CN102831768A - Hybrid power bus driving condition forecasting method based on internet of vehicles - Google Patents

Hybrid power bus driving condition forecasting method based on internet of vehicles Download PDF

Info

Publication number
CN102831768A
CN102831768A CN2012102911378A CN201210291137A CN102831768A CN 102831768 A CN102831768 A CN 102831768A CN 2012102911378 A CN2012102911378 A CN 2012102911378A CN 201210291137 A CN201210291137 A CN 201210291137A CN 102831768 A CN102831768 A CN 102831768A
Authority
CN
China
Prior art keywords
car
vehicle
truck
driving cycle
information
Prior art date
Application number
CN2012102911378A
Other languages
Chinese (zh)
Other versions
CN102831768B (en
Inventor
周雅夫
连静
吕仁志
李琳辉
李海波
贾朴
庞博
Original Assignee
大连理工大学
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 大连理工大学 filed Critical 大连理工大学
Priority to CN201210291137.8A priority Critical patent/CN102831768B/en
Publication of CN102831768A publication Critical patent/CN102831768A/en
Application granted granted Critical
Publication of CN102831768B publication Critical patent/CN102831768B/en

Links

Abstract

The invention discloses a hybrid power bus driving condition forecasting method based on an internet of vehicles, and belongs to the technical field of modern transportation. The hybrid power bus driving condition forecasting method is characterized by including steps that real-time vehicle position information and running data are matched and stored; the position information of a vehicle is transmitted in real time, the position information of vehicles around the vehicle is received, and front vehicles which run in the same direction and on the same road with the vehicle and are positioned in front of the vehicle by certain distances are selected; the front vehicles separated from the vehicle within a certain distance transmit historical data to the vehicle; forecasting weights of driving parameters of the front vehicles to the driving condition of the vehicle are determined according to the distances between the front vehicles and the vehicle, and forecast characteristic parameters of the driving condition of the vehicle are computed according to the forecasting weights and characteristic parameters of the front vehicles; the driving condition within a certain distance in front of the vehicle is identified and forecast according to the forecast characteristic parameters of the driving condition of the vehicle and a fuzzy identification model; and control parameters of the vehicle are adjusted by an HCU (hybrid control unit) according to a forecast result. The hybrid power bus driving condition forecasting method has the advantages that lagging of a traditional method is eliminated, forecast accuracy is improved, and accordingly fuel economy and emission performance of the vehicle are improved.

Description

A kind of hybrid power passenger car driving cycle Forecasting Methodology based on the car networking
Technical field
The invention belongs to the Modern Transportation Technology field, relate to a kind of hybrid electric vehicle and sail the operating mode Forecasting Methodology, specially refer to a kind of hybrid power passenger car driving cycle Forecasting Methodology based on the car networking.
Background technology
Driving cycle has very big influence to power coupling, emission level and the fuel consume of vehicle, so its power coupling to hybrid vehicle, control strategy formulation etc. have crucial effects.China had made up metropolitan driving cycles such as Beijing, Shanghai after deliberation in recent years; But the actual operating mode of automobile is along with factors vary such as time, place, environment, weathers; It is at random, a uncertain process; Existing driving cycle Forecasting Methodology is to make the self-adaptation adjustment of hybrid vehicle control strategy according to the driving cycle data of some cycles, discerns and regulate control strategy afterwards but this method is based on the data accumulation of vehicle operating some cycles, so have hysteresis quality; And accuracy rate is low, and is little to the control reference significance of the following running status of vehicle.So, need a kind of hybrid power passenger car driving cycle forecast method at present, come the following driving cycle of vehicle is carried out identification prediction, lag behind and the low problem of recognition accuracy to solve vehicle driving-cycle identification.
Summary of the invention
The technical matters that the present invention will solve is: identification exists hysteresis and the low shortcoming of accuracy rate to the hybrid power passenger car driving cycle; A kind of hybrid power passenger car driving cycle Forecasting Methodology based on the car networking is proposed; Driving cycle historical data through with front vehicles is transferred to this car; This car carries out identification prediction according to preceding car data to the operating condition in future; And according to operating mode identification prediction result the following operational parameter control of vehicle is adjusted, the optimum control of real-time ensuring vehicle reaches best fuel economy and emission performance.
Technical scheme of the present invention is: (Global Positioning System, GPS) module, central processing module constitute by data acquisition module, radio frequency identification module, short distance communication module, GPS in the present invention.Wherein data acquisition module and vehicle control device LAN (Controller Area Network, CAN) bus links to each other, and is responsible for the collection vehicle service data; Radio frequency identification module is responsible for the identity information checking between vehicle; The short distance communication module is responsible for the data transmission communication in front vehicles and this workshop; The GPS module is responsible for this car location; Central processing module is responsible for data acquisition, vehicle location, data communication, calculating, the work of operating mode identification prediction of this system of overall coordination; And with hybrid power entire car controller (Hybrid Control Unit; HCU) communication; To HCU driving cycle identification prediction information is provided, so that HCU adjusts the controlled variable of vehicle in real time.
Hybrid power passenger car driving cycle Forecasting Methodology based on the car networking comprises preparatory stage, stage of communication, identification prediction stage, and three's intersection is carried out synchronously.
Preparatory stage:
The GPS module is located also and electronic map match this car in real time, with real-time position information (x 0, y 0, z 0) pass to short distance communication module and central processing module; The short distance communication module is sent this parking stall in real time towards periphery and is put information (x 0, y 0, z 0), the positional information (x of N other cars around receiving simultaneously p, y p, z p) (p=1,2 ..., N) import central processing module into; Data acquisition module is gathered this car operational factor through the CAN Bus Real Time, the real-time position information (x that real-time parameter and GPS module are transmitted 0, y 0, z 0) coupling, deposit central processing module in; Central processing module through around N his car position information (x p, y p, z p) (p=1,2 ..., N) put information (x with this parking stall 0, y 0, z 0), the road information of N his car, directional information and calculating and this vehicle headway S around analyzing p(p=1,2 ..., N), if two cars are not in the same way or do not go the same way or apart from S p(p=1,2 ..., N), then abandon communication greater than L (L representes the screening distance to front vehicles, because apart from the road changed condition is excessive too a long way, reference value is little, regulates according to the communication distance of short distance communication module and the traffic level of passing road); If two cars in the same way and go the same way and apart from S p(p=1,2 ...,, then carry out the communication stage N) less than L.
Stage of communication:
Behind the confirming of preparatory stage, from around N his car filter out with this car in the same way and go the same way and be positioned at the vehicle of this car the place ahead distance less than L, confirm as front truck 1, front truck 2 ..., front truck M, altogether M is (0≤M≤N); If M>0, the radio frequency identification module of this car to front truck 1, front truck 2 ..., front truck M sends communication request; The front vehicles radio frequency identification module sends this car current position (x with the fixed telecommunication agreement to this car through the short distance communication module after the communication request identification that receives is passed through 0, y 0, z 0) (the x to the place ahead 0+ Δ s, y 0+ Δ s, z 0+ Δ s) history run parameter and the positional information between (Δ s is the variable in distance amount) carried out the identification prediction stage then; If M=0, this car extract the historical running data of the some cycles of its data acquisition module collection.
The identification prediction stage:
As M>0, after this car receives data, it is resolved, and central processing module according to the place ahead different vehicle front truck 1, front truck 2 ..., front truck M and this car apart from S q(q=1,2 ..., M, M≤N) confirm front truck q (q=1,2 ..., the parameter of going M) is to the go prediction weights omega of operating mode of this car q(q=1,2 ..., M), adopt formula (1) to confirm weights omega q(q=1,2 ..., M):
ω q = s M - q + 1 s 1 + s 2 + · · · + s M ( s q ≤ 30 , q = 1,2 , · · · , M ) - - - ( 1 )
This car that adopts formula (2) to calculate prediction operating mode feature parameter of going:
In the formula, T representes this car of predicting operating mode feature parameter vector that goes;
a QiThe parameter i of expression front truck q;
ω qThe operational factor of expression front truck q is to the go weight of operating mode prediction of this car;
Storage H class driving cycle is stored and each operating mode control corresponding parameter among the HCU as standard condition in the central processing module; The central processing module of this car adopts Fuzzy Identification Model (formula (3)) to this car current position (x according to the driving cycle characteristic parameter vector T of prediction 0, y 0, z 0) (the x to the place ahead 0+ Δ s, y 0+ Δ s, z 0+ Δ s) driving cycle between carries out its affiliated driving cycle classification of identification prediction.If M=0, the driving cycle characteristic parameter of the historical data in extraction communication stage adopts formula (3) to carry out the identification of driving cycle.
u hj = 1 Σ k = 1 H Σ i = 1 m [ w i ( r ij - s ih ) ] 2 Σ i = 1 m [ w i ( r ij - s ik ) ] 2 - - - ( 3 )
Wherein, u HjFor sample j to be identified belongs to h (the relative degree of membership of class standard operating mode of 1≤h≤H);
r IjNormalized value for the characteristic parameter i of sample j to be identified;
s IhIt is the normalized value of the eigenwert i of h class standard operating mode;
w iWeight for driving cycle characteristic parameter i;
M is the number of driving cycle characteristic parameter;
H is the number of standard condition classification;
Through after the identification to driving cycle, central processing module is transferred to HCU with recognition result, and HCU accesses the controlled variable that adapts with it according to recognition result, makes vehicle reach optimum control in real time.
Effect of the present invention and benefit are: the present invention reduces road traffic condition and changes the influence to running state of the vehicle; Eliminated the hysteresis quality of traditional driving cycle Forecasting Methodology; Improve the driving cycle prediction accuracy, thereby made the driving cycle of the suitable more real-time change of controlled variable of hybrid power passenger car, improved the fuel economy and the emission performance of vehicle.
Description of drawings
Fig. 1 is the process flow diagram of hybrid power passenger car driving cycle Forecasting Methodology of the present invention.
Fig. 2 is a hybrid power passenger car driving cycle Forecasting Methodology fundamental diagram of the present invention.
Among the figure: 1 car; 2 front trucks 1; 3 front trucks 2; 4 front trucks 3; 5 satellites;
s 1, s 2, s 3The distance of this car 1 and front truck 1,2,3.
Embodiment
Be described in detail embodiment of the present invention below in conjunction with technical scheme and accompanying drawing.
Embodiment
Technical scheme of the present invention is as shown in Figure 1.
Based on the hybrid power passenger car driving cycle Forecasting Methodology of car networking, Fig. 2 is a fundamental diagram of the present invention, and its detailed process is following:
Preparatory stage:
The GPS module is located also and electronic map match this car (1) in real time, with real-time position information (x 0, y 0, z 0) pass to short distance communication module and central processing module; The short distance communication module is sent this car (1) positional information (x in real time towards periphery 0, y 0, z 0), the positional information (x of N other cars around receiving simultaneously p, y p, z p) (p=1,2 ..., N) import central processing module into; Data acquisition module is gathered this car (1) operational factor speed of a motor vehicle v etc. through the CAN Bus Real Time, the real-time position information (x that real-time parameter and GPS module are transmitted 0, y 0, z 0) coupling, deposit central processing module in; Central processing module is through N his car position information (x p, y p, z p) (p=1,2 ..., N) with this car (1) positional information (x 0, y 0, z 0), analyze road information, the directional information of N his car and calculate and this car (1) between apart from S p(p=1,2 ..., N), if two cars are not in the same way or do not go the same way or apart from S p(p=1,2 ..., N), then abandon communication greater than L (L can regulate according to the communication distance of short distance communication module and the traffic level of passing road); If two cars in the same way and go the same way and apart from S p(p=1,2 ...,, then carry out the communication stage N) less than L.
Stage of communication:
Behind the confirming of preparatory stage, from around N his car filter out M (0≤M≤N) with this car (1) in the same way and go the same way and be positioned at this car (1) the place ahead apart from vehicle less than L, confirm as front truck 1 (2), front truck 2 (3) ... Front truck M, M altogether; If M>0, the radio frequency identification module of this car (1) to front truck 1 (2), front truck 2 (3) ... Front truck M sends communication request; The front vehicles radio frequency identification module sends this car (1) current position (x with the fixed telecommunication agreement to this car (1) through the short distance communication module after the communication request identification that receives is passed through 0, y 0, z 0) (the x to the place ahead 0+ Δ s, y 0+ Δ s, z 0+ Δ s) history run parameter and the positional information between (Δ s is the variable in distance amount) carried out the identification prediction stage then; If M=0, this car (1) extract the historical running data of the some cycles of its data acquisition module collection.
The identification prediction stage:
As M>0, after this car (1) receives data, it is resolved, and central processing module according to the place ahead different vehicle front truck 1 (2), front truck 2 (3) ... Front truck M and this car (1) apart from S q(q=1,2 ..., M, M≤N) confirm front truck q (q=1,2 ..., the prediction weights omega of parameter of going M) to this car (1) driving cycle q(q=1,2 ..., M), adopt formula (1) to confirm weights omega q(q=1,2 ..., M):
ω q = s M - q + 1 s 1 + s 2 + · · · + s M ( s q ≤ 30 , q = 1,2 , · · · , M ) - - - ( 1 )
Adopt formula (2) to calculate this car (1) and predict the operating mode feature parameter of going:
In the formula, T representes that this car (1) driving cycle characteristic parameter of predicting is vectorial;
a IjThe parameter j of expression front truck i;
ω qThe operational factor of expression front truck q is to the weight of this car (1) driving cycle prediction;
Store H class driving cycles such as Shanghai, Beijing, EDC, USADC, JDC in the central processing module as standard condition, storage and each operating mode control corresponding parameter among the HCU; The central processing module of this car (1) adopts Fuzzy Identification Model (formula (3)) to this car (1) current position (x according to the driving cycle characteristic parameter vector T of prediction 0, y 0, z 0) (the x to the place ahead 0+ Δ s, y 0+ Δ s, z 0+ Δ s) driving cycle between carries out its affiliated driving cycle classification of identification prediction.If M=0, the driving cycle characteristic parameter of the historical data in extraction communication stage adopts formula (3) to carry out the identification of driving cycle.
u hj = 1 Σ k = 1 H Σ i = 1 m [ w i ( r ij - s ih ) ] 2 Σ i = 1 m [ w i ( r ij - s ik ) ] 2 - - - ( 3 )
Wherein, u HjFor sample j to be identified belongs to h (the relative degree of membership of class standard operating mode of 1≤h≤H);
r IjNormalized value for the characteristic parameter i of sample j to be identified;
s IhIt is the normalized value of the eigenwert i of h class standard operating mode;
w iWeight for driving cycle characteristic parameter i;
M is the number of driving cycle characteristic parameter;
H is the number of standard condition classification;
Through after the identification to driving cycle, central processing module is transferred to HCU with recognition result, and HCU accesses the controlled variable that adapts with it according to recognition result, makes vehicle reach optimum control in real time.
Each vehicle all is that the continuous circulation of three phases is carried out, and repeats no more at this.

Claims (1)

1. hybrid power passenger car driving cycle Forecasting Methodology based on car networking is characterized in that comprising with the next stage:
Preparatory stage:
The GPS module is located also and electronic map match this car in real time, with real-time position information (x 0, y 0, z 0) pass to short distance communication module and central processing module; The short distance communication module is sent this parking stall in real time towards periphery and is put information (x 0, y 0, z 0), the positional information (x of N other cars around receiving simultaneously p, y p, z p) (p=1,2,3 ..., N) import central processing module into; Data acquisition module is gathered this car operational factor through the CAN Bus Real Time, the real-time position information (x that real-time parameter and GPS module are transmitted 0, y 0, z 0) coupling, deposit central processing module in; Central processing module through around N his car position information (x p, y p, z p) (p=1,2,3 ..., N) put information (x with this parking stall 0, y 0, z 0), the road information of N his car, directional information and calculating and this vehicle headway S around analyzing p(p=1,2,3 ..., N), if two cars are not in the same way or do not go the same way or apart from S p(p=1,2,3;, N) (L representes the screening distance to front vehicles, because apart from the road changed condition is excessive too a long way greater than L; Reference value is little, regulates according to the communication distance of short distance communication module and the traffic level of passing road), then abandon communication; If two cars in the same way and go the same way and apart from S p(p=1,2,3 ...,, then carry out the communication stage N) less than L;
Stage of communication:
Behind the confirming of preparatory stage, from around N his car filter out with this car in the same way and go the same way and be positioned at the vehicle of this car the place ahead distance less than L, confirm as front truck 1, front truck 2, front truck 3 ..., altogether M is (0≤M≤N); If M>0, the radio frequency identification module of this car to front truck 1, front truck 2, front truck 3 ... Send communication request; The front vehicles radio frequency identification module sends this car current position (x with the fixed telecommunication agreement to this car through the short distance communication module after the communication request identification that receives is passed through 0, y 0, z 0) (the x to the place ahead 0+ Δ s, y 0+ Δ s, z 0+ Δ s) history run parameter and the positional information between (Δ s is the variable in distance amount) carried out the identification prediction stage then; If M=0, this car extract the historical running data of the some cycles of its data acquisition module collection;
The identification prediction stage:
As M>0, after this car receives data, it is resolved, and central processing module according to the place ahead different vehicle front truck 1, front truck 2, front truck 3 ... With this car apart from S q(q=1,2,3 ..., M, M≤N) confirm front truck q (q=1,2,3 ..., the parameter of going M) is to the go prediction weights omega of operating mode of this car q(q=1,2,3 ..., M), adopt formula (1) to confirm weights omega q(q=1,2,3 ..., M):
ω q = s M - q + 1 s 1 + s 2 + · · · + s M ( s q ≤ 30 , q = 1,2 , 3 , · · · , M ) - - - ( 1 )
This car that adopts formula (2) to calculate prediction operating mode feature parameter of going:
In the formula, T representes this car of predicting operating mode feature parameter vector that goes;
a QiThe parameter i of expression front truck q;
ω qThe operational factor of expression front truck q is to the go weight of operating mode prediction of this car;
Storage H class driving cycle is stored and each operating mode control corresponding parameter among the HCU as standard condition in the central processing module; The central processing module of this car adopts Fuzzy Identification Model (formula (3)) to this car current position (x according to the driving cycle characteristic parameter vector T of prediction 0, y 0, z 0) (the x to the place ahead 0+ Δ s, y 0+ Δ s, z 0+ Δ s) driving cycle between carries out its affiliated driving cycle classification of identification prediction; If M=0, the driving cycle characteristic parameter of the historical data in extraction communication stage adopts formula (3) to carry out the identification of driving cycle;
u hj = 1 Σ k = 1 H Σ i = 1 m [ w i ( r ij - s ih ) ] 2 Σ i = 1 m [ w i ( r ij - s ik ) ] 2 - - - ( 3 )
Wherein, u HjFor sample j to be identified belongs to h (the relative degree of membership of class standard operating mode of 1≤h≤H);
r IjNormalized value for the characteristic parameter i of sample j to be identified;
s IhIt is the normalized value of the eigenwert i of h class standard operating mode;
w iWeight for driving cycle characteristic parameter i;
M is the number of driving cycle characteristic parameter;
H is the number of standard condition classification;
Through after the identification to driving cycle, central processing module is transferred to HCU with recognition result, and HCU accesses the controlled variable that adapts with it according to recognition result, makes vehicle reach optimum control in real time.
CN201210291137.8A 2012-08-15 2012-08-15 Hybrid power bus driving condition forecasting method based on internet of vehicles CN102831768B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201210291137.8A CN102831768B (en) 2012-08-15 2012-08-15 Hybrid power bus driving condition forecasting method based on internet of vehicles

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201210291137.8A CN102831768B (en) 2012-08-15 2012-08-15 Hybrid power bus driving condition forecasting method based on internet of vehicles

Publications (2)

Publication Number Publication Date
CN102831768A true CN102831768A (en) 2012-12-19
CN102831768B CN102831768B (en) 2014-10-15

Family

ID=47334873

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201210291137.8A CN102831768B (en) 2012-08-15 2012-08-15 Hybrid power bus driving condition forecasting method based on internet of vehicles

Country Status (1)

Country Link
CN (1) CN102831768B (en)

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103606271A (en) * 2013-11-27 2014-02-26 大连理工大学 Method for controlling hybrid power urban buses
CN104484721A (en) * 2014-12-19 2015-04-01 清华大学 Energy optimization and control method of hybrid bus fleet
CN104843001A (en) * 2014-02-14 2015-08-19 福特全球技术公司 Autonomous control in a dense vehicle environment
CN107172215A (en) * 2017-07-18 2017-09-15 吉林大学 Future travel work information acquisition methods under car networking environment
CN107205046A (en) * 2017-07-18 2017-09-26 吉林大学 Join the following operating condition Information Acquisition System of vehicle towards intelligent network
CN107284452A (en) * 2017-07-18 2017-10-24 吉林大学 Merge the following operating mode forecasting system of hybrid vehicle of intelligent communication information
CN107346460A (en) * 2017-07-18 2017-11-14 吉林大学 Following operating mode Forecasting Methodology based on the lower front truck operation information of intelligent network contact system
CN107527113A (en) * 2017-08-01 2017-12-29 北京理工大学 A kind of operating mode Forecasting Methodology of hybrid car travel operating mode
CN108734810A (en) * 2018-04-17 2018-11-02 江苏大学 A kind of pure electric automobile driving cycle prediction technique based on car networking

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2002225657A (en) * 2001-02-06 2002-08-14 Oki Electric Ind Co Ltd Travel lane detecting system for vehicle
JP2004258900A (en) * 2003-02-25 2004-09-16 Aisin Seiki Co Ltd Surrounding vehicle information providing system
US20050228553A1 (en) * 2004-03-30 2005-10-13 Williams International Co., L.L.C. Hybrid Electric Vehicle Energy Management System
CN101323304A (en) * 2008-07-28 2008-12-17 北京交通大学 Running status intelligent recognition system for hybrid power electric automobile
CN102025775A (en) * 2010-10-29 2011-04-20 北京工业大学 Monitoring system for automobiles in internet of things
CN201910142U (en) * 2011-01-18 2011-07-27 曲涛 Vehicle rear-end collision early warning system based on GPS locating information and wireless communication network
CN101419677B (en) * 2008-12-11 2011-11-23 北京交通大学 Method for identifying running state of hybrid electric automobile
CN102431553A (en) * 2011-10-18 2012-05-02 奇瑞汽车股份有限公司 Active safety system and method of vehicle
CN102582637A (en) * 2011-12-20 2012-07-18 北京交通大学 Operation working condition intelligent identification evaluation system for hybrid shunter

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2002225657A (en) * 2001-02-06 2002-08-14 Oki Electric Ind Co Ltd Travel lane detecting system for vehicle
JP2004258900A (en) * 2003-02-25 2004-09-16 Aisin Seiki Co Ltd Surrounding vehicle information providing system
US20050228553A1 (en) * 2004-03-30 2005-10-13 Williams International Co., L.L.C. Hybrid Electric Vehicle Energy Management System
CN101323304A (en) * 2008-07-28 2008-12-17 北京交通大学 Running status intelligent recognition system for hybrid power electric automobile
CN101419677B (en) * 2008-12-11 2011-11-23 北京交通大学 Method for identifying running state of hybrid electric automobile
CN102025775A (en) * 2010-10-29 2011-04-20 北京工业大学 Monitoring system for automobiles in internet of things
CN201910142U (en) * 2011-01-18 2011-07-27 曲涛 Vehicle rear-end collision early warning system based on GPS locating information and wireless communication network
CN102431553A (en) * 2011-10-18 2012-05-02 奇瑞汽车股份有限公司 Active safety system and method of vehicle
CN102582637A (en) * 2011-12-20 2012-07-18 北京交通大学 Operation working condition intelligent identification evaluation system for hybrid shunter

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
周雅夫 等: "ISG混合动力电动汽车控制策略研究", 《仪器仪表学报》 *
潘姝月 等: "城市公交车行驶工况的研究", 《机械设计与制造》 *
连静 等: "汽车电子控制系统半实物仿真平台开发", 《大连理工大学学报》 *

Cited By (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103606271A (en) * 2013-11-27 2014-02-26 大连理工大学 Method for controlling hybrid power urban buses
CN103606271B (en) * 2013-11-27 2015-10-28 大连理工大学 A kind of mixed power city bus control method
CN104843001A (en) * 2014-02-14 2015-08-19 福特全球技术公司 Autonomous control in a dense vehicle environment
CN104484721A (en) * 2014-12-19 2015-04-01 清华大学 Energy optimization and control method of hybrid bus fleet
CN104484721B (en) * 2014-12-19 2016-03-23 清华大学 A kind of energy optimal control method of hybrid-power bus team
CN107284452A (en) * 2017-07-18 2017-10-24 吉林大学 Merge the following operating mode forecasting system of hybrid vehicle of intelligent communication information
CN107205046A (en) * 2017-07-18 2017-09-26 吉林大学 Join the following operating condition Information Acquisition System of vehicle towards intelligent network
CN107172215A (en) * 2017-07-18 2017-09-15 吉林大学 Future travel work information acquisition methods under car networking environment
CN107346460A (en) * 2017-07-18 2017-11-14 吉林大学 Following operating mode Forecasting Methodology based on the lower front truck operation information of intelligent network contact system
CN107346460B (en) * 2017-07-18 2018-06-12 吉林大学 Following operating mode Forecasting Methodology based on the lower front truck operation information of intelligent network contact system
CN107205046B (en) * 2017-07-18 2018-02-27 吉林大学 Towards the following operating condition Information Acquisition System of intelligent network connection vehicle
CN107172215B (en) * 2017-07-18 2018-03-02 吉林大学 Future travel work information acquisition methods under car networking environment
CN107527113A (en) * 2017-08-01 2017-12-29 北京理工大学 A kind of operating mode Forecasting Methodology of hybrid car travel operating mode
CN108734810A (en) * 2018-04-17 2018-11-02 江苏大学 A kind of pure electric automobile driving cycle prediction technique based on car networking

Also Published As

Publication number Publication date
CN102831768B (en) 2014-10-15

Similar Documents

Publication Publication Date Title
CN105654779B (en) Highway construction area traffic coordinating and controlling method based on bus or train route, truck traffic
US9587954B2 (en) System and method for vehicle routing using stochastic optimization
CN105139677B (en) The No-shell culture vehicle pass-through guiding system and its bootstrap technique cooperateed with based on bus or train route
US8972145B2 (en) Systems and methods for predicting traffic signal information
Zulkefli et al. Hybrid powertrain optimization with trajectory prediction based on inter-vehicle-communication and vehicle-infrastructure-integration
CN104002680B (en) The speed control based on efficiency with traffic compatibility velocity shifts
CN104157139B (en) A kind of traffic congestion Forecasting Methodology and method for visualizing
CN107284441B (en) The energy-optimised management method of the adaptive plug-in hybrid-power automobile of real-time working condition
CN102390320B (en) Vehicle anti-collision early warning system based on vehicle-mounted sensing network
US8949028B1 (en) Multi-modal route planning
CN103175534B (en) The system that vehicle route is determined
CN101994584B (en) Road grade coordinated engine control systems
CN100374332C (en) Vehicle imbedding type system
CN108162771B (en) Intelligent charging navigation method for electric automobile
CN102622907B (en) Driving assistant method and system for electric vehicle
CN102762428B (en) Controller of vehicle
CN103606271B (en) A kind of mixed power city bus control method
CN104502122B (en) Unmanned vehicle remote control brake performance testing and assessment method
CN102089178B (en) Adapter device and method for charging a vehicle
CN102837697B (en) A kind of electronlmobil course continuation mileage management system and method for work
CN103106702B (en) Based on the bus trip service system of cloud computing
CN103200525B (en) A kind of car networking trackside information gathering and service system
Wu et al. Energy-optimal speed control for electric vehicles on signalized arterials
CN105000019A (en) Method and system for detecting, tracking and estimating stationary roadside objects
DE112012000447T5 (en) System and method of fuel quantity management of a vehicle

Legal Events

Date Code Title Description
PB01 Publication
C06 Publication
SE01 Entry into force of request for substantive examination
C10 Entry into substantive examination
GR01 Patent grant
C14 Grant of patent or utility model
CF01 Termination of patent right due to non-payment of annual fee

Granted publication date: 20141015

Termination date: 20170815

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