CN111591279B - Plug-in hybrid electric vehicle battery power track planning method and system - Google Patents

Plug-in hybrid electric vehicle battery power track planning method and system Download PDF

Info

Publication number
CN111591279B
CN111591279B CN202010312714.1A CN202010312714A CN111591279B CN 111591279 B CN111591279 B CN 111591279B CN 202010312714 A CN202010312714 A CN 202010312714A CN 111591279 B CN111591279 B CN 111591279B
Authority
CN
China
Prior art keywords
road
soc
predicted
speed
gradient
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202010312714.1A
Other languages
Chinese (zh)
Other versions
CN111591279A (en
Inventor
周维
翟浩然
张宁峰
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hunan University
Original Assignee
Hunan University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hunan University filed Critical Hunan University
Priority to CN202010312714.1A priority Critical patent/CN111591279B/en
Publication of CN111591279A publication Critical patent/CN111591279A/en
Application granted granted Critical
Publication of CN111591279B publication Critical patent/CN111591279B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W20/00—Control systems specially adapted for hybrid vehicles
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B60W40/06—Road conditions
    • B60W40/076—Slope angle of the road
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/105—Speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions

Landscapes

  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Human Computer Interaction (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)
  • Hybrid Electric Vehicles (AREA)

Abstract

The invention discloses a method and a system for planning battery power tracks of a plug-in hybrid electric vehicle, wherein the method comprises the following steps: acquiring a predicted vehicle speed and a road gradient on a driving path based on an ITS system and a navigation system; respectively clustering the predicted vehicle speed and the road gradient based on an ordered sample clustering algorithm; merging the clustered predicted vehicle speed and road gradient, dividing the driving path into a plurality of sections, wherein the predicted vehicle speed and the road gradient in each section are consistent in characteristics; and planning a vehicle battery SoC track according to the multiple sections of road sections and the corresponding predicted vehicle speed and road slope. Clustering according to the predicted vehicle speed and road gradient, merging to divide the path into a plurality of sections, and generating an SoC reference track according to the energy requirements of different sections; compared with the traditional global dynamic programming algorithm or neural network and the like, the SoC track close to the global optimal has lower calculation amount, can be refreshed in real time according to the traffic change condition, has high robustness, and has more energy-saving potential when being applied to energy management control.

Description

Plug-in hybrid electric vehicle battery power track planning method and system
Technical Field
The invention relates to the technical field of energy management of hybrid electric vehicles, in particular to a method and a system for planning a battery power track of a plug-in hybrid electric vehicle.
Background
The plug-in hybrid electric vehicle (PHEV) is a new energy vehicle, combines the advantages of a pure electric vehicle and the advantages of a traditional hybrid electric vehicle, and greatly solves the problem of driving range anxiety under the condition of ensuring the electric driving advantages of the vehicle. The current PHEV mainly comprises an engine, a generator, a driving motor, a storage battery combination control system and other components, when the electric quantity level of the storage battery is higher, a vehicle can run in a pure electric mode, and when the electric quantity level is lower, the engine can intervene in work and can charge the battery; the battery can be charged through the charging pile.
With the development of Intelligent Transportation Systems (ITS) and car networking, it becomes possible to accurately obtain future road and traffic information. The method comprises the steps of planning a global electric quantity (SoC) track of a battery by fusing predicted future road and traffic information in an energy management system of the PHEV, tracking the electric quantity track through algorithms such as model predictive control or adaptive ECMS and the like, controlling the output power of an engine and the battery, realizing high-efficiency work of the system and effectively reducing the fuel consumption of the automobile.
Chinese patent CN2015108863352, for example, discloses a "system and method of controlling a hybrid vehicle" which discloses predicting a future speed of the vehicle from information relating to a travel route and then deriving an SoC trajectory for the travel route for the predicted future speed. According to the scheme, the SoC track line of the full stroke needs to be solved by using a dynamic programming algorithm, the calculation amount is large, the real-time performance is poor, the ITS cannot acquire the change details of the vehicle speed track, the robustness for solving the SoC track line and tracking by using the dynamic programming algorithm is low, and the control effect is poor.
Disclosure of Invention
The invention provides a method and a system for planning battery power tracks of a plug-in hybrid electric vehicle, which are used for solving the problems of large calculated amount and low robustness of an SoC track planning algorithm in the prior art.
In a first aspect, a method for planning a battery power track of a plug-in hybrid electric vehicle is provided, which includes:
acquiring a predicted vehicle speed and a road gradient on a driving path based on an ITS system and a navigation system;
respectively clustering the predicted vehicle speed and the road gradient based on an ordered sample clustering algorithm;
merging the clustered predicted vehicle speed and road gradient, and further dividing the driving path into a plurality of sections, wherein the predicted vehicle speed and the road gradient characteristics in each section are consistent; the predicted vehicle speed and road gradient characteristics in each section of road section are consistent, namely: the predicted speed in each section of road section is in the same predicted speed class, and the road gradient in each section of road section is in the same road gradient class;
and planning a vehicle battery SoC track according to the multiple sections of road sections and the corresponding predicted vehicle speed and road slope.
Further, the step of respectively clustering the predicted vehicle speed and the road gradient based on the ordered sample clustering algorithm comprises the following steps of:
obtaining an ordered sample sequence { x ] of predicted speed/road slope1,x2,x3,…,xnAnd the number k of clusters required, where xnFor the nth predicted speed/road grade sample;
defining a predicted speed/road grade class as G (i, j) comprising a data sequence { x }i,xi+1,…xjH, the diameter D (i, j) of the predicted speed/road grade class G (i, j) is defined as:
Figure BDA0002458448180000021
wherein the content of the first and second substances,
Figure BDA0002458448180000022
to predict the center of the speed/road gradient class G (i, j), and:
Figure BDA0002458448180000023
an objective function e [ p (n, k) ] is set, which can be expressed as:
Figure BDA0002458448180000024
wherein, { i }1,i2,…,ik,ik+1Is the optimal set of segmentation points for the predicted speed/road slope sample sequence, ikIs the k-th division point, and i1And ik+1The first and last samples of the predicted speed/road grade sample sequence, respectively;
the value of the objective function e [ p (n, k) ] is minimized by dynamic programming solution, resulting in k predicted speed/road grade classes.
Further, the number k of clusters required for predicting the speed/road gradient sample sequence is determined by self-adaptation, and the method specifically comprises the following steps:
Drindicating predicted speed/road at some value of kSum of distances of any two points in the slope class r:
Figure BDA0002458448180000025
will DrStandardized and recorded as the standard diameter Wk：
Figure BDA0002458448180000026
Defining the amount of Gapn(k) Comprises the following steps:
Figure BDA0002458448180000031
wherein the content of the first and second substances,
Figure BDA0002458448180000032
is log (W)k) The expectation of the reference distribution under n predicted speed/road gradient samples is calculated as: taking the random numbers with the same quantity as the original predicted speed/road gradient samples within the range of the maximum value and the minimum value of the predicted speed/road gradient samples, solving the standard diameter of the random numbers and marking the standard diameter as the standard diameter
Figure BDA0002458448180000033
Repeating the process for B times
Figure BDA0002458448180000034
The average value can be obtained
Figure BDA0002458448180000035
Definition log (W)k) The standard deviation of (a) is sd (k):
Figure BDA0002458448180000036
definition of
Figure BDA0002458448180000037
Analog error definition s ink：
Figure BDA0002458448180000038
Selecting the gas satisfying Gap (k) being equal to or more than Gap (k +1) -sk+1Is used as the optimal predicted speed/road gradient sample sequence cluster number.
The number k of the clusters is determined by the self-adaptive method, so that the method can adapt to different terrain and vehicle speed track characteristics, different optimal cluster numbers are determined according to data with different characteristics, a clustering result can be more reasonable, and a finally planned SoC track is further closer to a globally optimal SoC track.
Further, the step of planning the SoC track of the vehicle battery according to the multiple road sections and the corresponding predicted vehicle speeds and road slopes specifically includes:
obtaining the average road resistance of each road section according to the predicted vehicle speed and road gradient corresponding to each road section, and predicting the SoC track descending or ascending on the corresponding road section according to the positive and negative of the average road resistance;
for the road section with rising SoC, the slope k of SoC track on each road sectionu：
Figure BDA0002458448180000039
Wherein, Δ SoCuFor SoC increments on corresponding SoC ascending sections, luThe mileage of the corresponding SoC ascending road section is obtained;
for the road sections with the reduced SoC, one road section is selected as a standard road section, and a standard slope k is calculatedstAnd slope k of each sub-segmentd：
Figure BDA0002458448180000041
Wherein, SoC0For initial battery charge, SoCminThe battery charge at the end of the trip, sendTotal mileage,/dFor mileage corresponding to SoC descending road section, Δ snThe distance difference between the standard road section and the corresponding SoC descending road section under the condition that the SoC variation is the same;
and combining the SoC tracks of all the road sections according to the relative positions on the position domain to obtain the SoC track corresponding to the whole driving path.
Further, said Δ snThe formula is as follows;
Figure BDA0002458448180000042
wherein the content of the first and second substances,
Figure BDA0002458448180000043
is the average road resistance corresponding to the standard road segment,
Figure BDA0002458448180000044
the average road resistance of the corresponding SoC descending road section is obtained;
the Δ SoCuObtained by the following formula:
Figure BDA0002458448180000045
wherein eta istIn order to obtain a coefficient of efficiency of conversion of electric energy,
Figure BDA0002458448180000046
to correspond to the average road resistance of the SoC up link,
Figure BDA0002458448180000047
is the average value of the terminal voltage of the battery, QnomThe rated capacity of the battery.
In a second aspect, a plug-in hybrid electric vehicle battery power trajectory planning system includes:
the vehicle speed and gradient acquisition module is used for acquiring the predicted vehicle speed and road gradient on the driving path based on the ITS system and the navigation system;
the road section segmentation module is used for respectively clustering the predicted vehicle speed and the road gradient based on an ordered sample clustering algorithm, merging the clustered predicted vehicle speed and road gradient, and further segmenting the driving path into a plurality of sections of road sections, wherein the predicted vehicle speed and the road gradient in each section of road section are the same;
and the SoC track planning module is used for planning the SoC track of the vehicle battery according to the multiple sections of road sections and the corresponding predicted vehicle speed and road slope.
Further, the step of respectively clustering the predicted vehicle speed and the road gradient based on the ordered sample clustering algorithm comprises the following steps of:
obtaining an ordered sample sequence { x ] of predicted speed/road slope1,x2,x3,…,xnAnd the number k of clusters required, where xnFor the nth predicted speed/road grade sample;
defining a predicted speed/road grade class as G (i, j) comprising a data sequence { x }i,xi+1,…xjH, the diameter D (i, j) of the predicted speed/road grade class G (i, j) is defined as:
Figure BDA0002458448180000051
wherein the content of the first and second substances,
Figure BDA0002458448180000052
to predict the center of the speed/road gradient class G (i, j), and:
Figure BDA0002458448180000053
an objective function e [ p (n, k) ] is set, which can be expressed as:
Figure BDA0002458448180000054
wherein, { i }1,i2,…,ik,ik+1Is the optimal set of segmentation points for the predicted speed/road slope sample sequence, ikIs the k-th division point, and i1And ik+1The first and last samples of the predicted speed/road grade sample sequence, respectively;
the value of the objective function e [ p (n, k) ] is minimized by dynamic programming solution, resulting in k predicted speed/road grade classes.
Further, the number k of clusters required for predicting the speed/road gradient sample sequence is determined by self-adaptation, and the method specifically comprises the following steps:
Drrepresents the sum of the distances of any two points within the predicted speed/road gradient class r at some value of k:
Figure BDA0002458448180000055
will DrStandardized and recorded as the standard diameter Wk：
Figure BDA0002458448180000056
Defining the amount of Gapn(k) Comprises the following steps:
Figure BDA0002458448180000057
wherein the content of the first and second substances,
Figure BDA0002458448180000058
is log (W)k) The expectation of the reference distribution under n predicted speed/road gradient samples is calculated as: taking the random numbers with the same quantity as the original predicted speed/road gradient samples within the range of the maximum value and the minimum value of the predicted speed/road gradient samples, solving the standard diameter of the random numbers and marking the standard diameter as the standard diameter
Figure BDA0002458448180000059
Repeating the process for B times
Figure BDA00024584481800000510
The average value can be obtained
Figure BDA0002458448180000061
Definition log (W)k) The standard deviation of (a) is sd (k):
Figure BDA0002458448180000062
definition of
Figure BDA0002458448180000063
Analog error definition s ink：
Figure BDA0002458448180000064
Selecting the gas satisfying Gap (k) being equal to or more than Gap (k +1) -sk+1Is used as the optimal predicted speed/road gradient sample sequence cluster number.
Further, the step of planning the SoC track of the vehicle battery according to the multiple road sections and the corresponding predicted vehicle speeds and road slopes specifically includes:
obtaining the average road resistance of each road section according to the predicted vehicle speed and road gradient corresponding to each road section, and predicting the SoC track descending or ascending on the corresponding road section according to the positive and negative of the average road resistance;
for the road section with rising SoC, the slope k of SoC track on each road sectionu：
Figure BDA0002458448180000065
Wherein, Δ SoCuFor SoC increments on corresponding SoC ascending sections, luThe mileage of the corresponding SoC ascending road section is obtained;
for the road sections with the reduced SoC, one road section is selected as a standard road section, and a standard slope k is calculatedstAnd slope k of each sub-segmentd：
Figure BDA0002458448180000066
Wherein, SoC0For initial battery charge, SoCminThe battery charge at the end of the trip, sendTotal mileage,/dFor mileage corresponding to SoC descending road section, Δ snThe distance difference between the standard road section and the corresponding SoC descending road section under the condition that the SoC variation is the same;
and combining the SoC tracks of all the road sections according to the relative positions on the position domain to obtain the SoC track corresponding to the whole driving path.
Further, said Δ snThe formula is as follows;
Figure BDA0002458448180000071
wherein the content of the first and second substances,
Figure BDA0002458448180000072
is the average road resistance corresponding to the standard road segment,
Figure BDA0002458448180000073
the average road resistance of the corresponding SoC descending road section is obtained;
the Δ SoCuObtained by the following formula:
Figure BDA0002458448180000074
wherein eta istIn order to obtain a coefficient of efficiency of conversion of electric energy,
Figure BDA0002458448180000075
to correspond to the average road resistance of the SoC up link,
Figure BDA0002458448180000076
is the average value of the terminal voltage of the battery, QnomThe rated capacity of the battery.
Advantageous effects
The invention provides a method and a system for planning battery power tracks of a plug-in hybrid electric vehicle, which can be used for rapidly planning SoC tracks according to future travel information acquired from an ITS system and a navigation system. The SoC track planning is carried out according to energy demand characteristics of different road sections, the speed and the gradient are direct factors influencing the energy demand characteristics of the road sections, so that the vehicle speed and the gradient of the road are clustered according to two characteristics of predicted speed and gradient of the road, then the driving path is combined to be divided into a plurality of sections of road sections with different energy demands, and the SoC reference track is generated based on the plurality of sections of road sections with different energy demands. Compared with the existing scheme (the global dynamic programming algorithm generates the optimal track or a neural network and the like), the algorithm in the scheme has lower calculation amount and is closer to the globally optimal SoC track than the SoC reference track design method based on the general simple rule; due to the fact that the calculated amount is low, refreshing can be conducted at a certain frequency according to traffic change conditions, and the method is good in instantaneity and high in robustness. The obtained SoC track planning result can be applied to an energy management algorithm, so that a lower-layer optimization control algorithm (a model prediction control algorithm, a self-adaptive equivalent fuel consumption minimum strategy and the like) tracks the SoC track, and the efficient distribution of energy is realized.
Drawings
Fig. 1 is a flowchart of a method for planning a battery power trajectory of a plug-in hybrid electric vehicle according to an embodiment of the present invention;
fig. 2 is a schematic diagram of SoC trace planning of two single slopes of a SoC trace descending segment according to an embodiment of the present invention;
fig. 3 is a schematic diagram of SoC trace planning with different slopes in succession for a SoC trace descending segment according to an embodiment of the present invention;
fig. 4 is a schematic diagram of SoC trace planning including a single SoC trace rising segment and having the same slope of SoC trace falling segment according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an SoC trajectory plan under an integrated condition according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a battery power trajectory planning system of a plug-in hybrid electric vehicle according to an embodiment of the present invention;
FIG. 7 is a graphical illustration of predicted vehicle speed and elevation information for a road, in accordance with an embodiment of the present invention;
fig. 8 is a schematic diagram of the cluster segmentation result and the SoC planning result in the embodiment provided in fig. 7.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
Example 1
As shown in fig. 1, the embodiment provides a method for planning a battery power trajectory of a plug-in hybrid electric vehicle, which first obtains a destination and a current location, and then performs the following steps:
s01: acquiring a predicted vehicle speed and a road gradient on a driving path based on an ITS system and a navigation system; specifically, macroscopic traffic flow information (average speed of each road section) of a driving path or speed limit information of each road section is acquired through an ITS system and a vehicle navigation system (GPS) and is used as predicted speed information; and acquiring elevation information on a driving path according to an ITS system and a vehicle navigation system (GPS), thereby calculating road gradient information.
S02: respectively clustering the predicted vehicle speed and the road gradient based on an ordered sample clustering algorithm; the method specifically comprises the following steps:
obtaining an ordered sample sequence { x ] of predicted speed/road slope1,x2,x3,…,xnAnd the number k of clusters required, where xnFor the nth predicted speed/road gradient sample, the sequence of the ordered samples of the predicted speed/road gradient is obtained by sampling at preset intervals in sequence, for example, sampling once at intervals of 100 m; the number k of clusters is determined by self-adaptation, and the specific determination process is as follows:
Dris shown inPredicting the sum of the distances between any two points in the speed/road gradient class r under a certain k value:
Figure BDA0002458448180000081
will DrStandardized and recorded as the standard diameter Wk：
Figure BDA0002458448180000082
Defining the amount of Gapn(k) Comprises the following steps:
Figure BDA0002458448180000083
wherein the content of the first and second substances,
Figure BDA0002458448180000084
is log (W)k) The expectation of the reference distribution under n predicted speed/road gradient samples is calculated as: taking the random numbers with the same quantity as the original predicted speed/road gradient samples within the range of the maximum value and the minimum value of the predicted speed/road gradient samples, solving the standard diameter of the random numbers and marking the standard diameter as the standard diameter
Figure BDA0002458448180000085
Repeating the process for B times
Figure BDA0002458448180000086
The average value can be obtained
Figure BDA0002458448180000087
Definition log (W)k) The standard deviation of (a) is sd (k):
Figure BDA0002458448180000091
definition of
Figure BDA0002458448180000092
Analog error definition s ink：
Figure BDA0002458448180000093
Selecting the gas satisfying Gap (k) being equal to or more than Gap (k +1) -sk+1Is used as the optimal predicted speed/road gradient sample sequence cluster number.
An ordered sample sequence { x ] of predicted speed/road gradient is obtained1,x2,x3,…,xnAnd the number k of clusters to be clustered is then carried out, and a predicted speed/road gradient class is defined as G (i, j), wherein the predicted speed/road gradient class comprises a data sequence { x }i,xi+1,…xjH, the diameter D (i, j) of the predicted speed/road grade class G (i, j) is defined as:
Figure BDA0002458448180000094
wherein the content of the first and second substances,
Figure BDA0002458448180000095
to predict the center of the speed/road gradient class G (i, j), and:
Figure BDA0002458448180000096
an objective function e [ p (n, k) ] is set, which can be expressed as:
Figure BDA0002458448180000097
wherein, { i }1,i2,…,ik,ik+1Is the optimal set of segmentation points for the predicted speed/road slope sample sequence, ikIs the k-th division point, and i1And ik+1Predicted speed/road grade respectivelyThe first and last sample of the sequence of samples;
the value of the objective function e [ p (n, k) ] is minimized by dynamic programming solution, resulting in k predicted speed/road grade classes. The specific solving process is as follows: if the position of the r-th sample division point is determined as i, the optimization sub-problem becomes the optimal division scheme for determining the optimal r + 1-k.
1) The cost of the segmentation scheme for calculating the optimal segmentation point r is equal to the distance from the point to the point r +1 plus the cost from the point r +1 to the end point:
e[p(n-ir+1,k-r+1)]＝e*[p(n-ir+1+1,k-r)]+D(ir,ir+1-1),ir+1∈[r+1,n-k+r],ir∈[r,ir+1)
2) solve for min { e [ p (n-i) ]r+1,k-r+1)]And recording the optimal e*[p(n-ir+1,k-r+1)]And each irCorresponding optimum ir+1The value of (c).
3) Repeating 1), 2) to r-1 from r-k-1, and then performing forward recursion to obtain an optimal segmentation sequence { i ═ k-11,i2,…,ik,ik+1}。
S03: merging the clustered predicted vehicle speed and road gradient, and further dividing the driving path into a plurality of sections, wherein the predicted vehicle speed and the road gradient characteristics in each section are consistent; it should be noted that, the fact that the predicted vehicle speed and the road gradient feature in each section of road are consistent means that: the predicted vehicle speed in each section of road section is in the same predicted vehicle speed class, and the road gradient in each section of road section is in the same road gradient class.
S04: and planning a vehicle battery SoC track according to the multiple sections of road sections and the corresponding predicted vehicle speed and road slope. Wherein, the SoC trajectory planning specifically includes:
obtaining the average road resistance of each road section according to the predicted vehicle speed and road gradient corresponding to each road section, and predicting the SoC track descending or ascending on the corresponding road section according to the positive and negative of the average road resistance; wherein the average road resistance of the r-th road section
Figure BDA0002458448180000106
Calculated by the following formula:
Figure BDA0002458448180000101
wherein
Figure BDA0002458448180000102
To be the average vehicle speed,
Figure BDA0002458448180000103
is the average road grade, m is the vehicle mass, g is the gravitational acceleration, f is the road friction coefficient, ρaIs the density of air, CdIs the wind resistance coefficient, and A is the windward area.
For the road section with rising SoC, the slope k of SoC track on each road sectionu：
Figure BDA0002458448180000104
Wherein, Δ SoCuFor SoC increments on corresponding SoC ascending sections, luThe mileage of the corresponding SoC ascending road section is obtained;
for the road sections with the reduced SoC, one road section is selected as a standard road section, and a standard slope k is calculatedstAnd slope k of each sub-segmentd：
Figure BDA0002458448180000105
Wherein, SoC0For initial battery charge, SoCminThe battery charge at the end of the trip, sendTotal mileage,/dFor mileage corresponding to SoC descending road section, Δ snThe distance difference between the standard road section and the corresponding SoC descending road section under the condition that the SoC variation is the same;
and combining the SoC tracks of all the road sections according to the relative positions on the position domain to obtain the SoC track corresponding to the whole driving path.
Wherein said Δ snThe formula is as follows;
Figure BDA0002458448180000111
wherein the content of the first and second substances,
Figure BDA0002458448180000112
is the average road resistance corresponding to the standard road segment,
Figure BDA0002458448180000113
the average road resistance of the corresponding SoC descending road section is obtained;
the Δ SoCuObtained by the following formula:
Figure BDA0002458448180000114
wherein eta istIn order to obtain a coefficient of efficiency of conversion of electric energy,
Figure BDA0002458448180000115
to correspond to the average road resistance of the SoC up link,
Figure BDA0002458448180000116
is the average value of the terminal voltage of the battery, QnomThe rated capacity of the battery.
For further understanding, a more detailed analytical description is provided below.
According to the global optimal battery electric quantity track result of the plug-in hybrid electric vehicle calculated off line, the SoC presents the characteristic of piecewise linearity along with the distance s, the SoC descending speed is the same between the road sections with the same energy demand characteristic, the SoC descending speed is different between the different road sections, and the characteristic which influences the descending speed most obviously is the road resistance of the road sections.
For SoC track descent segment:
when the average power requirement of the vehicle is positive, such as a level road and an uphill road section, and no large braking deceleration working condition exists, the power output is carried out by the APU and the battery according to the optimal energy proportion determined by the FDP algorithm, and at the moment, the SoC is reduced along with the increase of the driving range. It has been explained in the foregoing that the ratio of SoC(s) slopes of sub-links is equal to the ratio of their average road resistances, so that for any link with different average road resistance characteristics, when SoC drops from the same initial state to the same end state, such difference in average road resistance (i.e., difference in SoC(s) slopes) will result in difference in mileage between links resulting in Δ s, as shown in fig. 2.Δ s is the reflection of the slope of different SoC(s) on the location domain between the same SoC states. Obviously, for two road segments, if the difference between their average road resistances is larger, the difference between the slopes of soc(s) is larger, and thus the value of Δ s is larger. Here, Δ s is used as an intermediate variable to assist in the design of the reference trajectory.
K in FIG. 2i、kjThe slope of soc(s) on the road section i and the road section j, and l is the length of the road section j. From the geometry of the above figure, it can be deduced that:
Figure BDA0002458448180000117
the calculated formula for Δ s is obtained:
Figure BDA0002458448180000118
wherein the content of the first and second substances,
Figure BDA0002458448180000119
and
Figure BDA00024584481800001110
the average road resistance on the road section i and the road section j are respectively, and according to the characteristic of the segmented linearity of the soc(s), when the average power requirement is positive, the optimal soc(s) reference track should be a descending broken line consisting of a plurality of line sections with different slopes.
The general definition of Δ s is given above. For a global SoC trace,since one SoC span corresponds to only one segment, it is necessary to construct a standard segment slope as the comparison segment i defined in fig. 2. The standard section can directly select any sub-section of the SoC drop on the section, and the average road resistance of the sub-section is used as the comparison standard of other sub-sections. FIG. 3 selects the 1 st sub-segment as the standard segment, and the slope thereof is equal to the slope k of the 1 st segment1In the drawing with the reference k1Indicated by the two dashed lines. After the standard slope exists, the deltas definition given by the figure 2 and the formula (2) can be carried out on the other road sections, and the deltas of the other road sections are respectively calculated in the SoC interval corresponding to each road sectionn：
Figure BDA0002458448180000121
In the formula (I), the compound is shown in the specification,
Figure BDA0002458448180000122
is the average road resistance of the standard section;
Figure BDA0002458448180000123
is the average road resistance of a certain sub-section; ldIs the mileage of a certain sub-road section. Due to Δ snIs only related to the length of the corresponding road section and the ratio of the average road resistance, and is not related to the relative position of each road section, so that the standard section and the actual SoC (S) track mileage S are in the whole SoC intervalendThe total mileage difference between the road sections is equal to deltas of each road sectionnAnd (4) summing. FIG. 3 shows only Δ snAll greater than 0, but readily available from similar geometric relationships for Δ snLess than 0, the inference is still applicable. This relationship can also be explained starting from the definition of Δ s: because each road section in the whole process is compared with the selected standard sub-road section according to the definition of the figure 2, the SoC interval [ SoC ] corresponding to each road sectionn1,SoCn2]Within the range of the position of the homogeneous difference Δ s between the sub-sections and the standard sub-sectionsn. When SoC intervals of all sub-road sections are connected in series to form a total SoC interval [ SoC0,SoCmin]Due to the standardsThe same interval [ SoC ] is formed after the sub-sections are connected in series0,SoCmin]Then the whole course mileage S after the series connectionendThe mileage difference between the standard mileage and the constructed total standard mileage is delta snAnd (4) summing. The slope of the standard segment, i.e. the selected 1 st segment, is:
Figure BDA0002458448180000124
the slope of each sub-segment is obtained by referring to the form of formula (1):
Figure BDA0002458448180000125
for SoC trace up-going segment:
when the plug-in hybrid vehicle runs on a downhill section with a large gradient and a low running speed, the power demand is negative because the gradient affects the power demand to a greater extent than other resistance, the APU basically remains off, the motor operates in a regenerative braking state, and the soc(s) track rises. The optimal ratio for the energy distribution situation is now completely different from that for a level road or an uphill slope, and the aforementioned design method is therefore no longer applicable for this situation. If the APU is kept closed on a large downhill section, the battery outputs or recovers all the required energy. The energy variation of the battery can be expressed as formula (6.a), Qc2、Qc1Capacity, V, of the battery in final and initial states, respectivelyt2、Vt1Respectively, the terminal voltages of the battery packs, taking into account that the average terminal voltages of the battery packs do not vary much, and therefore the average terminal voltages are used
Figure BDA0002458448180000131
Instead.
Figure BDA0002458448180000132
SoC variation quantity delta SoC for capacity variation in the formula (6.a)uTo show that:
Figure BDA0002458448180000133
thereby establishing a relation between the battery pack SoC and the battery pack energy change:
Figure BDA0002458448180000134
and (7.a) and (7.b) are obtained according to the vehicle dynamic equation and the energy relation when the vehicle descends the slope. The equation (7.a) is substantially the amount of change in kinetic energy of the vehicle as it travels the section of road, where the average slope of the downhill slope is
Figure BDA0002458448180000135
Taking a negative value; the expression (7.b) indicates that the battery energy and the vehicle required energy are inversely varied, and Δ QdWhen the energy demand of the vehicle is negative, the part of the energy is converted into the rise of the electric energy of the battery pack through regenerative braking by the motor. The energy recovered in the regenerative braking process is inevitably lost due to the influence of factors such as the efficiency of a transmission system, the power generation efficiency of a motor, the charging efficiency of a battery pack and the like. Therefore, the efficiency coefficient eta is used heretTo represent the overall efficiency loss over the several places.
Figure BDA0002458448180000136
△Qb＝-ηt△Qd (7.b)
Wherein the content of the first and second substances,
Figure BDA0002458448180000137
to be the average vehicle speed,
Figure BDA0002458448180000138
is the average road grade, m is the vehicle mass, g is the gravitational acceleration, f is the road friction coefficient, ρaIs the density of air, CdIs the wind resistance coefficient, and A is the windward area.
And (6.c), (7.a) and (7.b) to obtain the SoC variation on the road section:
Figure BDA0002458448180000139
fig. 4 shows the planned trajectory when there is one SoC up-segment. The slope of the SoC rising segment can be calculated from the geometrical relationship in the graph:
Figure BDA00024584481800001310
in the formula IuAnd the road section mileage corresponding to the SoC ascending section.
In order to first focus on the design idea of the SoC trace in the whole process when the SoC ascending section is included, the simple case shown in fig. 4 is taken as an example, and the multi-path section multi-slope composite case will be described below. In this case, there is only one SoC up-link, and the average road resistance, i.e., slope, of the other two links is the same. As in the SoC descending segment described above, the SoC slope of a segment is selected as the standard slope (the slope k of the first segment in FIG. 4)1). The mileage difference Δ s at this time is 0 as shown in equation (3). However, the SoC state will increase by Δ SoC due to the presence of the downhill segmentuAt this time, the broken line in fig. 4 is a standard slope at the time of design, and on the basis of equation (4), a standard slope expression in such a simple case can be derived:
Figure BDA0002458448180000141
it should be noted that the above proposed trajectory design method for SoC ascending segment is suitable for the case that the regenerative braking capability substantially meets the braking requirement, i.e. the influence of mechanical braking is eliminated.
For the integrated case:
the actual working conditions faced by the vehicle in running are relatively complex, and the vehicle can have both the running conditions of uphill and downhill and the running conditions of high and low speed in the whole process of one-time running, and the factorsThe average road resistance of each sub-road section is positive or negative, so that the battery SoC in the whole process can rise or fall along with the driving distance. The principle of trajectory planning for the SoC ascending segment and the SoC descending segment is given above, namely, two SoC trajectory planning models represented by fig. 3 and fig. 4, and SoC(s) slope calculation methods for the two models are provided. For the rising of soc(s), the slope of each segment in the rising can be directly obtained from the formula (9), so that the calculation can be performed by the same method under the condition of multi-working-condition compounding. For the case of soc(s) drop, due to the need to introduce the intermediate variable Δ s and the standard slope kstThe optimal SoC(s) trajectory planning method discussed below aims to study a specific expression of a standard slope of a SoC descending segment under a multi-working-condition composite condition.
For the composite situation that the whole process is divided into a plurality of sub-road sections, the sum of the lengths of the sub-road sections is the total mileage s from the energy point of viewendAnd the variation quantity delta SoC of SoC on each sub-road sectionnAlgebraic addition is also a change in SoC over the total path, i.e. the following relationship must be satisfied:
send＝∑l＝∑ld+∑lu (11.a)
SoC0-SoCmin＝∑|△SoC|＝∑|△SoCd|-∑|△SoCu| (11.b)
in the formula, subscripts u and d for variables correspond to an SoC ascending road section and a descending road section, respectively.
On the basis of the general formula obtained by the formula (4), according to the relation of the formula (11), the sigma-delta SoC which cannot be directly calculateddL is expressed by SoC variation of the whole process and the rising segment, and the standard slope under the general condition is obtained:
Figure BDA0002458448180000142
the above process can be visualized by the geometrical relationships in fig. 5.
According to the conclusions that have been made in the foregoing: between sub-segment slope routing segments during SoC drop
Figure BDA0002458448180000143
The ratio and the length of the sub-path are determined; the slope of the sub-road segment at the rising time of SoC is determined by the slope of the segment
Figure BDA0002458448180000144
And sub-road segment length determination, i.e. the slope calculation of the trajectory is independent of the relative position between sub-road segments. Therefore, on the premise that the total SoC variation is the same and the SoC constraint is satisfied, the relative positions of the sub-road sections can be recombined. In fig. 5, the whole process is divided into 5 segments according to the average road resistance, and the slope of the segments is k1～k5The black bold line is the optimal soc(s) trajectory, which is planned in a linear relationship. The whole course of the SoC track is [ SoCmin,SoC0]Change in between, total driving range is Send. Here, the slope of the first segment is chosen as the standard slope, and the dashed line represents the soc(s) trace with the standard slope. The track of each sub-path is equivalent to the track of a thin solid line through translation recombination, and the slope of each sub-path before and after the equivalence is unchanged (respectively represented by k').
Example 2
The embodiment provides a plug-in hybrid electric vehicle battery power trajectory planning system, as shown in fig. 6, including:
the vehicle speed and gradient obtaining module 1 is used for obtaining a predicted vehicle speed and a road gradient on a driving path based on an ITS system and a navigation system;
the road section segmentation module 2 is used for respectively clustering the predicted vehicle speed and the road gradient based on an ordered sample clustering algorithm, merging the clustered predicted vehicle speed and road gradient, and further segmenting the driving path into a plurality of sections of road sections, wherein the characteristics of the predicted vehicle speed and the road gradient in each section of road section are consistent;
and the SoC track planning module 3 is used for planning the SoC track of the vehicle battery according to the multiple sections of road sections and the corresponding predicted vehicle speed and road slope.
In detail, in the road segment segmentation module 2, the step of clustering the predicted vehicle speed and the road gradient based on the ordered sample clustering algorithm respectively comprises the following steps:
obtaining an ordered sample sequence { x ] of predicted speed/road slope1,x2,x3,…,xnAnd the number k of clusters required, where xnFor the nth predicted speed/road grade sample; the clustering number k is determined by self-adaptation, and the specific process is as follows:
Drrepresents the sum of the distances of any two points within the predicted speed/road gradient class r at some value of k:
Figure BDA0002458448180000151
will DrStandardized and recorded as the standard diameter Wk：
Figure BDA0002458448180000152
Defining the amount of Gapn(k) Comprises the following steps:
Figure BDA0002458448180000153
wherein the content of the first and second substances,
Figure BDA0002458448180000154
is log (W)k) The expectation of the reference distribution under n predicted speed/road gradient samples is calculated as: taking the random numbers with the same quantity as the original predicted speed/road gradient samples within the range of the maximum value and the minimum value of the predicted speed/road gradient samples, solving the standard diameter of the random numbers and marking the standard diameter as the standard diameter
Figure BDA0002458448180000155
Repeating the process for B times
Figure BDA0002458448180000156
The average value can be obtained
Figure BDA0002458448180000161
Definition log (W)k) The standard deviation of (a) is sd (k):
Figure BDA0002458448180000162
definition of
Figure BDA0002458448180000163
Analog error definition s ink：
Figure BDA0002458448180000164
Selecting the gas satisfying Gap (k) being equal to or more than Gap (k +1) -sk+1Is used as the optimal predicted speed/road gradient sample sequence cluster number.
An ordered sample sequence { x ] of predicted speed/road gradient is obtained1,x2,x3,…,xnAnd the number k of clusters to be clustered is then carried out, and a predicted speed/road gradient class is defined as G (i, j), wherein the predicted speed/road gradient class comprises a data sequence { x }i,xi+1,…xjH, the diameter D (i, j) of the predicted speed/road grade class G (i, j) is defined as:
Figure BDA0002458448180000165
wherein the content of the first and second substances,
Figure BDA0002458448180000166
to predict the center of the speed/road gradient class G (i, j), and:
Figure BDA0002458448180000167
an objective function e [ p (n, k) ] is set, which can be expressed as:
Figure BDA0002458448180000168
wherein, { i }1,i2,…,ik,ik+1Is the optimal set of segmentation points for the predicted speed/road slope sample sequence, ikIs the k-th division point, and i1And ik+1The first and last samples of the predicted speed/road grade sample sequence, respectively;
the value of the objective function e [ p (n, k) ] is minimized by dynamic programming solution, resulting in k predicted speed/road grade classes.
In detail, in the SoC trajectory planning module 3, the planning of the SoC trajectory of the vehicle battery according to the plurality of road sections and the corresponding predicted vehicle speeds and road slopes specifically includes:
obtaining the average road resistance of each road section according to the predicted vehicle speed and road gradient corresponding to each road section, and predicting the SoC track descending or ascending on the corresponding road section according to the positive and negative of the average road resistance;
for the road section with rising SoC, the slope k of SoC track on each road sectionu：
Figure BDA0002458448180000171
Wherein, Δ SoCuFor SoC increments on corresponding SoC ascending sections, luThe mileage of the corresponding SoC ascending road section is obtained;
for the road sections with the reduced SoC, one road section is selected as a standard road section, and a standard slope k is calculatedstAnd slope k of each sub-segmentd：
Figure BDA0002458448180000172
Wherein, SoC0For initial battery charge, SoCminThe battery charge at the end of the trip, sendTotal mileage,/dFor mileage corresponding to SoC descending road section, Δ snFor standard road section and corresponding SoC descending roadThe mileage difference of the segment under the condition of the same SoC variation;
and combining the SoC tracks of all the road sections according to the relative positions on the position domain to obtain the SoC track corresponding to the whole driving path.
Wherein said Δ snThe formula is as follows;
Figure BDA0002458448180000173
wherein the content of the first and second substances,
Figure BDA0002458448180000174
is the average road resistance corresponding to the standard road segment,
Figure BDA0002458448180000175
the average road resistance of the corresponding SoC descending road section is obtained;
the Δ SoCuObtained by the following formula:
Figure BDA0002458448180000176
wherein eta istIn order to obtain a coefficient of efficiency of conversion of electric energy,
Figure BDA0002458448180000177
to correspond to the average road resistance of the SoC up link,
Figure BDA0002458448180000178
is the average value of the terminal voltage of the battery, QnomThe rated capacity of the battery. For other specific implementation schemes, reference is made to the method for planning the battery power trajectory of the plug-in hybrid electric vehicle described in embodiment 1, and details are not repeated herein.
Fig. 6 further provides a control system for applying the electric quantity trajectory planning system provided in this embodiment to PHEV energy management, where the electric quantity trajectory planning system provided in this embodiment is configured in a vehicle controller, the ITS system and the navigation system are connected with the vehicle controller, and send traffic flow information and terrain information to the electric quantity trajectory planning system in the vehicle controller, so as to perform SoC trajectory planning, and the vehicle controller further receives state parameters of a vehicle power system, and then generates a power distribution request based on the state parameters and the SoC trajectory, and sends the power distribution request to the vehicle power system.
First, a destination and a current position need to be acquired, a travel path is planned to obtain a section of road, predicted vehicle speed and elevation information on the planned travel path are acquired based on an ITS system and a navigation system, as shown in fig. 7, and a predicted vehicle speed ordered sample sequence and a road slope ordered sample sequence can be generated according to the acquired predicted vehicle speed and elevation information at certain sampling intervals.
The optimal segmentation numbers k of the sequence of the ordered samples of the predicted vehicle speed and the sequence of the ordered samples of the road gradient obtained according to the self-adaptive determination method are respectively 3 and 8, then the clustering results of the predicted vehicle speed and the road gradient obtained through the clustering of the ordered samples are shown in fig. 8, so that the whole road is segmented into 10 sub-working condition road sections after merging, and then the SoC track planning is carried out on the basis of the 10 sub-working condition road sections, wherein the planning result is shown in fig. 8, and the SoC track planning result is closer to the global optimal SoC track.
The invention provides a method and a system for planning battery power tracks of a plug-in hybrid electric vehicle, which can be used for rapidly planning SoC tracks according to future travel information acquired from an ITS system and a navigation system. The SoC track planning is carried out according to energy demand characteristics of different road sections, the speed and the gradient are direct factors influencing the energy demand characteristics of the road sections, so that the vehicle speed and the gradient of the road are clustered according to two characteristics of predicted speed and gradient of the road, then the driving path is combined to be divided into a plurality of sections of road sections with different energy demands, and the SoC reference track is generated based on the plurality of sections of road sections with different energy demands. Compared with the existing scheme (the global dynamic programming algorithm generates the optimal track or a neural network and the like), the algorithm in the scheme has lower calculation amount and is closer to the globally optimal SoC track than the SoC reference track design method based on the general simple rule; due to the fact that the calculated amount is low, refreshing can be conducted at a certain frequency according to traffic change conditions, and the method is good in instantaneity and high in robustness. The obtained SoC track planning result can be applied to an energy management algorithm, so that a lower-layer optimization control algorithm (a model prediction control algorithm, a self-adaptive equivalent fuel consumption minimum strategy and the like) tracks the SoC track, and the efficient distribution of energy is realized.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (8)

1. A method for planning battery power tracks of a plug-in hybrid electric vehicle is characterized by comprising the following steps:
acquiring a predicted vehicle speed and a road gradient on a driving path based on an ITS system and a navigation system;
respectively clustering the predicted vehicle speed and the road gradient based on an ordered sample clustering algorithm;
merging the clustered predicted vehicle speed and road gradient, and further dividing the driving path into a plurality of sections, wherein the predicted vehicle speed and the road gradient characteristics in each section are consistent;
according to the multi-section road section and the corresponding predicted vehicle speed and road slope, a vehicle battery SoC track is planned, and the method specifically comprises the following steps:
obtaining the average road resistance of each road section according to the predicted vehicle speed and road gradient corresponding to each road section, and predicting the SoC track descending or ascending on the corresponding road section according to the positive and negative of the average road resistance;
for the road section with rising SoC, the slope k of SoC track on each road sectionu：
Figure FDA0002977576520000011
Wherein, Delta SoCuFor SoC increments on corresponding SoC ascending sections, luThe mileage of the corresponding SoC ascending road section is obtained;
for the road sections with the reduced SoC, one road section is selected as a standard road section, and a standard slope k is calculatedstAnd slope k of each sub-segmentd：
Figure FDA0002977576520000012
Wherein, SoC0For initial battery charge, SoCminThe battery charge at the end of the trip, sendTotal mileage,/dIs the mileage corresponding to the SoC descending road section, Δ snThe distance difference between the standard road section and the corresponding SoC descending road section under the condition that the SoC variation is the same;
and combining the SoC tracks of all the road sections according to the relative positions on the position domain to obtain the SoC track corresponding to the whole driving path.
2. The method for planning the battery power track of the plug-in hybrid electric vehicle according to claim 1, wherein the step of clustering the predicted vehicle speed and the road gradient respectively based on the ordered sample clustering algorithm comprises the following steps:
obtaining an ordered sample sequence { x ] of predicted speed/road slope1,x2,x3,…,xnAnd the number k of clusters required, where xnFor the nth predicted speed/road grade sample;
defining a predicted speed/road grade class as G (i, j) comprising a data sequence { x }i,xi+1,…xjH, the diameter D (i, j) of the predicted speed/road grade class G (i, j) is defined as:
Figure FDA0002977576520000021
wherein the content of the first and second substances,
Figure FDA0002977576520000022
to predict the center of the speed/road gradient class G (i, j), and:
Figure FDA0002977576520000023
an objective function e [ p (n, k) ] is set, which can be expressed as:
Figure FDA0002977576520000024
wherein, { i }1,i2,…,ik,ik+1Is the optimal set of segmentation points for the predicted speed/road slope sample sequence, ikIs the k-th division point, and i1And ik+1The first and last samples of the predicted speed/road grade sample sequence, respectively;
the value of the objective function e [ p (n, k) ] is minimized by dynamic programming solution, resulting in k predicted speed/road grade classes.
3. The method for planning the battery power track of the plug-in hybrid electric vehicle according to claim 2, wherein the number k of clusters required for predicting the speed/road gradient sample sequence is determined by self-adaptation, and the method comprises the following specific steps:
Drrepresents the sum of the distances of any two points within the predicted speed/road gradient class r at some value of k:
Figure FDA0002977576520000025
will DrStandardized and recorded as the standard diameter Wk：
Figure FDA0002977576520000026
Defining the amount of Gapn(k) Comprises the following steps:
Figure FDA0002977576520000027
wherein the content of the first and second substances,
Figure FDA0002977576520000028
is log (W)k) The expectation of the reference distribution under n predicted speed/road gradient samples is calculated as: taking the random numbers with the same quantity as the original predicted speed/road gradient samples within the range of the maximum value and the minimum value of the predicted speed/road gradient samples, solving the standard diameter of the random numbers and marking the standard diameter as the standard diameter
Figure FDA0002977576520000029
Repeating the process for B times
Figure FDA00029775765200000210
The average value can be obtained
Figure FDA00029775765200000211
Definition log (W)k) The standard deviation of (a) is sd (k):
Figure FDA0002977576520000031
definition of
Figure FDA0002977576520000032
Analog error definition s ink：
Figure FDA0002977576520000033
Selecting the gas satisfying Gap (k) being equal to or more than Gap (k +1) -sk+1Is taken as the optimal prediction speedDegree/road slope sample sequence cluster number.
4. The method of claim 1, wherein the Δ s is a function of a battery power trajectory of the plug-in hybrid vehiclenThe formula is as follows;
Figure FDA0002977576520000034
wherein the content of the first and second substances,
Figure FDA0002977576520000035
is the average road resistance corresponding to the standard road segment,
Figure FDA0002977576520000036
the average road resistance of the corresponding SoC descending road section is obtained;
the Δ SoCuObtained by the following formula:
Figure FDA0002977576520000037
wherein eta istIn order to obtain a coefficient of efficiency of conversion of electric energy,
Figure FDA0002977576520000038
to correspond to the average road resistance of the SoC up link,
Figure FDA0002977576520000039
is the average value of the terminal voltage of the battery, QnomIs the rated capacity of the battery.
5. The utility model provides a plug-in hybrid vehicle battery power track planning system which characterized in that includes:
the vehicle speed and gradient acquisition module is used for acquiring the predicted vehicle speed and road gradient on the driving path based on the ITS system and the navigation system;
the road section segmentation module is used for respectively clustering the predicted vehicle speed and the road gradient based on an ordered sample clustering algorithm, merging the clustered predicted vehicle speed and road gradient, and further segmenting the driving path into a plurality of sections of road sections, wherein the predicted vehicle speed and the road gradient in each section of road section are the same;
the SoC track planning module is used for planning the SoC track of the vehicle battery according to the multiple sections of road sections and the corresponding predicted vehicle speed and road slope, and specifically comprises the following steps:
obtaining the average road resistance of each road section according to the predicted vehicle speed and road gradient corresponding to each road section, and predicting the SoC track descending or ascending on the corresponding road section according to the positive and negative of the average road resistance;
for the road section with rising SoC, the slope k of SoC track on each road sectionu：
Figure FDA00029775765200000310
Wherein, Delta SoCuFor SoC increments on corresponding SoC ascending sections, luThe mileage of the corresponding SoC ascending road section is obtained;
for the road sections with the reduced SoC, one road section is selected as a standard road section, and a standard slope k is calculatedstAnd slope k of each sub-segmentd：
Figure FDA0002977576520000041
Wherein, SoC0For initial battery charge, SoCminThe battery charge at the end of the trip, sendTotal mileage,/dIs the mileage corresponding to the SoC descending road section, Δ snThe distance difference between the standard road section and the corresponding SoC descending road section under the condition that the SoC variation is the same;
and combining the SoC tracks of all the road sections according to the relative positions on the position domain to obtain the SoC track corresponding to the whole driving path.
6. The system for planning battery power tracks of plug-in hybrid vehicles according to claim 5, wherein the clustering of the predicted vehicle speed and road grade based on the ordered sample clustering algorithm comprises the steps of:
obtaining an ordered sample sequence { x ] of predicted speed/road slope1,x2,x3,…,xnAnd the number k of clusters required, where xnFor the nth predicted speed/road grade sample;
defining a predicted speed/road grade class as G (i, j) comprising a data sequence { x }i,xi+1,…xjH, the diameter D (i, j) of the predicted speed/road grade class G (i, j) is defined as:
Figure FDA0002977576520000042
wherein the content of the first and second substances,
Figure FDA0002977576520000043
to predict the center of the speed/road gradient class G (i, j), and:
Figure FDA0002977576520000044
an objective function e [ p (n, k) ] is set, which can be expressed as:
Figure FDA0002977576520000045
wherein, { i }1,i2,…,ik,ik+1Is the optimal set of segmentation points for the predicted speed/road slope sample sequence, ikIs the k-th division point, and i1And ik+1The first and last samples of the predicted speed/road grade sample sequence, respectively;
the value of the objective function e [ p (n, k) ] is minimized by dynamic programming solution, resulting in k predicted speed/road grade classes.
7. The system for planning the battery power track of the plug-in hybrid electric vehicle according to claim 6, wherein the number k of clusters required for predicting the speed/road gradient sample sequence is determined by self-adaptation, and the method comprises the following specific steps:
Drrepresents the sum of the distances of any two points within the predicted speed/road gradient class r at some value of k:
Figure FDA0002977576520000051
will DrStandardized and recorded as the standard diameter Wk：
Figure FDA0002977576520000052
Defining the amount of Gapn(k) Comprises the following steps:
Figure FDA0002977576520000053
wherein the content of the first and second substances,
Figure FDA0002977576520000054
is log (W)k) The expectation of the reference distribution under n predicted speed/road gradient samples is calculated as: taking the random numbers with the same quantity as the original predicted speed/road gradient samples within the range of the maximum value and the minimum value of the predicted speed/road gradient samples, solving the standard diameter of the random numbers and marking the standard diameter as the standard diameter
Figure FDA0002977576520000055
Repeating the process for B times
Figure FDA0002977576520000056
The average value can be obtained
Figure FDA0002977576520000057
Definition log (W)k) The standard deviation of (a) is sd (k):
Figure FDA0002977576520000058
definition of
Figure FDA0002977576520000059
Analog error definition s ink：
Figure FDA00029775765200000510
Selecting the gas satisfying Gap (k) being equal to or more than Gap (k +1) -sk+1Is used as the optimal predicted speed/road gradient sample sequence cluster number.
8. The system of claim 5, wherein the Δ s is a function of a battery power trajectory of the plug-in hybrid vehiclenThe formula is as follows;
Figure FDA0002977576520000061
wherein the content of the first and second substances,
Figure FDA0002977576520000062
is the average road resistance corresponding to the standard road segment,
Figure FDA0002977576520000063
the average road resistance of the corresponding SoC descending road section is obtained;
the Δ SoCuObtained by the following formula:
Figure FDA0002977576520000064
wherein eta istIn order to obtain a coefficient of efficiency of conversion of electric energy,
Figure FDA0002977576520000065
to correspond to the average road resistance of the SoC up link,
Figure FDA0002977576520000066
is the average value of the terminal voltage of the battery, QnomIs the rated capacity of the battery.
CN202010312714.1A 2020-04-20 2020-04-20 Plug-in hybrid electric vehicle battery power track planning method and system Active CN111591279B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010312714.1A CN111591279B (en) 2020-04-20 2020-04-20 Plug-in hybrid electric vehicle battery power track planning method and system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010312714.1A CN111591279B (en) 2020-04-20 2020-04-20 Plug-in hybrid electric vehicle battery power track planning method and system

Publications (2)

Publication Number Publication Date
CN111591279A CN111591279A (en) 2020-08-28
CN111591279B true CN111591279B (en) 2021-05-28

Family

ID=72180375

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010312714.1A Active CN111591279B (en) 2020-04-20 2020-04-20 Plug-in hybrid electric vehicle battery power track planning method and system

Country Status (1)

Country Link
CN (1) CN111591279B (en)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112572404B (en) * 2020-12-24 2021-11-30 吉林大学 Heavy commercial vehicle hybrid power energy management method based on front road information
CN112937547B (en) * 2021-01-28 2023-03-24 北京理工大学 Plug-in hybrid power bus energy management method based on global working conditions
CN113232651B (en) * 2021-02-08 2022-07-22 杭州电子科技大学 Fuel automobile ecological self-adaptive cruise control method based on ADHDP
CN113320520B (en) * 2021-07-15 2022-08-12 合众新能源汽车有限公司 Energy control method and system of extended range type automobile
CN115035724B (en) * 2022-07-13 2022-12-02 吉林大学 Logistics vehicle punctuality transportation vehicle and road cooperative control method based on ecological formation
WO2024066702A1 (en) * 2022-09-29 2024-04-04 比亚迪股份有限公司 Hybrid vehicle and energy management method therefor, apparatus, medium and electronic device

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103434509A (en) * 2013-07-10 2013-12-11 大连理工大学 Control system of hybrid power bus and motive power control method of hybrid power bus
CN103640569A (en) * 2013-11-28 2014-03-19 江苏大学 Hybrid electric vehicle energy management system and method based on multi-agent technology
CN110682905A (en) * 2019-10-12 2020-01-14 重庆大学 Method for acquiring battery charge state reference variable quantity in time domain based on driving mileage

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9360527B2 (en) * 2011-08-12 2016-06-07 Johnson Controls Technology Llc System and method for energy prediction in battery packs

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103434509A (en) * 2013-07-10 2013-12-11 大连理工大学 Control system of hybrid power bus and motive power control method of hybrid power bus
CN103640569A (en) * 2013-11-28 2014-03-19 江苏大学 Hybrid electric vehicle energy management system and method based on multi-agent technology
CN110682905A (en) * 2019-10-12 2020-01-14 重庆大学 Method for acquiring battery charge state reference variable quantity in time domain based on driving mileage

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
聚类算法详解;思绪无限;《CSDN》;20180516;全文 *

Also Published As

Publication number Publication date
CN111591279A (en) 2020-08-28

Similar Documents

Publication Publication Date Title
CN111591279B (en) Plug-in hybrid electric vehicle battery power track planning method and system
Zhou et al. An integrated predictive energy management for light-duty range-extended plug-in fuel cell electric vehicle
Li et al. Adaptive energy management strategy for fuel cell/battery hybrid vehicles using Pontryagin's Minimal Principle
US9758145B2 (en) Vehicle energy management device
Johannesson et al. Assessing the potential of predictive control for hybrid vehicle powertrains using stochastic dynamic programming
Rezaei et al. Energy management strategies of battery-ultracapacitor hybrid storage systems for electric vehicles: Review, challenges, and future trends
CN108909702A (en) A kind of plug-in hybrid-power automobile energy management method and system
US8612077B2 (en) Hybrid electric vehicle and method of path dependent receding horizon control
Abousleiman et al. Energy consumption model of an electric vehicle
Hu et al. An online rolling optimal control strategy for commuter hybrid electric vehicles based on driving condition learning and prediction
Zendegan et al. Predictive battery state of charge reference generation using basic route information for optimal energy management of heavy-duty fuel cell vehicles
Wang et al. A hybrid energy management strategy based on line prediction and condition analysis for the hybrid energy storage system of tram
CN109344996A (en) A kind of urban railway transit train optimization and energy saving method
Yi et al. Energy aware driving: Optimal electric vehicle speed profiles for sustainability in transportation
CN114148325B (en) Method for managing predictive energy of heavy-duty hybrid commercial vehicle
CN113815437A (en) Predictive energy management method for fuel cell hybrid electric vehicle
Daniels et al. The optimal use of the solar powered automobile
Lamantia et al. Remaining driving range estimation framework for electric vehicles in platooning applications
US20160362096A1 (en) Method and System for Selecting Power Sources in Hybrid Electric Vehicles
Ruan et al. A modularized electric vehicle model-in-the-loop simulation for transportation electrification modeling and analysis
Zhang et al. Battery aging-minimal speed control of autonomous heavy-duty electric trucks in adaptation to highway topography and traffic
Vajedi Real-time optimal control of a plug-in hybrid electric vehicle using trip information
Gu et al. A review of intelligent road preview methods for energy management of hybrid vehicles
Kljaić et al. Assessment of Railway Train Energy Efficiency and Safety Using Real-time Track Condition Information
Lee et al. Nonlinear Model Predictive Control Based Adaptive Equivalent Consumption Minimization Strategy for Fuel Cell Electric Bus Considering Average Travel Speed

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant