CN102831768B - Hybrid power bus driving condition forecasting method based on internet of vehicles - Google Patents
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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
Technical field
The invention belongs to Modern Transportation Technology field, relate to a kind of hybrid electric vehicle and sail operating mode Forecasting Methodology, specially refer to a kind of hybrid power passenger car driving cycle Forecasting Methodology based on car networking.
Background technology
Driving cycle has a great impact the power matching of vehicle, emission level and fuel consume, so it has vital effect to the power matching of hybrid vehicle, control strategy formulation etc.China had built Beijing after deliberation in recent years, the metropolitan driving cycle such as Shanghai, but the actual operating mode of automobile is along with the time, place, environment, the factors vary such as weather, be one random, uncertain process, existing driving cycle Forecasting Methodology is according to the driving cycle data of some cycles, to make the self-adaptation adjustment of Control Strategy for Hybrid Electric Vehicle, but the method is the data accumulation based on vehicle operating some cycles identifies and regulates afterwards control strategy, so there is hysteresis quality, and accuracy rate is low, control reference significance to the following running status of vehicle is little.So a kind of method that needs at present hybrid power passenger car driving cycle to predict, carries out identification prediction to the following driving cycle of vehicle, to solve vehicle driving-cycle identification, lag behind and the low problem of recognition accuracy.
Summary of the invention
The technical problem to be solved in the present invention is: for hybrid power passenger car driving cycle, identification exists and lags behind and the low shortcoming of accuracy rate, a kind of hybrid power passenger car driving cycle Forecasting Methodology based on car networking is proposed, by the driving cycle historical data of front vehicles is transferred to this car, this car carries out identification prediction according to front car data to following operating condition, 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 consumption and emission.
Technical scheme of the present invention is: the present invention consists of data acquisition module, radio frequency identification module, short distance communication module, GPS (Global Positioning System, GPS) module, central processing module.Wherein data acquisition module and vehicle control device LAN (Controller Area Network, CAN) bus is connected, and is responsible for collection vehicle service data; Radio frequency identification module is responsible for the identity information checking between vehicle; Short distance communication module is responsible for the data transmission communication in front vehicles and this workshop; 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 whole vehicle controller (Hybrid Control Unit, HCU) communication, to HCU, provide driving cycle identification prediction information, so that HCU adjusts the control parameter of vehicle in real time.
Hybrid power passenger car driving cycle Forecasting Methodology based on car networking comprises preparatory stage, stage of communication, identification prediction stage, and three's intersection is synchronously carried out.
Preparatory stage:
GPS module is located also and electronic map match this car in real time, by real-time position information (x
0, y
0, z
0) pass to short distance communication module and central processing module; Short distance communication module sends this truck position information (x in real time towards periphery
0, y
0, z
0), receive the positional information (x of N other cars around simultaneously
p, y
p, z
p) import central processing module into; Data acquisition module is by this car of CAN bus Real-time Collection operational factor, 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 by N his truck position information (x around
p, y
p, z
p) and this truck position information (x
0, y
0, z
0), analyze around road information, directional information the calculating and this vehicle headway s of N his car
pif two cars are not in the same way or do not go the same way or apart from s
pbe greater than L, wherein, L represents the screening distance to front vehicles, and because distance road conditions change too far away is excessive, reference value is little, according to the traffic level of the communication distance of short distance communication module and passing road, regulates, and abandons communication; If two cars in the same way and go the same way and apart from s
pbe less than L, carry out the communication stage, wherein, p=1,2 ..., N.
Stage of communication:
After the determining of preparatory stage, from N his car around, filter out with this car in the same way and go the same way and be positioned at the vehicle that this front side distance is less than L, be defined as front truck 1, front truck 2 ..., front truck M, altogether M, wherein 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; Front vehicles radio frequency identification module sends this car current position (x with fixed telecommunication agreement to this car by short distance communication module after the communication request identification receiving is passed through
0, y
0, z
0) to precalculated position, the place ahead (x
0+ Δ s, y
0+ Δ s, z
0+ Δ s) history run parameter and positional information between, wherein Δ s is change of distance amount, then carries out the identification prediction stage; If M=0, this car extracts the historical running data of the some cycles of its data acquisition module collection.
The identification prediction stage:
Work as M>0, this car receives after 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 distance s
qdetermine the prediction weights omega of parameter to this car driving cycle of travelling of front truck q
q, adopt formula (1) to determine weights omega
q, wherein, q=1,2 ..., M, M≤N:
This car that adopts formula (2) to calculate prediction operating mode feature parameter of travelling:
In formula, T represents this car operating mode feature parameter vector that travels of prediction;
A
kqk the characteristic parameter that represents front truck q, wherein, q=1,2 ..., M, k=1,2 ..., K, the characteristic parameter number that K is driving cycle;
ω
mthe weight of the characteristic parameter of the driving cycle of expression front truck M to the prediction of this car driving cycle;
T
krepresent prediction this car driving cycle k characteristic parameter wherein, q=1,2 ..., M, k=1,2 ..., K, the characteristic parameter number that K is driving cycle;
In central processing module, store H class driving cycle as standard condition, in HCU, storage is controlled parameter accordingly with each operating mode; The central processing module of this car, according to the driving cycle characteristic parameter vector T of prediction, adopts Fuzzy Identification Model (formula (3)) to this car current position (x
0, y
0, z
0) to precalculated position, the place ahead (x
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.
Wherein, H is the standard condition number of storing in central processing module;
H is the h class standard operating mode of the H class standard operating mode of storing in central processing module, 1≤h≤H;
H' is the h' class standard operating mode of the H class standard operating mode of storing in central processing module, 1≤h'≤H;
U
hTfor this car of prediction operating mode feature parameter vector T that travels belongs to the relative degree of membership of h class standard operating mode;
R
kTfor travel k the characteristic parameter t of operating mode feature parameter vector T of this car of prediction
knormalized value;
X
khit is the normalized value of k characteristic parameter of h class standard operating mode;
μ
kweight for k characteristic parameter of driving cycle;
K is the number of driving cycle characteristic parameter;
Process is to after the identification of driving cycle, and central processing module is transferred to HCU by recognition result, and HCU recalls the control parameter adapting with it according to recognition result, make vehicle reach in real time optimum control.
Effect of the present invention and benefit are: the present invention reduces road traffic condition and changes the impact on running state of the vehicle; Eliminated the hysteresis quality of traditional driving cycle Forecasting Methodology; Improve the accuracy of driving cycle prediction, thereby made the control parameter of hybrid power passenger car more be applicable to the driving cycle of real-time change, improved the fuel consumption and emission of vehicle.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of hybrid power passenger car driving cycle Forecasting Methodology of the present invention.
Fig. 2 is hybrid power passenger car driving cycle Forecasting Methodology fundamental diagram of the present invention.
In 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
Below in conjunction with technical scheme and accompanying drawing, describe the specific embodiment of the present invention in detail.
Embodiment
Technical scheme of the present invention as shown in Figure 1.
Hybrid power passenger car driving cycle Forecasting Methodology based on car networking, Fig. 2 is fundamental diagram of the present invention, its detailed process is as follows:
Preparatory stage:
GPS module is located also and electronic map match this car (1) in real time, by real-time position information (x
0, y
0, z
0) pass to short distance communication module and central processing module; Short distance communication module sends this car (1) positional information (x in real time towards periphery
0, y
0, z
0), receive the positional information (x of N other cars around simultaneously
p, y
p, z
p) import central processing module into; Data acquisition module is by this car of CAN bus Real-time Collection (1) operational factor speed of a motor vehicle v etc., 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 by N his truck position information (x
p, y
p, z
p) and 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) spacing s
pif two cars are not in the same way or do not go the same way or apart from s
pbe greater than L, wherein, L can regulate according to the traffic level of the communication distance of short distance communication module and passing road,, abandon communication; If two cars in the same way and go the same way and apart from s
pbe less than L, carry out the communication stage, wherein, p=1,2 ..., N.
Stage of communication:
After the determining of preparatory stage, from N his car around, filter out M 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 the vehicle that is less than L, be defined as front truck 1 (2), front truck 2 (3) ... front truck M, altogether M, wherein 0≤M≤N; 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; Front vehicles radio frequency identification module sends this car (1) current position (x with fixed telecommunication agreement to this car (1) by short distance communication module after the communication request identification receiving is passed through
0, y
0, z
0) to precalculated position, the place ahead (x
0+ Δ s, y
0+ Δ s, z
0+ Δ s) history run parameter and positional information between, wherein Δ s is change of distance amount, then carries out the identification prediction stage; If M=0, this car (1) extracts the historical running data of the some cycles of its data acquisition module collection.
The identification prediction stage:
Work as M>0, this car (1) receives after data, it is resolved, and central processing module according to the place ahead different vehicle front truck 1 (2), front truck 2 (3) ... the distance s of front truck M and this car (1)
qdetermine the prediction weights omega of parameter to this car (1) driving cycle of travelling of front truck q
q, adopt formula (1) to determine weights omega
q, wherein, q=1,2 ..., M, M≤N:
Adopt formula (2) to calculate this car (1) prediction driving cycle characteristic parameter:
In formula, T represents this car operating mode feature parameter vector that travels of prediction;
A
kqk the characteristic parameter that represents front truck q, wherein, q=1,2 ..., M, k=1,2 ..., K, the characteristic parameter number that K is driving cycle;
ω
mthe weight of the characteristic parameter of the driving cycle of expression front truck M to the prediction of this car driving cycle;
T
krepresent prediction this car driving cycle k characteristic parameter wherein, q=1,2 ..., M, k=1,2 ..., K, the characteristic parameter number that K is driving cycle;
In central processing module, store the H class driving cycles such as Shanghai, Beijing, EDC, USADC, JDC as standard condition, in HCU, storage is controlled parameter accordingly with each operating mode; The central processing module of this car (1), according to the driving cycle characteristic parameter vector T of prediction, adopts Fuzzy Identification Model (formula (3)) to this car (1) current position (x
0, y
0, z
0) to precalculated position, the place ahead (x
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.
Wherein, H is the standard condition number of storing in central processing module;
H is the h class standard operating mode of the H class standard operating mode of storing in central processing module, 1≤h≤H;
H' is the h' class standard operating mode of the H class standard operating mode of storing in central processing module, 1≤h'≤H;
U
hTfor this car of prediction operating mode feature parameter vector T that travels belongs to the relative degree of membership of h class standard operating mode;
R
kTfor travel k the characteristic parameter t of operating mode feature parameter vector T of this car of prediction
knormalized value;
X
khit is the normalized value of k characteristic parameter of h class standard operating mode;
μ
kweight for k characteristic parameter of driving cycle;
K is the number of driving cycle characteristic parameter;
Process is to after the identification of driving cycle, and central processing module is transferred to HCU by recognition result, and HCU recalls the control parameter adapting with it according to recognition result, make vehicle reach in real time optimum control.
Each vehicle is that three phases constantly loops, and does not repeat them here.
Claims (1)
1. a hybrid power passenger car driving cycle Forecasting Methodology of networking based on car, is characterized in that comprising with the next stage:
Preparatory stage:
GPS module is located also and electronic map match this car in real time, by real-time position information (x
0, y
0, z
0) pass to short distance communication module and central processing module; Short distance communication module sends this truck position information (x in real time towards periphery
0, y
0, z
0), receive the positional information (x of N other cars around simultaneously
p, y
p, z
p) import central processing module into; Data acquisition module is by this car of CAN bus Real-time Collection operational factor, 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 by N his truck position information (x around
p, y
p, z
p) and this truck position information (x
0, y
0, z
0), analyze around road information, directional information the calculating and this vehicle headway s of N his car
pif two cars are not in the same way or do not go the same way or apart from s
pbe greater than L, wherein, L represents the screening distance to front vehicles, and because distance road conditions change too far away is excessive, reference value is little, according to the traffic level of the communication distance of short distance communication module and passing road, regulates, and abandons communication; If two cars in the same way and go the same way and apart from s
pbe less than L, carry out the communication stage, wherein, p=1,2 ..., N;
Stage of communication:
After the determining of preparatory stage, from N his car around, filter out with this car in the same way and go the same way and be positioned at the vehicle that this front side distance is less than L, be defined as front truck 1, front truck 2 ..., front truck M, altogether M, wherein 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; Front vehicles radio frequency identification module sends this car current position (x with fixed telecommunication agreement to this car by short distance communication module after the communication request identification receiving is passed through
0, y
0, z
0) to precalculated position, the place ahead (x
0+ Δ s, y
0+ Δ s, z
0+ Δ s) history run parameter and positional information between, wherein Δ s is change of distance amount, then carries out the identification prediction stage; If M=0, this car extracts the historical running data of the some cycles of its data acquisition module collection;
The identification prediction stage:
Work as M>0, this car receives after 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 distance s
qdetermine the prediction weights omega of parameter to this car driving cycle of travelling of front truck q
q, adopt formula (1) to determine weights omega
q, wherein, q=1,2 ..., M, M≤N:
This car that adopts formula (2) to calculate prediction operating mode feature parameter of travelling:
In formula, T represents this car operating mode feature parameter vector that travels of prediction;
A
kqk the characteristic parameter that represents front truck q, wherein, q=1,2 ..., M, k=1,2 ..., K, the characteristic parameter number that K is driving cycle;
ω
mthe weight of the characteristic parameter of the driving cycle of expression front truck M to the prediction of this car driving cycle;
T
krepresent prediction this car driving cycle k characteristic parameter wherein, q=1,2 ..., M, k=1,2 ..., K, the characteristic parameter number that K is driving cycle;
In central processing module, store H class driving cycle as standard condition, in HCU, storage is controlled parameter accordingly with each operating mode; The central processing module of this car, according to the driving cycle characteristic parameter vector T of prediction, adopts Fuzzy Identification Model (formula (3)) to this car current position (x
0, y
0, z
0) to precalculated position, the place ahead (x
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;
Wherein, H is the standard condition number of storing in central processing module;
H is the h class standard operating mode of the H class standard operating mode of storing in central processing module, 1≤h≤H;
H' is the h' class standard operating mode of the H class standard operating mode of storing in central processing module, 1≤h'≤H;
U
hTfor this car of prediction operating mode feature parameter vector T that travels belongs to the relative degree of membership of h class standard operating mode;
R
kTfor travel k the characteristic parameter t of operating mode feature parameter vector T of this car of prediction
knormalized value;
X
khit is the normalized value of k characteristic parameter of h class standard operating mode;
μ
kweight for k characteristic parameter of driving cycle;
K is the number of driving cycle characteristic parameter;
Process is to after the identification of driving cycle, and central processing module is transferred to HCU by recognition result, and HCU recalls the control parameter adapting with it according to recognition result, make vehicle reach in real time optimum control.
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| CN103606271B (en) * | 2013-11-27 | 2015-10-28 | 大连理工大学 | A kind of mixed power city bus control method |
| US9079587B1 (en) * | 2014-02-14 | 2015-07-14 | Ford Global Technologies, Llc | Autonomous control in a dense vehicle environment |
| CN104484721B (en) * | 2014-12-19 | 2016-03-23 | 清华大学 | A kind of energy optimal control method of hybrid-power bus team |
| 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 |
| CN107284452B (en) * | 2017-07-18 | 2018-04-10 | 吉林大学 | Merge the hybrid vehicle future operating mode forecasting system of intelligent communication information |
| CN107172215B (en) * | 2017-07-18 | 2018-03-02 | 吉林大学 | Future travel work information acquisition methods under car networking environment |
| 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 |
| CN107527113B (en) * | 2017-08-01 | 2021-03-23 | 北京理工大学 | Working condition prediction method for driving working condition of hybrid vehicle |
| CN108734810B (en) * | 2018-04-17 | 2020-09-25 | 江苏大学 | Pure electric vehicle running condition prediction method based on Internet of vehicles |
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