CN108394402A - A kind of mixing torque control method of parallel hybrid electric vehicle - Google Patents
A kind of mixing torque control method of parallel hybrid electric vehicle Download PDFInfo
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- 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
- B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
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- 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
- B60W10/00—Conjoint control of vehicle sub-units of different type or different function
- B60W10/04—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units
- B60W10/06—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units including control of combustion engines
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- 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
- B60W10/00—Conjoint control of vehicle sub-units of different type or different function
- B60W10/04—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units
- B60W10/08—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units including control of electric propulsion units, e.g. motors or generators
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- B60W2510/00—Input parameters relating to a particular sub-units
- B60W2510/06—Combustion engines, Gas turbines
- B60W2510/0676—Engine temperature
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- B—PERFORMING OPERATIONS; TRANSPORTING
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- 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
- B60W2510/00—Input parameters relating to a particular sub-units
- B60W2510/24—Energy storage means
- B60W2510/242—Energy storage means for electrical energy
- B60W2510/244—Charge state
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- 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
- B60W2710/00—Output or target parameters relating to a particular sub-units
- B60W2710/06—Combustion engines, Gas turbines
- B60W2710/0666—Engine torque
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- 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
- B60W2710/00—Output or target parameters relating to a particular sub-units
- B60W2710/08—Electric propulsion units
- B60W2710/083—Torque
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Abstract
A kind of mixing torque control method of parallel hybrid electric vehicle, is related to electrical vehicular power device.The mixing torque control method of the parallel hybrid electric vehicle, acquire ABS speeds, hybrid system demand torque, engine temperature and the battery pack state of charge of automobile, the engine torque and Motor torque exported when hybrid vehicle oil consumption is minimum is determined using BP neural network, it can realize the real-time control to engine and generator torque, so that entire vehicle is in efficient working condition in whole process, each time phase of operation, effectively reduces fuel consumption.
Description
Technical field
The present invention relates to electrical vehicular power device, more particularly to the mixing torque control of a kind of parallel hybrid electric vehicle
Method processed.
Background technology
With becoming increasingly conspicuous for global energy and environmental problem, low oil consumption is developed, the new automobile of low emission becomes current
The top priority of automobile industry development.In this background, the mixing for merging conventional fuel oil automobile and pure electric automobile advantage is dynamic
Power automobile becomes the current most low emission of application prospect, low energy consumption automobile.As a kind of novel polyergic source vehicles,
The performance of hybrid vehicle with to mix torque closely related.It, should being capable of basis under the premise of meeting automobile dynamic quality
The characteristic of automobile dynamic system and real-time operating condition realize the rational torque distribution between engine, motor, to obtain
The maximum fuel-economy performance of vehicle, minimum discharge and stable cornering ability.
The mixing moment of torsion control of hybrid vehicle is a major issue for being related to nonlinear dynamic optimization, which makees
To influence a main bottleneck of vehicle performance and hybrid power industrialization process, do not solved finally so far, letter
It waits breaking through.It is integrated with electrical, mechanical, chemically and thermally mechanical system Nonlinear Dynamic system since hybrid power system is one
System, the co-ordination between itself and its each component are extremely complex, it is difficult to establish the accurate mathematical model of system；Meanwhile vehicle
Driving cycle and driver's operation have randomness, and which increases the difficulty that mixing torque accurately controls.
Invention content
In view of the deficienciess of the prior art, the object of the present invention is to provide a kind of mixing of parallel hybrid electric vehicle
Torque control method, reasonable design can effectively reduce the fuel consumption of automobile.
The technical solution adopted in the present invention is：A kind of mixing torque control method of parallel hybrid electric vehicle,
Technical essential is the ABS speeds for acquiring automobile, hybrid system demand torque, engine temperature and battery pack state of charge, is utilized
BP neural network determines the engine torque and Motor torque exported when hybrid vehicle oil consumption is minimum.
Using Fuzzy C-Means Clustering Algorithm to the ABS speeds of acquisition, hybrid system demand torque, engine temperature and electricity
Pond group state of charge is classified.
The Fuzzy C-Means Clustering Algorithm includes the following steps：
(1) it is directed to n sample, setting classification number c, and is randomly provided one group of initial cluster center V=(V1,V2...Vc),
The object function that ambiguity in definition C- mean values divide is as follows：
Wherein, J (U, V) is object function,For k-th of sample XkIt is under the jurisdiction of the degree of i-th of fuzzy subset, h is to add
Index is weighed, i is fuzzy subset, and k is k-th sample, dikTo be sample at a distance from each cluster centre, c is classification number, and n is sample
This number.
dik=| XK-Vi| for sample at a distance from each cluster centre；
(2) new subordinated-degree matrix is calculated, output matrix is：
In formula, uikFor subordinated-degree matrix, xiFor i-th of sample, vkFor k-th of cluster centre, xjFor j-th of sample, h is
Weighted Index, as h=1, it is Hard c-means clustering, usual h=2 that fuzzy clustering, which is just degenerated,；
(3) cluster centre is adjusted by following formula：
In formula, ViIndicate cluster centre, xkIndicate k-th of sample.
By formula (3) calculating target function, when adjacent iteration twice gained object function varies less, then it is assumed that algorithm is own to be received
It holds back, has also just obtained all kinds of cluster centres and each sample is subordinate to angle value for all kinds of, so as to complete entirely sampling is waited for
The fuzzy clustering of this collection divides, and otherwise goes to step (2).
The beneficial effects of the invention are as follows：The mixing torque control method of the parallel hybrid electric vehicle, acquires automobile
ABS speeds, hybrid system demand torque, engine temperature and battery pack state of charge are being mixed using BP neural network determination
The engine torque and Motor torque that power vehicle oil consumption exports when minimum can realize the reality to engine and generator torque
When control, so that entire vehicle is in efficient working condition in whole process, each time phase of operation, effectively reduce fuel oil
Consumption.
Description of the drawings
It in order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, below will be to institute in embodiment
Attached drawing to be used is needed to be briefly described, it should be apparent that, the accompanying drawings in the following description is only some implementations of the present invention
Example, for those of ordinary skill in the art, without creative efforts, can also obtain according to these attached drawings
Other attached drawings.
Fig. 1 is the flow chart of the mixing torque control method of parallel hybrid electric vehicle in the embodiment of the present invention；
Fig. 2 is the fundamental diagram of parallel hybrid electric vehicle in the embodiment of the present invention.
Specific implementation mode
Keep the above objects, features and advantages of the present invention more obvious and easy to understand, below in conjunction with the accompanying drawings 1, Fig. 2 and specific
The present invention is described in further detail for embodiment.
The mixing torque control method of parallel hybrid electric vehicle used in the embodiment of the present invention, acquires the ABS of automobile
Speed, hybrid system demand torque, engine temperature and battery pack state of charge are determined using BP neural network in hybrid power
The engine torque and Motor torque exported when automobile fuel consumption is minimum, specifically includes following steps：
Step 101, using Fuzzy C-Means Clustering Algorithm to the ABS speeds of acquisition, hybrid system demand torque, engine
Temperature and battery pack state of charge are classified, wherein Fuzzy C-Means Clustering Algorithm includes the following steps：
(1) it is directed to n sample, setting classification number c, and is randomly provided one group of initial cluster center V=(V1,V2...Vc),
The object function that ambiguity in definition C- mean values divide is as follows：
Wherein, J (U, V) is object function,For k-th of sample XkIt is under the jurisdiction of the degree of i-th of fuzzy subset, h is to add
Index is weighed, i is fuzzy subset, and k is k-th sample, dikTo be sample at a distance from each cluster centre, c is classification number, and n is sample
This number.
dik=| XK-Vi| for sample at a distance from each cluster centre；
(2) new subordinated-degree matrix is calculated, output matrix is：
In formula, uikFor subordinated-degree matrix xiFor i-th of sample, vkFor k-th of cluster centre, xjFor j-th of sample, h is to add
Index is weighed, as h=1, it is Hard c-means clustering, usual h=2 that fuzzy clustering, which is just degenerated,；
(3) cluster centre is adjusted by following formula：
In formula, ViIndicate cluster centre, xkK-th of sample.
By formula (3) calculating target function, when adjacent iteration twice gained object function varies less, then it is assumed that algorithm is own to be received
It holds back, has also just obtained all kinds of cluster centres and each sample is subordinate to angle value for all kinds of, so as to complete entirely sampling is waited for
The fuzzy clustering of this collection divides, and otherwise goes to step (2).
Step 102, using BP neural network determine the engine torque that is exported when hybrid vehicle oil consumption is minimum and
Motor torque, it is specific as follows：
To avoid the dimension difference of each inputoutput data of neural network, accelerates the convergence of neural network, reduce and calculate hardly possible
Degree.The training sample chosen above is standardized before being trained, the input of network, output data are limited in [0,1]
In section, change type is as follows：
In formula, x represents input or output data, xminRepresent all samples inputs, output data minimum value, xmaxGeneration
All samples of table inputs, output data maximum value.
Standard Bp algorithms are long there are the training time, convergence rate is slow, and the ginsengs such as initial weight, learning rate and momentum term coefficient
Number the shortcomings of being difficult to adjust, when training in the present embodiment, use Levenberg-Marquardt algorithms, it is combined under gradient
The advantage of drop method and one Newton method of Gauss, the local convergence of one Newton method of existing Gauss, and the overall situation with gradient descent method
Characteristic, training step are as follows:
(1) network is submitted into all inputs and calculates corresponding network output and error ei(x) (i=1 ..., N；N is sample
This number), then calculate the error criterion function E of all outputsk(x), it can be set as：
(2) E is calculatedk(x) to network parameter vector xkThe Jacobian matrix Js (x) of (weights and close value) differential, i.e.,
In formula, e1(x) error is indicated.
(3) regulation of network parameter is sought according to the following formula：
△ x=- [J (x)TJ(x)+μI]-1J(x)Te(x)
In formula, μ is the nonnegative number that some can adaptively be adjusted.
(4) the network convergence condition for reaching setting exits training and stores network of relation parameter, otherwise goes to the 5th step.
(5) x is usedk+ △ x adjust network parameter, recalculate error criterion function Ek+1(x), if Ek+1(x)<Ek(x), then
Network training speed μ divided by θ (θ>1), and x is setk+1=xk+ △ x turn the 1st step；Otherwise, μ is increased θ times, turns (3) step.
The present invention, as basic tool, realizes the engine and electricity to hybrid vehicle using BP neural network method
Machine torque is controlled well, and the real-time monitoring based on BP neural network can be realized to the instantaneous optimal of hybrid power system
Control, effectively reduce fuel consumption, and can overcome very well traditional instantaneous optimization energy management strategies can not real-time control the shortcomings that.
BP has the advantages that some that other neural networks do not have:Levenberg-Marquardt algorithms, it is combined
The advantage of gradient descent method and one Newton method of Gauss, the local convergence of one Newton method of existing Gauss, and there is gradient descent method
Global property.
Although specific embodiments of the present invention have been described above, those skilled in the art in the art should manage
Solution, these are merely examples, and many changes and modifications may be made, without departing from the principle of the present invention
And essence.The scope of the present invention is only limited by the claims that follow.
Claims (3)
1. a kind of mixing torque control method of parallel hybrid electric vehicle, which is characterized in that acquire automobile ABS speeds,
Hybrid system demand torque, engine temperature and battery pack state of charge are determined using BP neural network in hybrid vehicle
The engine torque and Motor torque exported when oil consumption is minimum.
2. the mixing torque control method of parallel hybrid electric vehicle as described in claim 1, which is characterized in that utilize mould
Paste C- means clustering algorithms to the ABS speeds of acquisition, hybrid system demand torque, engine temperature and battery pack state of charge into
Row classification.
3. the mixing torque control method of parallel hybrid electric vehicle as described in claim 1, which is characterized in that described
Fuzzy C-Means Clustering Algorithm includes the following steps：
(1) it is directed to n sample, setting classification number c, and is randomly provided one group of initial cluster center V=(V1,V2...Vc), definition
The object function that Fuzzy C-means divide is as follows：
Wherein, J (U, V) is object function,For k-th of sample XkIt is under the jurisdiction of the degree of i-th of fuzzy subset, h is that weighting refers to
Number, i are fuzzy subset, and k is k-th sample, dikTo be sample at a distance from each cluster centre, c is classification number, and n is sample number.
dik=| XK-Vi| for sample at a distance from each cluster centre；
(2) new subordinated-degree matrix is calculated, output matrix is：
In formula, uikFor subordinated-degree matrix, xiFor i-th of sample, vkFor k-th of cluster centre, xjFor j-th of sample, h is weighting
Index, as h=1, it is Hard c-means clustering, usual h=2 that fuzzy clustering, which is just degenerated,；
(3) cluster centre is adjusted by following formula：
In formula, ViIndicate cluster centre, xkIndicate k-th of sample.
By formula (3) calculating target function, when adjacent iteration twice gained object function varies less, then it is assumed that oneself restrains algorithm,
Also it has just obtained all kinds of cluster centres and each sample is subordinate to angle value for all kinds of, so as to complete entire sample set to be selected
Fuzzy clustering divides, and otherwise goes to step (2).
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110733493A (en) * | 2019-11-22 | 2020-01-31 | 辽宁工业大学 | A power distribution method for a hybrid electric vehicle |
| CN112849119A (en) * | 2019-11-12 | 2021-05-28 | 上海汽车变速器有限公司 | Multivariable torque optimizing control distribution method for engine and motor of hybrid electric vehicle |
| CN113006996A (en) * | 2019-12-20 | 2021-06-22 | 广州汽车集团股份有限公司 | ISG dragging torque control method, device and unit of plug-in hybrid electric vehicle |
| CN119911260A (en) * | 2025-04-02 | 2025-05-02 | 潍柴雷沃智慧农业科技股份有限公司 | Power split hybrid tractor control method, system, device and medium |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112849119A (en) * | 2019-11-12 | 2021-05-28 | 上海汽车变速器有限公司 | Multivariable torque optimizing control distribution method for engine and motor of hybrid electric vehicle |
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| CN113006996A (en) * | 2019-12-20 | 2021-06-22 | 广州汽车集团股份有限公司 | ISG dragging torque control method, device and unit of plug-in hybrid electric vehicle |
| CN113006996B (en) * | 2019-12-20 | 2022-08-19 | 广州汽车集团股份有限公司 | ISG dragging torque control method, device and unit of plug-in hybrid electric vehicle |
| CN119911260A (en) * | 2025-04-02 | 2025-05-02 | 潍柴雷沃智慧农业科技股份有限公司 | Power split hybrid tractor control method, system, device and medium |
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Application publication date: 20180814 |