together.It is gathering around Under stifled road conditions, speed variation is smaller, and vehicle operation data is influenced and more close then to the shadow of this vehicle apart from this vehicle by surrounding vehicles Sound is bigger, therefore when predicting NextState velocity and acceleration, the error of current state velocity and acceleration is adjusted:
Wherein, μiIt indicates that when fore-aft vehicle and this vehicle are same types be 1, is otherwise 0, LiIndicate fore-aft vehicle centroid position Length apart from this vehicle centroid position, viIndicate that fore-aft vehicle is rolling the average speed in historical time window T, aiIndicate front and back Average acceleration of the vehicle in receding horizon T.
If jogging road conditions, then by markov rolling forecast result and the knot predicted by historical information between Che-vehicle Fruit compares, and asks two kinds of results of prediction and calculation with current state phase close values as prediction result, it may be assumed that
vt+1=vt±min{|vt+1,c-vt|,|vt+1,m-vt|} (5)
at+1=at±min{|at+1,c-at|,|at+1,m-at|} (6)
Wherein, vt+1,cIt indicates through Che-vehicle historical information prediction NextState speed, vt+1,mIndicating can by Ma Er The NextState speed that husband's rolling forecast obtains, at+1,cIt indicates through Che-vehicle historical information prediction NextState acceleration, at+1,mIndicate the NextState acceleration obtained by markov rolling forecast.
Step 4), by prediction result, network transmission to master controller, master controller obtains the speed of prediction by wireless communication Error between degree, acceleration and actual speed, acceleration, and using the error as the feed back input predicted next time, it is right Prediction result is constantly adjusted.
Above-mentioned, although the foregoing specific embodiments of the present invention is described with reference to the accompanying drawings, not protects model to the present invention The limitation enclosed, those skilled in the art should understand that, based on the technical solutions of the present invention, those skilled in the art are not Need to make the creative labor the various modifications or changes that can be made still within protection scope of the present invention.

Claims (6)

1. a kind of intelligent network joins pure electric automobile driving cycle prediction technique, which comprises the steps of:
Step 1), video detection system acquisition road information are simultaneously sent to cloud server, and cloud server handles it Obtain vehicle flowrate, vehicle and vehicle centroid position;
Step 2), car-mounted terminal obtains car speed and acceleration information, GPS satellite positioning system obtain vehicle position information, And the information of acquisition is transmitted to cloud server, cloud server stores data, analyzed and is calculated;
Step 3), cloud server is according to the speed, acceleration of acquisition, position, vehicle, vehicle flowrate and vehicle centroid distance letter Breath carries out operating condition prediction and calculates;If the road conditions that pass unimpeded, future travel work information is predicted using markov rolling forecast method;If For congestion road conditions, then future travel information is predicted according to the historical information between Che-vehicle;If jogging road conditions, then will Markov rolling forecast result and by between Che-vehicle historical information predict result compare, take and current state phase Close values are as prediction result;
Prediction result is transmitted to master controller by step 4), and master controller obtains the error between prediction result and actual value, adjusts Whole prediction result next time.
2. intelligent network according to claim 1 joins pure electric automobile driving cycle prediction technique, which is characterized in that the horse Er Kefu rolling forecast method predicts future travel work information, specifically: the historical data rolled in historical time window T is taken, it is right Speed-acceleration carries out state demarcation, calculates the frequency converted between each state and constitutes transfering state matrix, takes maximum probability value Product with current state is modified predicted value as predicted value, and according to Current traffic traffic information:Wherein, vtIndicate present speed, atIndicate current acceleration, vt+1 Indicate NextState speed, at+1Indicate NextState acceleration, KcIndicate the correction factor to pass unimpeded under road conditions, KhIndicate jogging road Correction factor under condition, maxPijIndicate the maximum probability that NextState is transferred to from state i.
3. intelligent network according to claim 2 joins pure electric automobile driving cycle prediction technique, which is characterized in that described turn Shifting state matrix
4. intelligent network according to claim 1 or 2 joins pure electric automobile driving cycle prediction technique, which is characterized in that institute When stating congestion road conditions, cloud server is gone the same way according to this vehicle with the history running data of n vehicle after m forward, is predicted:Wherein μiIt indicates fore-aft vehicle and whether this vehicle is similar Type, LiIndicate length of the fore-aft vehicle centroid position apart from this vehicle centroid position, viIndicate that fore-aft vehicle is rolling historical time window Average speed in T, aiIndicate that fore-aft vehicle is rolling the average acceleration in historical time window T.
5. intelligent network according to claim 4 joins pure electric automobile driving cycle prediction technique, which is characterized in that the μi When being 1, fore-aft vehicle and this vehicle are same types of vehicles, the μiWhen being 0, fore-aft vehicle and this vehicle are not same types of vehicles.
6. intelligent network according to claim 4 joins pure electric automobile driving cycle prediction technique, which is characterized in that described slow When walking along the street condition, with current state phase close values according to vt+1=vt±min{|vt+1,c-vt|,|vt+1,m-vt|}、at+1=at±min{| at+1,c-at|,|at+1,m-at| prediction result is obtained, wherein vt+1,cIt indicates through Che-vehicle historical information prediction NextState vehicle Speed, vt+1,mIndicate the NextState speed obtained by markov rolling forecast, at+1,cIt indicates to pass through Che-vehicle historical information The NextState acceleration of prediction, at+1,mIndicate the NextState acceleration obtained by markov rolling forecast.
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CN110610611A (en) * 2019-09-26 2019-12-24 江苏大学 Driving safety evaluation method for intelligent networked vehicles in mixed traffic flow
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