CN108108841A - A kind of hybrid power energy management strategies global optimization system based on large database concept - Google Patents
A kind of hybrid power energy management strategies global optimization system based on large database concept Download PDFInfo
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
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- G06F18/23—Clustering techniques
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- 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
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/16—Information or communication technologies improving the operation of electric vehicles
- Y02T90/167—Systems integrating technologies related to power network operation and communication or information technologies for supporting the interoperability of electric or hybrid vehicles, i.e. smartgrids as interface for battery charging of electric vehicles [EV] or hybrid vehicles [HEV]
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- 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
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S30/00—Systems supporting specific end-user applications in the sector of transportation
- Y04S30/10—Systems supporting the interoperability of electric or hybrid vehicles
- Y04S30/12—Remote or cooperative charging
Abstract
The invention discloses a kind of hybrid power energy management strategies global optimization systems based on large database concept, it is made of teledata library module and car-mounted terminal two parts, the car-mounted terminal includes data acquisition module, pending data memory module, hybrid power whole vehicle controller, CPU module, individuation data memory module；Data acquisition module includes GPS module, accelerator pedal position acquisition module, brake pedal position acquisition module, steering moment acquisition module, transmission gear acquisition module, six axle sensor modules, engine running parameter acquisition module, battery charge state acquisition module.The rapid car networking technology of present invention comprehensive utilization current development, sensor technology, GPS technology, the driving information contribution for the vehicle for loading the system by each, quickly set up a traffic information large database concept for covering full region.
Description
Technical field
The invention belongs to technical field of modern transportation, are related to a kind of hybrid electric vehicle and sail operating mode prediction, energy management
The system of tactful global optimization, more specifically, being that be related to a kind of hybrid power energy management strategies based on large database concept complete
Office's optimization system.
Background technology
In face of the pressure of the energy and environment, development is energy saving and new-energy automobile is imperative.Hybrid vehicle is due to tool
There are higher fuel economy and relatively low discharge, become major automobile business men and march toward the important way in new-energy automobile field
Footpath.And the energy management control strategy of hybrid vehicle, to realizing its high efficiency, low emission has conclusive influence, therefore
Soul and core technology as vehicle.Currently, the energy management strategies of hybrid vehicle are broadly divided into based on simple rule
Control strategy, instantaneous optimization control strategy and global optimization control strategy three types.
Control strategy based on simple rule is mainly threshold control method, and this method is according to engine and electronic
The MAP chart of machine to set the turn-off criterion of engine, torque limit value, rotational speed limit, SOC limit values of battery etc., makes engine,
Motor, battery are all operated between high efficient area, and so as to improve fuel economy, this control method is quick and easy, highly practical,
Therefore have been widely used, but since threshold value is fixed value, do not account for actual driving operating mode, so as to obtain
Obtain the maximal efficiency of Full Vehicle System.
Instantaneous optimization control strategy is at each moment of vehicle traveling, calculates all hairs for meeting driver's demand torque
Motivation and motor output torque combine the electricity of corresponding fuel consumption and consumption, which is expressed as sending out
The equivalent fuel consumption of motivation fuel consumption and consumption electricity, adjusts motor output torque, obtains transient fuel consumption most
Engine corresponding to the minimum value and motor output torque are combined the operating point as hybrid power assembly by small value, meanwhile,
This method can also consider emission performance, and so as to obtain comprehensive optimal performance, but such method needs are largely floated
Point processing, real-time is poor, and control effect too relies on the accuracy of each component capabilities characterisitic parameter, by cell degradation, starts
The influence of dynamics etc. is difficult to realize in the real-time control of actual vehicle.
Instantaneous optimization control strategy is optimized for some real-time operating mode, it is ensured that the instantaneous optimal combustion of vehicle
Oily economy and emission performance, but from the sum of optimum theory, instantaneous minimum value and it is not equal to the minimum value of sum, therefore not
Can guarantee entire vehicle run during it is optimal, global optimization control strategy can realize optimization truly.
Global optimization control strategy is for target with one section of set complete equivalent fuel consumption of vehicle travelled in road conditions, discharge etc.
Function establishes global optimization mathematical model under the conditions of vehicle mechanical constraint etc. is met, is meeting motor, engine, battery etc.
Under the restriction of condition, global optimal energy allocative decision is acquired.But since global optimization control strategy needs are known in advance
Entire driving cycle could obtain optimal effect of optimization, thus limit its extensive use.
The content of the invention
The purpose of the invention is to overcome deficiency of the prior art, for current entire stroke operating mode, it is difficult to predict lead
A kind of the problem of causing global optimization that can not realize, it is proposed that hybrid power energy management strategies global optimization based on large database concept
System, the rapid car networking technology of system comprehensive utilization current development, sensor technology, GPS technology, passes through each loading
The driving information contribution of the vehicle of the system quickly sets up the traffic information large database concept of a full region of covering, in addition, logical
Acquisition driving information is crossed, realizes the self study to drivers preference, so as to before stroke starts, be predicted by large database concept
The operating mode of entire stroke, and drivers preference amendment is combined, it realizes the global optimization of hybrid power energy hole, reaches better
Fuel consumption and emission.
The purpose of the present invention is what is be achieved through the following technical solutions.
The hybrid power energy management strategies global optimization system based on large database concept of the present invention, by remote data base mould
Block and car-mounted terminal two parts composition, the car-mounted terminal include data acquisition module, pending data memory module, mix and move
Power entire car controller, CPU module, individuation data memory module；
The data acquisition module, pending data memory module, CPU module and individuation data storage mould
Block is sequentially connected in series to be communicated by CAN bus；The CPU module passes through cellular network with teledata library module
Carry out data transmission；The CPU module is communicated with hybrid power whole vehicle controller with CAN bus, sends optimization
Control information afterwards；
The data acquisition module includes GPS module, accelerator pedal position acquisition module, brake pedal position acquisition mould
Block, steering moment acquisition module, transmission gear acquisition module, six axle sensor modules, engine running parameter acquisition module,
Battery charge state acquisition module；The GPS module is described to add for obtaining vehicle position information, real-time speed, standard time
Speed pedal station acquisition module is used to obtain accelerator pedal displacement and change in displacement rate, the brake pedal position acquisition module
For obtaining brake pedal displacement and change in displacement rate, the steering moment acquisition module is described for obtaining direction information
Transmission gear acquisition module for obtaining real-time gear information, for obtaining vehicle attitude believe by the six axle sensors module
Breath, the engine running parameter acquisition module are described for obtaining the real-time rotating speed of engine, power, torque, fuel consumption
Battery charge state acquisition module is used to obtain real-time state of charge；
The pending data memory module is used to storing the real time data of data collecting module collected, during storage according to when
Between order packing storage is carried out to data；
The effect of the CPU module：(1) after single stroke, to the number in pending data memory module
Two classes will be packaged as after information processing according to unpacking, and according to tentation data processing rule, one kind is to be driven with vehicle and driver
The related personalizing parameters of preference are sailed, are stored in individuation data memory module, one kind is general parameter only related with road conditions, is led to
It crosses cellular network and is uploaded to teledata library module, the personalizing parameters and the general parameter related with road conditions are referred to as
Operating mode property parameters；(2) before stroke starts, according to path planning, download correspondence position from teledata library module and correspond to the time
General parameter, and combine individuation data memory module in personalizing parameters, energy pipe is carried out by global optimization approach
The optimization of reason strategy calculates, and the location information according to GPS module acquisition sends control information to mixed in real time in the process of moving
Power entire car controller is closed, so as to fulfill whole optimal.
The tentation data processing rule is realized with the following method：
(1) vehicle driving-cycle is divided into city operating mode and two major class of mountainous region operating mode；Wherein, the city operating mode is divided into
For downtown operating mode, suburbs operating mode, outer suburbs operating mode, expressway operating mode, the mountainous region operating mode refers to comprising continuous climbing for a long time, turns
The operating mode of one or more of curved, descending feature, data are stored according to operating mode classification after processing；
(2) data processing includes three phases：Operating mode block division stage, operating mode block's attribute parameter calculation stage, operating mode block
Property parameters are packaged and memory phase；
(3) the operating mode block division stage is carried out by operating mode's switch controller, and the operating mode's switch controller input becomes
It measures as vehicle location, time, speed, engine power, car body obliqueness, output variable is corresponding operating mode classification；The operating mode is known
Other controller mainly includes three parts：One-dimensional Gaussian filter, block divider, cluster analysis device；
The car body obliqueness refers to the angle of front and rear axle midpoint line and horizontal plane, when front axle midpoint is higher than rear axle midpoint,
Inclination value is just, when front axle midpoint is less than rear axle midpoint, inclination value is negative, and the inclination value can pass through six axle sensor
Module obtains；
(4) according to the corresponding standard condition of the operating mode block, its corresponding operating mode property parameters is resolved；
(5) after the completion of the operating mode property parameters resolve, the general parameter is transmitted to teledata library module
It is stored, the personalizing parameters is transmitted to individuation data memory module and are stored；
(6) the teledata library module unpacks the general parameter received, and new data and legacy data are carried out pair
After being handled than integration, refresh data with the continuous accumulation of data, will form the work information of the full region of covering, and to vehicle
Mounted terminal transmits the integration of data, improves the accuracy to the prediction of same a road section operating mode.
The division of operating mode block described in step (3) is realized with the following method：
A. with the smooth speed versus time curve of one-dimensional Gaussian filter, unnecessary velocity disturbance is eliminated；
B. operating mode block division is carried out to curve with the block divider；
C. operating mode classification judgement is carried out to it by elementary cell of operating mode block using cluster analysis device.
One-dimensional Gaussian filter is as follows described in step A：
[w1 w2 w3]T
w1、w2、w3The weight of respectively the 1st, the 2nd, the 3rd sampled point, w1+w2+w3=1；
The rate matrices formed to M sampled point
[v1,v2,…vN-1,vN,vN+1,…vM]
The speed v of n-th sampled point after filteringNFor
vN=vN-1w1+vNw2+vN+1w3
Block divider division rule is described in step B：
B.1 idling (stopping) speed domain [0, v is defineds], i.e., travel speed is in [0, vs] in the range of be in idling for vehicle
The necessary condition of (stopping) state；
B.2 idling (stopping) the effective time region [Td is definedl, Tdh], i.e., travel speed is in [0, vs] in the range of continue
Time is in [Tdl, Tdh] necessary and sufficient condition of idling (stopping) state is in domain for vehicle；
B.3 define and at the uniform velocity travel acceleration domain [al, ah], i.e., vehicle acceleration is in [al, ah] in the range of at the uniform velocity to travel
The necessary condition of state；
B.4 define and at the uniform velocity travel the effective time region [Tyl, Tyh], i.e., acceleration is in [al, ah] in the range of duration
In [Tyl, Tyh] necessary and sufficient condition of at the uniform velocity state is in domain for vehicle；
B.5 the equal domain of average speed is definedI.e. when the average speed difference of two continuous blocks exists
When interior, it is believed that its average speed is equal；
B.6 operating mode block division is changed with time using speed as Primary Reference foundation, and thinks that average speed is equal
Continuous segment is same operating block, and the continuous segment that average speed does not wait is different operating mode blocks.
B.6 detailed process is step：
B.6.1 using idling mode as segmentation section, complete stroke is divided into i sections (including idling operation sections), and records every section
The corresponding beginning and ending timeStart-stop position(p 1,2 ... is i)；
B.6.2 corresponding acceleration is calculated the sampled point in each section(p 1,2 ... i, N 1,2 ... M, M are p sections
Middle sampling number), extract all j sections at the uniform velocity sections, and calculate each at the uniform velocity section average speed in the section(p 1,2 ... i, q are
1,2 ... j)；
B.6.3 at the uniform velocity section terminal extends：To p sections, (p 1,2 ... all j sections at the uniform velocity sections in i), it is even that starting point extends to this
The starting point for the gear stage that is connected before fast section, jth section stop, which extends to gear stage thereafter, to be terminated, remaining section of stop is constant, each after extension
Section terminal position is denoted asBeginning and ending time is denoted as
B.6.4 all at the uniform velocity sections in i sections are reintegrated, it is specifically, average speed in adjacent at the uniform velocity section is equal
Section merges, and the idling operation section of midfeather is merged into same operating block together, and average speed in p sections is not waited
The at the uniform velocity section that is connected is split as different operating mode blocks, and the idling section not being merged is merged into connected previous at the uniform velocity section, and update is closed
And all i after the completion of splittingnewSection operating mode block beginning and ending time beStart stop bit is set to(pnewThe i for 1,2 ...new)。
The clustering algorithm used in step C：
C.1 each operating mode block be 1 sample to be clustered, inewA sample composition set to be clustered：
X={ x1,x2,…,xpnew,…,xinew}
C.2 each sample to be clustered is represented with four characteristic parameters：
Wherein,For average speed, αpnewFor dead time ratio,For mean power,It is exhausted for car body obliqueness
To the average value of value；
C.3 i is usednew× 4 rank matrixes represent that each sample character pair parameter to be clustered is：
C.4 since characteristic parameter dimension is different, each characteristic parameter is normalized, obtains characteristic parameter rule
It formats matrix：
Wherein, rijFor the normalized value of the ith feature parameter of j-th of sample to be clustered；
C.5 four features of foregoing downtown operating mode, suburbs operating mode, outer suburbs operating mode, expressway operating mode, hill path operating mode are joined
Several normalized values is as cluster centre matrix：
Wherein, sihFor the normalized value of the ith feature parameter of h class standard operating modes；
C.6 fuzzy clustering matrix is calculated：
Wherein, uhjRelative defects of j-th of sample to be clustered to h-th of standard condition are represented, calculation formula is as follows：
Wherein, ωiFor the weight of four characteristic parameters, value is ω=[0.3 0.15 0.25 0.2]；
C.7 according to the above results, all i are determinednewThe corresponding standard condition of section operating mode block.
Operating mode property parameters described in step (4) resolve rule and are defined as follows：
A. the general parameter for providing each standard condition is：The downtown operating mode and suburbs operating mode general parameter are
Average speed, dead time length, stroke distances；The outer suburbs operating mode and expressway operating mode general parameter be average speed,
Bend position, bend curvature；The mountainous region operating mode general parameter for average speed, ramp position, the gradient, length of grade, bend position,
Bend curvature；
B. the personalizing parameters for providing each standard condition are：Car resistance, transforming gear number, accelerator pedal are put down
Equal displacement, the accelerator pedal displacement average rate of change, brake pedal average displacement, the brake pedal displacement average rate of change；
C. in the operating mode property parameters, the gradient, length of grade are asked by the GPS module and the resolving of six axle sensor modules
, bend position and bend curvature are acquired by the GPS module and the resolving of steering moment acquisition module, and car resistance and vehicle are certainly
Body relating to parameters, is acquired by respective formula, remaining parameter is acquired by the acquisition of respective sensor module.
Compared with prior art, advantageous effect caused by technical scheme is：
(1) the driving information contribution for the vehicle that the present invention loads the system by each, it is complete can quickly to set up covering
The traffic information large database concept of region even if from far-off regions, as long as there is vehicle traveling, that is, have data that can look into, avoids professional people
The limitation that member surveys and draws on the spot, reduces the consumption of human and material resources, financial resources.It, can be timely meanwhile dependent on the refreshing of data
The information such as section weather condition, vehicle flowrate are provided.
(2) present invention is before travel, you can according to path planning, the newest road conditions for corresponding to place are downloaded from large database concept
Parameter with vehicle-mounted optimization module, carries out global optimization, avoids the characteristics of real-time optimization processing data amount is big, and time-consuming, carry
The optimization of fuel consumption rate is realized while high treatment effeciency.
(3) vehicle, driver are driven the personalizing parameters such as preference by the present invention and this generalization parameter of road conditions parameter is closed
And filter, it is stored separately, both ensure that the versatility of information in large database concept, avoided data redundancy, in turn ensured global optimization
The actual conditions of vehicle in itself can be combined to carry out, realize optimal scheme customization.
Description of the drawings
Fig. 1 is the system construction drawing of the present invention；
Fig. 2 is that flow chart is realized in data processing of the present invention；
Fig. 3 is the corresponding speed versus time curve of certain one stroke.
Specific embodiment
Technical solution of the present invention is described in further detail in the following with reference to the drawings and specific embodiments, it is described specific
Embodiment is only explained the present invention, is not intended to limit the invention.
As shown in Figure 1, the hybrid power energy management strategies global optimization system based on large database concept of the present invention, by remote
Journey database module and car-mounted terminal two parts composition, the car-mounted terminal includes data acquisition module, pending data stores
Module, hybrid power whole vehicle controller (HCU), CPU module and individuation data memory module.The data are adopted
Collection module, pending data memory module, CPU module and individuation data memory module are sequentially connected in series total by CAN
Line communicates；The CPU module is carried out data transmission with teledata library module by cellular network；In described
Central processor module is communicated with hybrid power whole vehicle controller (HCU) with CAN bus, sends the control information after optimization.
The data acquisition module mainly includes GPS module, accelerator pedal position acquisition module, brake pedal position acquisition
Module, steering moment acquisition module, transmission gear acquisition module, six axle sensor modules, engine running parameter acquisition mould
Block, battery charge state acquisition module.The GPS module is used to obtain vehicle position information, real-time speed, standard time, institute
Accelerator pedal position acquisition module is stated for obtaining accelerator pedal displacement and change in displacement rate, the brake pedal position acquisition
For obtaining brake pedal displacement and change in displacement rate, the steering moment acquisition module is used to obtain direction information module,
For obtaining real-time gear information, the six axle sensors module is used to obtain vehicle attitude the transmission gear acquisition module
Information, the engine running parameter acquisition module are used to obtain the real-time rotating speed of engine, power, torque, fuel consumption, institute
Battery charge state acquisition module is stated for obtaining real-time state-of-charge (SOC) value.Preferably, the data acquisition module sampling
At intervals of 1s.
The pending data memory module is used to storing the real time data of data collecting module collected, during storage according to when
Between order packing storage is carried out to data.
The effect of the CPU module：(1) after single stroke, to the number in pending data memory module
Two classes will be packaged as after information processing according to unpacking, and according to tentation data processing rule, one kind is to be driven with vehicle and driver
The related personalizing parameters of preference are sailed, are stored in individuation data memory module, one kind is general parameter only related with road conditions, is led to
It crosses cellular network and is uploaded to teledata library module, the personalizing parameters and the general parameter related with road conditions are referred to as
Operating mode property parameters；(2) before stroke starts, according to path planning, download correspondence position from teledata library module and correspond to the time
General parameter, and combine individuation data memory module in personalizing parameters, energy pipe is carried out by global optimization approach
The optimization of reason strategy calculates and (carries out global optimization to the energy control strategy of entire stroke), and foundation in the process of moving
The location information of GPS module acquisition sends control information to hybrid power whole vehicle controller in real time, so as to fulfill whole optimal.
The tentation data processing rule is realized with the following method：
(1) vehicle driving-cycle is divided into city operating mode and two major class of mountainous region operating mode.Wherein, the city operating mode is divided into
For downtown operating mode, suburbs operating mode, outer suburbs operating mode, expressway operating mode.The mountainous region operating mode refers to comprising continuous climbing for a long time, turns
The operating mode of one or more of curved, descending feature, data are stored according to operating mode classification after processing.
(2) as shown in Fig. 2, data processing includes three phases：Operating mode block division stage, operating mode block's attribute parameter calculation rank
Section, operating mode block's attribute parameter is packaged and memory phase.
(3) the operating mode block division stage is carried out by operating mode's switch controller, and the operating mode's switch controller input becomes
It measures as vehicle location, time, speed, engine power, car body obliqueness, output variable is corresponding operating mode classification；The operating mode is known
Other controller mainly includes three parts：One-dimensional Gaussian filter, block divider, cluster analysis device.
The car body obliqueness refers to the angle of front and rear axle midpoint line and horizontal plane, when front axle midpoint is higher than rear axle midpoint,
Inclination value is just, when front axle midpoint is less than rear axle midpoint, inclination value is negative, and the inclination value can pass through six axle sensor
Module obtains.
The operating mode block division is realized with the following method：
A. with the smooth speed versus time curve of one-dimensional Gaussian filter, unnecessary velocity disturbance is eliminated.
The one-dimensional Gaussian filter is as follows：
[w1 w2 w3]T (1)
w1、w2、w3The weight of respectively the 1st, the 2nd, the 3rd sampled point, w1+w2+w3=1；
The rate matrices formed to M sampled point：
[v1,v2,…vN-1,vN,vN+1,…vM] (2)
The speed v of n-th sampled point after filteringNFor：
vN=vN-1w1+vNw2+vN+1w3 (3)
B. operating mode block division is carried out to curve with the block divider.Specifically, the block divider division rule is：
B.1 idling (stopping) speed domain [0, v is defineds], i.e., travel speed is in [0, vs] in the range of be in idling for vehicle
The necessary condition of (stopping) state；
B.2 idling (stopping) the effective time region [Td is definedl, Tdh], i.e., travel speed is in [0, vs] in the range of continue
Time is in [Tdl, Tdh] necessary and sufficient condition of idling (stopping) state is in domain for vehicle；
B.3 define and at the uniform velocity travel acceleration domain [al, ah], i.e., vehicle acceleration is in [al, ah] in the range of at the uniform velocity to travel
The necessary condition of state；
B.4 define and at the uniform velocity travel the effective time region [Tyl, Tyh], i.e., acceleration is in [al, ah] in the range of duration
In [Tyl, Tyh] necessary and sufficient condition of at the uniform velocity state is in domain for vehicle；
B.5 the equal domain of average speed is definedI.e. when the average speed difference of two continuous blocks exists
When interior, it is believed that its average speed is equal；
B.6 operating mode block division is changed with time using speed as Primary Reference foundation, and thinks that average speed is equal
Continuous segment is same operating block, and the continuous segment that average speed does not wait is different operating mode blocks.Specifically, partiting step is：
B.6.1 using idling mode as segmentation section, complete stroke is divided into i sections (including idling operation sections), and records every section
The corresponding beginning and ending timeStart-stop position(p 1,2 ... is i)；For example, for such as Fig. 3 institutes
Show the Velocity Time variation diagram of stroke, can this section of stroke be divided into A1, A2, A3, A4, A5 sections as stated above, wherein A2, A4 are
Idling operation section；
B.6.2 corresponding acceleration is calculated the sampled point in each section(p 1,2 ... i, N 1,2 ... M, M are p sections
Middle sampling number), extract all j sections at the uniform velocity sections, and calculate each at the uniform velocity section average speed in the section(p 1,2 ... i, q are
1,2 ... j)；For example, for stroke as shown in Figure 3, it is 0-1,4-5,8-9,10-11 at the uniform velocity to travel section；
B.6.3 at the uniform velocity section terminal extends：To p sections, (p 1,2 ... all j sections at the uniform velocity sections in i), it is even that starting point extends to this
The starting point for the gear stage that is connected before fast section, jth section stop, which extends to gear stage thereafter, to be terminated, remaining section of stop is constant, each after extension
Section terminal position is denoted asBeginning and ending time is denoted asFor example, for as shown in Figure 3
Stroke, at the uniform velocity section 1-2 expand to 0-3, and 5-6 expands to 4-7, and 9-10 expands to 8-10, and 11-12 expands to 10-13；
B.6.4 all at the uniform velocity sections in i sections are reintegrated, it is specifically, average speed in adjacent at the uniform velocity section is equal
Section merges, and the idling operation section of midfeather is merged into same operating block together, and average speed in p sections is not waited
The at the uniform velocity section that is connected is split as different operating mode blocks, and the idling section not being merged is merged into connected previous at the uniform velocity section, and update is closed
And all i after the completion of splittingnewSection operating mode block beginning and ending time beStart stop bit is set to(pnewThe i for 1,2 ...new).For example, for stroke as shown in Figure 3, will at the uniform velocity section 0-3,4-7 with
And idling section A2, A4 is merged as operating mode block B1, at the uniform velocity section 8-10,10-13 will be split as operating mode block B2, B3, therefore, again
The operating mode block obtained after integration is B1, B2, B3.
By above step, the division of operating mode block is completed.
C. operating mode classification judgement is carried out to it by elementary cell of operating mode block using cluster analysis device.It specifically can be used as follows
Clustering algorithm：
C.1 each operating mode block be 1 sample to be clustered, inewA sample composition set to be clustered：
X={ x1,x2,…,xpnew,…,xinew} (4)
C.2 each sample to be clustered is represented with four characteristic parameters：
Wherein,For average speed, αpnewFor dead time ratio,For mean power,It is exhausted for car body obliqueness
To the average value of value；
C.3 i is usednew× 4 rank matrixes represent that each sample character pair parameter to be clustered is：
C.4 since characteristic parameter dimension is different, each characteristic parameter is normalized, obtains characteristic parameter rule
It formats matrix：
Wherein, rijFor the normalized value of the ith feature parameter of j-th of sample to be clustered；
C.5 by foregoing five standard conditions (i.e. downtown operating mode, suburbs operating mode, outer suburbs operating mode, expressway operating mode, hill path
Operating mode) four characteristic parameters normalized value as cluster centre matrix：
Wherein, sihFor the normalized value of the ith feature parameter of h class standard operating modes；
C.6 fuzzy clustering matrix is calculated：
Wherein, uhjRelative defects of j-th of sample to be clustered to h-th of standard condition are represented, calculation formula is as follows：
Wherein, ωiFor the weight of four characteristic parameters, value is ω=[0.3 0.15 0.25 0.2]；
C.7 according to the above results, it may be determined that all inewThe corresponding standard condition of section operating mode block.
The clustering algorithm can make choice use according to actual needs, it is not limited to above-mentioned algorithm.
(4) according to the corresponding standard condition of the operating mode block, its corresponding operating mode property parameters is resolved.Specifically, it is described
Operating mode property parameters resolve rule and can be defined as follows：
A. the general parameter for providing each standard condition is：
A.1 the downtown operating mode and suburbs operating mode general parameter be average speed, dead time length, stroke away from
From；
A.2 the outer suburbs operating mode and expressway operating mode general parameter are average speed, bend position, bend curvature；
A.3 the mountainous region operating mode general parameter is average speed, ramp position, the gradient, length of grade, bend position, bend song
Rate；
B. the personalizing parameters for providing each standard condition are：Car resistance, transforming gear number, accelerator pedal are put down
Equal displacement, the accelerator pedal displacement average rate of change, brake pedal average displacement, the brake pedal displacement average rate of change；
C. in the operating mode property parameters, the gradient, length of grade are asked by the GPS module and the resolving of six axle sensor modules
, bend position and bend curvature are acquired by the GPS module and the resolving of steering moment acquisition module, and car resistance and vehicle are certainly
Body relating to parameters can be acquired by respective formula, remaining parameter can be gathered by respective sensor module and acquired.
(5) after the completion of the operating mode property parameters resolve, the general parameter is transmitted to teledata library module
It is stored, the personalizing parameters is transmitted to individuation data memory module and are stored.Specifically, packetization rules
It can be implemented as described below：
A. general parameter packetization rules are as follows：
| Operating mode is numbered | Operating mode block start position | Operating mode block final position | Time | Data segment | End mark |
Operating mode is numbered：Mountainous region (0x00) downtown (0x01) suburbs (0x02) outer suburbs (0x03) is at a high speed (0x04).
Data segment described in B.A is as follows according to the different compositions of operating mode：
B.1 downtown operating mode, suburbs floor data section：
| Average speed | Decollator | Dead time length | Decollator | Stroke distances | End mark |
B.2 outer suburbs operating mode, expressway floor data section：
B.3 mountainous region floor data section：
C. personalizing parameters packetization rules are as follows：
The operating mode property parameters packetization rules can modify according to actual needs.
(6) the teledata library module unpacks the general parameter received, and new data and legacy data are carried out pair
After being handled than integration, refresh data with the continuous accumulation of data, will form the work information of the full region of covering, and to more
A car-mounted terminal transmits the integration of data, can improve the accuracy to the prediction of same a road section operating mode.
Although the function and the course of work of the present invention are described above in conjunction with attached drawing, the invention is not limited in
Above-mentioned concrete function and the course of work, above-mentioned specific embodiment is only schematical rather than restricted, ability
The those of ordinary skill in domain is not departing from present inventive concept and scope of the claimed protection situation under the enlightenment of the present invention
Under, many forms can also be made, these are belonged within the protection of the present invention.
Claims (8)
1. a kind of hybrid power energy management strategies global optimization system based on large database concept, which is characterized in that by remotely counting
Formed according to library module and car-mounted terminal two parts, the car-mounted terminal include data acquisition module, pending data memory module,
Hybrid power whole vehicle controller, CPU module, individuation data memory module；
The data acquisition module, pending data memory module, CPU module and individuation data memory module according to
The secondary CAN bus that is connected in series through communicates；The CPU module is carried out with teledata library module by cellular network
Data transmission；The CPU module is communicated with hybrid power whole vehicle controller with CAN bus, after sending optimization
Control information；
The data acquisition module include GPS module, accelerator pedal position acquisition module, brake pedal position acquisition module, turn
To torque acquisition module, transmission gear acquisition module, six axle sensor modules, engine running parameter acquisition module, battery
State-of-charge acquisition module；The GPS module for obtaining vehicle position information, real-time speed, standard time, step on by the acceleration
For obtaining accelerator pedal displacement and change in displacement rate, the brake pedal position acquisition module is used for Board position acquisition module
Brake pedal displacement and change in displacement rate are obtained, the steering moment acquisition module is used to obtain direction information, the speed change
Device gear acquisition module is for obtaining real-time gear information, and the six axle sensors module is for obtaining vehicle-posture information, institute
Engine running parameter acquisition module is stated for obtaining the real-time rotating speed of engine, power, torque, fuel consumption, the battery
State-of-charge acquisition module is used to obtain real-time state of charge；
The pending data memory module is used to storing the real time data of data collecting module collected, and when storage is suitable according to the time
Ordered pair data carry out packing storage；
The effect of the CPU module：(1) after single stroke, to the data solution in pending data memory module
Bag, and two classes will be packaged as after information processing according to tentation data processing rule, one kind is to be driven partially with vehicle and driver
Good related personalizing parameters are stored in individuation data memory module, and one kind is general parameter only related with road conditions, passes through bee
Nest network is uploaded to teledata library module, and the personalizing parameters and the general parameter related with road conditions are referred to as operating mode
Property parameters；(2) before stroke starts, according to path planning, download correspondence position from teledata library module and correspond to the logical of time
With parameter, and the personalizing parameters in individuation data memory module are combined, pass through global optimization approach and carry out energy management plan
The optimization calculating omited, and the location information gathered in the process of moving according to GPS module sends control information to mixing and moves in real time
Power entire car controller, so as to fulfill whole optimal.
2. the hybrid power energy management strategies global optimization system according to claim 1 based on large database concept, special
Sign is that the tentation data processing rule is realized with the following method：
(1) vehicle driving-cycle is divided into city operating mode and two major class of mountainous region operating mode；Wherein, the city operating mode is divided into as city
Center operating mode, suburbs operating mode, outer suburbs operating mode, expressway operating mode, the mountainous region operating mode refer to comprising continuous climbing for a long time, turn,
The operating mode of one or more of descending feature, data are stored according to operating mode classification after processing；
(2) data processing includes three phases：Operating mode block division stage, operating mode block's attribute parameter calculation stage, operating mode block's attribute
Parameter is packaged and memory phase；
(3) the operating mode block division stage is carried out by operating mode's switch controller, and the operating mode's switch controller input variable is
Vehicle location, time, speed, engine power, car body obliqueness, output variable are corresponding operating mode classification；The operating mode's switch control
Device processed mainly includes three parts：One-dimensional Gaussian filter, block divider, cluster analysis device；
The car body obliqueness refers to the angle of front and rear axle midpoint line and horizontal plane, when front axle midpoint is higher than rear axle midpoint, inclination angle
It is worth for just, when front axle midpoint is less than rear axle midpoint, inclination value is negative, and the inclination value can pass through the six axle sensors module
It obtains；
(4) according to the corresponding standard condition of the operating mode block, its corresponding operating mode property parameters is resolved；
(5) after the completion of the operating mode property parameters resolve, the general parameter is transmitted to teledata library module and is carried out
Storage, the personalizing parameters are transmitted to individuation data memory module and are stored；
(6) the teledata library module unpacks the general parameter received, and new data with legacy data compare whole
After conjunction processing, refresh data with the continuous accumulation of data, will form the work information of the full region of covering, and to vehicle-mounted end
The integration of data is transmitted at end, improves the accuracy to the prediction of same a road section operating mode.
3. the hybrid power energy management strategies global optimization system according to claim 2 based on large database concept, special
Sign is that the division of operating mode block described in step (3) is realized with the following method：
A. with the smooth speed versus time curve of one-dimensional Gaussian filter, unnecessary velocity disturbance is eliminated；
B. operating mode block division is carried out to curve with the block divider；
C. operating mode classification judgement is carried out to it by elementary cell of operating mode block using cluster analysis device.
4. the hybrid power energy management strategies global optimization system according to claim 3 based on large database concept, special
Sign is that one-dimensional Gaussian filter is as follows described in step A：
[w1 w2 w3]T
w1、w2、w3The weight of respectively the 1st, the 2nd, the 3rd sampled point, w1+w2+w3=1；
The rate matrices formed to M sampled point
[v1,v2,…vN-1,vN,vN+1,…vM]
The speed v of n-th sampled point after filteringNFor
vN=vN-1w1+vNw2+vN+1w3
5. the hybrid power energy management strategies global optimization system according to claim 3 based on large database concept, special
Sign is that block divider division rule is described in step B：
B.1 idling (stopping) speed domain [0, v is defineds], i.e., travel speed is in [0, vs] in the range of be in idling for vehicle and (stop
Only) the necessary condition of state；
B.2 idling (stopping) the effective time region [Td is definedl, Tdh], i.e., travel speed is in [0, vs] in the range of duration
In [Tdl, Tdh] necessary and sufficient condition of idling (stopping) state is in domain for vehicle；
B.3 define and at the uniform velocity travel acceleration domain [al, ah], i.e., vehicle acceleration is in [al, ah] in the range of at the uniform velocity transport condition
Necessary condition；
B.4 define and at the uniform velocity travel the effective time region [Tyl, Tyh], i.e., acceleration is in [al, ah] in the range of duration exist
[Tyl, Tyh] necessary and sufficient condition of at the uniform velocity state is in domain for vehicle；
B.5 the equal domain of average speed is definedI.e. when the average speed difference of two continuous blocks existsWhen interior,
Think that its average speed is equal；
B.6 operating mode block division is changed with time using speed as Primary Reference foundation, and thinks equal continuous of average speed
Section is same operating block, and the continuous segment that average speed does not wait is different operating mode blocks.
6. the hybrid power energy management strategies global optimization system according to claim 5 based on large database concept, special
Sign is that B.6 detailed process is step：
B.6.1 using idling mode as segmentation section, complete stroke is divided into i sections (including idling operation sections), and records every section of correspondence
Beginning and ending timeStart-stop position(p 1,2 ... is i)；
B.6.2 corresponding acceleration is calculated the sampled point in each section(p 1,2 ... i, N 1,2 ... M, M are to be sampled in p sections
Points), extract all j sections at the uniform velocity sections, and calculate each at the uniform velocity section average speed in the section(p 1,2 ... i, q 1,2 ...
j)；
B.6.3 at the uniform velocity section terminal extends：To p sections (p 1,2 ... in i) all j sections at the uniform velocity section, starting points extend to the at the uniform velocity section
The starting point of preceding connected gear stage, jth section stop, which extends to gear stage thereafter, to be terminated, remaining section of stop is constant, is risen for each section after extension
Dead-centre position is denoted asBeginning and ending time is denoted as
B.6.4 all at the uniform velocity sections in i sections are reintegrated, specifically, by average speed equal segments in adjacent at the uniform velocity section into
Row merges, and the idling operation section of midfeather is merged into same operating block together, is connected what average speed in p sections did not waited
At the uniform velocity section is split as different operating mode blocks, and the idling section not being merged is merged into connected previous at the uniform velocity section, and update, which merges, tears open
All i after the completion of pointnewSection operating mode block beginning and ending time beStart stop bit is set to(pnewThe i for 1,2 ...new)。
7. the hybrid power energy management strategies global optimization system according to claim 3 based on large database concept, special
Sign is, the clustering algorithm used in step C：
C.1 each operating mode block be 1 sample to be clustered, inewA sample composition set to be clustered：
X={ x1,x2,…,xpnew,…,xinew}
C.2 each sample to be clustered is represented with four characteristic parameters：
<mrow>
<msub>
<mi>x</mi>
<mrow>
<mi>p</mi>
<mi>n</mi>
<mi>e</mi>
<mi>w</mi>
</mrow>
</msub>
<mo>=</mo>
<mrow>
<mo>(</mo>
<msub>
<mover>
<mi>v</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>p</mi>
<mi>n</mi>
<mi>e</mi>
<mi>w</mi>
</mrow>
</msub>
<mo>,</mo>
<msub>
<mi>&alpha;</mi>
<mrow>
<mi>p</mi>
<mi>n</mi>
<mi>e</mi>
<mi>w</mi>
</mrow>
</msub>
<mo>,</mo>
<msub>
<mover>
<mi>P</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>p</mi>
<mi>n</mi>
<mi>e</mi>
<mi>w</mi>
</mrow>
</msub>
<mo>,</mo>
<msub>
<mover>
<mi>a</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>p</mi>
<mi>n</mi>
<mi>e</mi>
<mi>w</mi>
</mrow>
</msub>
<mo>,</mo>
<mo>)</mo>
</mrow>
</mrow>
Wherein,For average speed, αpnewFor dead time ratio,For mean power,For car body obliqueness absolute value
Average value；
C.3 i is usednew× 4 rank matrixes represent that each sample character pair parameter to be clustered is：
<mrow>
<mi>X</mi>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<msub>
<mover>
<mi>v</mi>
<mo>&OverBar;</mo>
</mover>
<mn>1</mn>
</msub>
</mtd>
<mtd>
<msub>
<mover>
<mi>v</mi>
<mo>&OverBar;</mo>
</mover>
<mn>2</mn>
</msub>
</mtd>
<mtd>
<mn>...</mn>
</mtd>
<mtd>
<msub>
<mover>
<mi>v</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>i</mi>
<mi>n</mi>
<mi>e</mi>
<mi>w</mi>
</mrow>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>&alpha;</mi>
<mn>1</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>&alpha;</mi>
<mn>2</mn>
</msub>
</mtd>
<mtd>
<mn>...</mn>
</mtd>
<mtd>
<msub>
<mi>&alpha;</mi>
<mrow>
<mi>i</mi>
<mi>n</mi>
<mi>e</mi>
<mi>w</mi>
</mrow>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mover>
<mi>P</mi>
<mo>&OverBar;</mo>
</mover>
<mn>1</mn>
</msub>
</mtd>
<mtd>
<msub>
<mover>
<mi>P</mi>
<mo>&OverBar;</mo>
</mover>
<mn>2</mn>
</msub>
</mtd>
<mtd>
<mn>...</mn>
</mtd>
<mtd>
<msub>
<mover>
<mi>P</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>i</mi>
<mi>n</mi>
<mi>e</mi>
<mi>w</mi>
</mrow>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mover>
<mi>a</mi>
<mo>&OverBar;</mo>
</mover>
<mn>1</mn>
</msub>
</mtd>
<mtd>
<msub>
<mover>
<mi>a</mi>
<mo>&OverBar;</mo>
</mover>
<mn>2</mn>
</msub>
</mtd>
<mtd>
<mn>...</mn>
</mtd>
<mtd>
<msub>
<mover>
<mi>a</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>i</mi>
<mi>n</mi>
<mi>e</mi>
<mi>w</mi>
</mrow>
</msub>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
C.4 since characteristic parameter dimension is different, each characteristic parameter is normalized, obtains characteristic parameter normalization
Matrix：
<mrow>
<mi>R</mi>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<msub>
<mi>r</mi>
<mn>11</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>r</mi>
<mn>12</mn>
</msub>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<msub>
<mi>r</mi>
<mrow>
<mn>1</mn>
<mi>i</mi>
<mi>n</mi>
<mi>e</mi>
<mi>w</mi>
</mrow>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>r</mi>
<mn>21</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>r</mi>
<mn>22</mn>
</msub>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>r</mi>
<mn>31</mn>
</msub>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>r</mi>
<mn>41</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>r</mi>
<mn>42</mn>
</msub>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<msub>
<mi>r</mi>
<mrow>
<mn>4</mn>
<mi>i</mi>
<mi>n</mi>
<mi>e</mi>
<mi>w</mi>
</mrow>
</msub>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>=</mo>
<msub>
<mi>r</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
</mrow>
Wherein, rijFor the normalized value of the ith feature parameter of j-th of sample to be clustered；
C.5 by foregoing downtown operating mode, suburbs operating mode, outer suburbs operating mode, expressway operating mode, hill path operating mode four characteristic parameters
Normalized value is as cluster centre matrix：
<mrow>
<mi>S</mi>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<msub>
<mi>s</mi>
<mn>11</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>s</mi>
<mn>12</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>s</mi>
<mn>13</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>s</mi>
<mn>14</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>s</mi>
<mn>15</mn>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>s</mi>
<mn>21</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>s</mi>
<mn>22</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>s</mi>
<mn>23</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>s</mi>
<mn>24</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>s</mi>
<mn>25</mn>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>s</mi>
<mn>31</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>s</mi>
<mn>32</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>s</mi>
<mn>33</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>s</mi>
<mn>34</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>s</mi>
<mn>35</mn>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>s</mi>
<mn>41</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>s</mi>
<mn>42</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>s</mi>
<mn>43</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>s</mi>
<mn>44</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>s</mi>
<mn>45</mn>
</msub>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>=</mo>
<msub>
<mi>s</mi>
<mrow>
<mi>i</mi>
<mi>h</mi>
</mrow>
</msub>
</mrow>
Wherein, sihFor the normalized value of the ith feature parameter of h class standard operating modes；
C.6 fuzzy clustering matrix is calculated：
<mrow>
<mi>U</mi>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<msub>
<mi>u</mi>
<mn>11</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>u</mi>
<mn>12</mn>
</msub>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<msub>
<mi>u</mi>
<mrow>
<mn>1</mn>
<mi>i</mi>
<mi>n</mi>
<mi>e</mi>
<mi>w</mi>
</mrow>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>u</mi>
<mn>21</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>u</mi>
<mn>22</mn>
</msub>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<msub>
<mi>u</mi>
<mrow>
<mn>2</mn>
<mi>i</mi>
<mi>n</mi>
<mi>e</mi>
<mi>w</mi>
</mrow>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>u</mi>
<mn>51</mn>
</msub>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<mo>...</mo>
</mtd>
<mtd>
<msub>
<mi>u</mi>
<mrow>
<mn>5</mn>
<mi>i</mi>
<mi>n</mi>
<mi>e</mi>
<mi>w</mi>
</mrow>
</msub>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>=</mo>
<msub>
<mi>u</mi>
<mrow>
<mi>h</mi>
<mi>j</mi>
</mrow>
</msub>
</mrow>
Wherein, uhjRelative defects of j-th of sample to be clustered to h-th of standard condition are represented, calculation formula is as follows：
<mrow>
<msub>
<mi>u</mi>
<mrow>
<mi>h</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mrow>
<msubsup>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mn>5</mn>
</msubsup>
<mfrac>
<mrow>
<msubsup>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mn>4</mn>
</msubsup>
<msup>
<mrow>
<mo>&lsqb;</mo>
<msub>
<mi>&omega;</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>r</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>s</mi>
<mrow>
<mi>i</mi>
<mi>h</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
<mrow>
<msubsup>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mn>4</mn>
</msubsup>
<msup>
<mrow>
<mo>&lsqb;</mo>
<msub>
<mi>&omega;</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>r</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>s</mi>
<mrow>
<mi>i</mi>
<mi>k</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</mfrac>
</mrow>
</mfrac>
</mrow>
Wherein, ωiFor the weight of four characteristic parameters, value is ω=[0.3 0.15 0.25 0.2]；
C.7 according to the above results, all i are determinednewThe corresponding standard condition of section operating mode block.
8. the hybrid power energy management strategies global optimization system according to claim 2 based on large database concept, special
Sign is that operating mode property parameters described in step (4) resolve rule and are defined as follows：
A. the general parameter for providing each standard condition is：The downtown operating mode and suburbs operating mode general parameter are average
Speed, dead time length, stroke distances；The outer suburbs operating mode and expressway operating mode general parameter are average speed, bend
Position, bend curvature；The mountainous region operating mode general parameter is average speed, ramp position, the gradient, length of grade, bend position, bend
Curvature；
B. the personalizing parameters for providing each standard condition are：Car resistance, transforming gear number, accelerator pedal average bit
Shifting, the accelerator pedal displacement average rate of change, brake pedal average displacement, the brake pedal displacement average rate of change；
C. in the operating mode property parameters, the gradient, length of grade are acquired by the GPS module and the resolving of six axle sensor modules, curved
Road position and bend curvature are acquired by the GPS module and the resolving of steering moment acquisition module, and car resistance is joined with vehicle itself
Number is related, is acquired by respective formula, remaining parameter is acquired by the acquisition of respective sensor module.
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