CN108177648B - A kind of energy management method of the plug-in hybrid vehicle based on intelligent predicting - Google Patents
A kind of energy management method of the plug-in hybrid vehicle based on intelligent predicting Download PDFInfo
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- CN108177648B CN108177648B CN201810003711.2A CN201810003711A CN108177648B CN 108177648 B CN108177648 B CN 108177648B CN 201810003711 A CN201810003711 A CN 201810003711A CN 108177648 B CN108177648 B CN 108177648B
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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
- B60W20/11—Controlling the power contribution of each of the prime movers to meet required power demand using model predictive control [MPC] strategies, i.e. control methods based on models predicting performance
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/14—Plug-in electric vehicles
Abstract
The present invention provides a kind of energy management method of plug-in hybrid vehicle based on intelligent predicting, the driving cycle information for having the characteristics that multi-source, mixing is utilized, on-line study optimizes the distribution of PHEV global energy, improves the intelligent level of hybrid power system；Real-time learning and prediction model are established, the operating condition precision of prediction of control time domain is improved, realizes the multiple-objection optimization energy management in rolling time horizon, it is significant to the deep energy-saving potentiality for excavating PHEV, there is many beneficial effects.
Description
Technical field
The present invention relates to a kind of energy management technical fields of plug-in hybrid vehicle, and in particular, to Yi Zhongtong
Cross the method that the means of intelligent prediction realize energy management to plug-in hybrid vehicle.
Background technique
The dynamical system of plug-in hybrid vehicle due to the polyphyly of energy input, operating mode diversity, with
And diversified forms energy stream it is strong coupling the features such as, comprehensively control and energy management are always more complicated in this field
Technological difficulties.Its energy management and vehicle driving traffic environment, road pedestrian, weather, driver's driving style, vehicle itself shape
A variety of driving cycle factors such as state, road grade are closely related, however the traveling for how using these having the characteristics that multi-source, mixing
Work information realizes the global energy distribution of on-line study optimization plug-in hybrid vehicle, improves hybrid power system
Intelligent level is urgent problem to be solved in this field.
Summary of the invention
For technical problem present in above-mentioned this field, the present invention provides a kind of energy of plug-in hybrid vehicle
Quantity management method, specifically includes the following steps:
Step 1, On-line testing correspond to the multidimensional driving cycle information of target travel route, are based on deep learning algorithm pair
The target travel route establishes the reconstruction model of global driving cycle.To realize dynamic weight in advance to target travel route
Structure.
Step 2, the reconstruction model based on the global driving cycle established in the step 1, establish intensified learning network
Model obtains the power battery optimal energy track of the plug-in hybrid vehicle；
Step 3 constructs driver style deep layer volume according to the oneself state information and traffic information of the vehicle respectively
The deep layer convolutional neural networks model of product neural network model and traffic information, extracts corresponding driver style feature and traffic
Information characteristics, the future short-term operating condition real-time prediction model that the vehicle is established based on deep learning algorithm；
Step 4, according to the life model of the power battery, with the power battery optimal energy in the step 2
Track is built as final value constraint when rolling in conjunction with the following short-term operating condition real-time prediction model established in the step 3
Found the control strategy of the power battery.
Further, in the step 1 On-line testing multidimensional driving cycle information, including extract from open source Map Services
Quotient, traffic monitoring platform, car flow information, signal information, pedestrian information and the Weather information of vehicle-mounted vision system.Pass through standard
Above-mentioned various information MAPs are standard driving cycle information by mapping mode.
Further, described in the step 2 establishes intensified learning network model, obtains the plug-in hybrid
The power battery optimal energy track of vehicle, specifically includes: being consumed using global energy at least as the intensified learning network mould
The reinforcing of type is rewarded.
Further, the oneself state information of the vehicle in the step 3 include with turn to, gas pedal, brake pedal
Etc. relevant information.Different drivers judge in the heart vehicle follow gallop, lane variation behavior and the signal lamp in driving process
Difference is very big in reason, and which results in the differences of driving behavior.Still there are some friendships for being difficult to measure except driver's driving style
Logical environmental information can have an impact the following operating condition, thus the traffic information includes that such as environment car speed, signal lamp are converted
And road pedestrian's random walk etc..Database is established to the oneself state information and traffic information of the vehicle respectively, is utilized
Sample in the database constructs the deep layer convolutional neural networks model of the driver style deep layer and traffic information respectively.
Each network layer of the neural network is to be limited the depth that Boltzmann machine superposition forms by multiple Gauss-Bernoulli Jacob
It spends belief network end and neural network is added, the following temp work of the vehicle is carried out using the feature that deepness belief network generates
Condition is predicted in real time.The training process of whole network mainly consists of two parts, and a part is that successively trained one uncle of multiple Gausses exerts
The limited Boltzmann machine of benefit, each Gauss-Bernoulli Jacob are limited Boltzmann machine model and utilize the joint probability table based on energy
It reaches.Another part of model training process is the small parameter perturbations under Training, and concrete operations are that end introduces neural network
Layer is returned, deep layer convolutional neural networks structure is formed, is finely adjusted, was finely tuned using parameter of the back-propagation algorithm to pre-training
Parameter more new formula in journey is as follows:
Wherein J (W, b；X, y) be model loss function, by parameter W, b and input x and output y are determined, δ is residual error, 1
Represent the number of plies, w(1)It is weighting parameter, b(1)It is offset parameter, a(1)It is activation value, m is sample value quantity, and α is learning rate, w(1)
It is regular terms, λ is regularization coefficient.
Be based ultimately upon deep layer convolutional neural networks model realization prediction operating condition process can be used to lower mapping relations and
Propagated forward formula indicates:
F (traffic information, driving style information；W, b)=v
z(l+1)=W(l)a(l)+b(l)
a(l+1)=f (z(l+1))
Wherein v is Vehicle Speed, and 1 represents the number of plies, w(1)It is weighting parameter, b(1)It is offset parameter, a(1)It is activation
Value, z(1+1)Be unit weighted input and.
Further, the life model of the power battery in the step 4 is the circulation longevity of lithium-ion-power cell
Empirical model is ordered, power battery capacitance loss is introduced into the objective function of energy optimization to realize multiple-objection optimization management,
The empirical model that power battery capacity changes with charging and discharging currents are as follows:
Wherein QlossIt is power battery loss capacity, BexpIt is pre-exponential factor, with CrateInversely, R is gas constant,
TbattIt is the average absolute temperature of battery, AhIt is power battery accumulation charge and discharge ampere-hour number.
Further, the step 4 further includes the cost function established about power battery service life, traveling energy consumption.
The energy management method of the plug-in hybrid vehicle according to provided by aforementioned present invention is utilized with more
Source, the driving cycle information for mixing feature, on-line study optimize the distribution of PHEV global energy, improve the intelligence of hybrid power system
Change horizontal；Real-time learning and prediction model are established, the operating condition precision of prediction of control time domain is improved, realizes more mesh in rolling time horizon
Mark optimization energy management, it is significant to the deep energy-saving potentiality for excavating PHEV, there is many beneficial effects.
Detailed description of the invention
Fig. 1 is the schematic diagram for the multidimensional driving cycle information that On-line testing corresponds to target travel route
Fig. 2 is the schematic diagram for establishing the future short-term operating condition real-time prediction model of vehicle
Fig. 3 is the schematic diagram of the building process of deep layer convolutional neural networks
Fig. 4 is the overall flow schematic diagram of method provided by the present invention
Specific embodiment
Further explaination in detail is made to technical solution of the present invention with reference to the accompanying drawing.
The energy management method of a kind of plug-in hybrid vehicle provided by the present invention, as shown in figure 4, specifically including
Following steps:
Step 1, as shown in Figure 1, On-line testing correspond to target travel route multidimensional driving cycle information, be based on depth
Learning algorithm establishes the reconstruction model of global driving cycle to the target travel route.It is pre- to be realized to target travel route
First dynamic reconstruct.
Step 2, the reconstruction model based on the global driving cycle established in the step 1, establish intensified learning network
Model obtains the power battery optimal energy track of the plug-in hybrid vehicle；
Step 3 constructs driver style deep layer volume according to the oneself state information and traffic information of the vehicle respectively
The deep layer convolutional neural networks model of product neural network model and traffic information, extracts corresponding driver style feature and traffic
Information characteristics, the future short-term operating condition real-time prediction model that the vehicle is established based on deep learning algorithm；
Step 4, according to the life model of the power battery, with the power battery optimal energy in the step 2
Track is built as final value constraint when rolling in conjunction with the following short-term operating condition real-time prediction model established in the step 3
Found the control strategy of the power battery.
In the preferred embodiment of the application, the multidimensional driving cycle information of On-line testing in the step 1, including
Extract from open source Map Services quotient, traffic monitoring platform, car flow information, signal information, the pedestrian information of vehicle-mounted vision system
And Weather information.
In the preferred embodiment of the application, described in the step 2 establishes intensified learning network model, obtains
The power battery optimal energy track of the plug-in hybrid vehicle, specifically includes: using global energy consumption it is minimum as
The reinforcing of the intensified learning network model is rewarded.
In the preferred embodiment of the application, as shown in Fig. 2, the oneself state information of the vehicle in the step 3
Including information relevant to steering, gas pedal, brake pedal etc..Different drivers in driving process vehicle follow gallop,
Lane changes behavior and signal lamp judges that psychologically very greatly, which results in the differences of driving behavior for difference.It is travelled in driver
Still there are some traffic environment information for being difficult to measure that can have an impact to the following operating condition except style, thus the traffic information packet
Include such as conversion of environment car speed, signal lamp and road pedestrian's random walk.To the oneself state information of the vehicle and
Traffic information establishes database respectively, constructs the driver style deep layer and traffic respectively using the sample in the database
The deep layer convolutional neural networks model of information.
In the preferred embodiment of the application, as shown in figure 3, each network layer of the neural network is by multiple
Neural network is added in the deepness belief network end that Gauss-Bernoulli Jacob is limited Boltzmann machine superposition composition, utilizes depth conviction
The future short-term operating condition that the feature that network generates carries out the vehicle is predicted in real time.The training process of whole network is mainly by two
It is grouped as, a part is that successively trained multiple Gauss-Bernoulli Jacob are limited Boltzmann machine, each Gauss-Bernoulli Jacob is limited glass
The graceful machine model of Wurz is expressed using the joint probability based on energy.Another part of model training process is under Training
Small parameter perturbations, concrete operations are that end introduces neural net regression layer, form deep layer convolutional neural networks structure, are passed using reversed
It broadcasts algorithm to be finely adjusted the parameter of pre-training, the more new formula of the parameter in trim process is as follows:
Wherein J (W, b；X, y) be model loss function, by parameter W, b and input x and output y are determined, δ is residual error, 1
Represent the number of plies, w(1)It is weighting parameter, b(1)It is offset parameter, a(1)It is activation value, m is sample value quantity, and a is learning rate, w(1)
It is regular terms, λ is regularization coefficient.
In the preferred embodiment of the application, based on deep layer convolutional neural networks model realization prediction operating condition
Process, which can be used to lower mapping relations and propagated forward formula, to be indicated:
F (traffic information, driving style information；W, b)=v
z(l+1)=W(l)a(l)+b(l)
a(l+1)=f (z(l+1))
Wherein v is Vehicle Speed, and 1 represents the number of plies, w(1)It is weighting parameter, b(1)It is offset parameter, a(1)It is activation
Value, z(1+1)Be unit weighted input and.
In the preferred embodiment of the application, the life model of the power battery in the step 4 be lithium from
The cycle life empirical model of sub- power battery, power battery capacitance loss is introduced into the objective function of energy optimization with reality
Existing multiple-objection optimization management, the empirical model that power battery capacity changes with charging and discharging currents are as follows:
Wherein QlossIt is power battery loss capacity, DexpIt is pre-exponential factor, with CrateInversely, R is gas constant,
TbattIt is the average absolute temperature of battery, AhIt is power battery accumulation charge and discharge ampere-hour number.
In the preferred embodiment of the application, the step 4 further includes establishing about power battery service life, traveling energy
Measure the cost function of consumption.
It although an embodiment of the present invention has been shown and described, for the ordinary skill in the art, can be with
A variety of variations, modification, replacement can be carried out to these embodiments without departing from the principles and spirit of the present invention by understanding
And modification, the scope of the present invention is defined by the appended.
Claims (9)
1. a kind of energy management method of the plug-in hybrid vehicle based on intelligent predicting, it is characterised in that: specifically include
Following steps:
Step 1, On-line testing correspond to the multidimensional driving cycle information of target travel route, based on deep learning algorithm to described
Target travel route establishes the reconstruction model of global driving cycle；
The reconstruction model of step 2, the global driving cycle established based in the step 1, establishes intensified learning network model,
Obtain the power battery optimal energy track of the plug-in hybrid vehicle；
Step 3 constructs driver style deep layer convolution mind according to the oneself state information and traffic information of the vehicle respectively
Deep layer convolutional neural networks model through network model and traffic information, extracts corresponding driver style feature and traffic information
Feature, the future short-term operating condition real-time prediction model that the vehicle is established based on deep learning algorithm；
Step 4, according to the life model of the power battery, with the power battery optimal energy track in the step 2
Institute is established in conjunction with the following short-term operating condition real-time prediction model established in the step 3 as final value constraint when rolling
State the control strategy of power battery.
2. the method as described in claim 1, it is characterised in that: the multidimensional driving cycle information of 1 On-line testing in the step
Including car flow information, signal information, pedestrian information and Weather information, the multidimensional driving cycle information extraction is from increasing income map
Service provider, traffic monitoring platform or vehicle-mounted vision system.
3. the method as described in claim 1, it is characterised in that: described in the step 2 establishes intensified learning network model,
The power battery optimal energy track for obtaining the plug-in hybrid vehicle, specifically includes: minimum with global energy consumption
Reinforcing as the intensified learning network model is rewarded.
4. the method as described in claim 1, it is characterised in that: the oneself state information of the vehicle in the step 3 include with
Steering, gas pedal or the relevant information of brake pedal；The traffic information include with environment car speed, signal lamp convert with
And the relevant information of road pedestrian's random walk；Data are established respectively to the oneself state information and traffic information of the vehicle
Library constructs the driver style deep layer convolutional neural networks model and traffic information using the sample in the database respectively
Deep layer convolutional neural networks model.
5. method as claimed in claim 4, it is characterised in that: the driver style deep layer convolutional neural networks model and friendship
Each network layer for communicating the neural network of the deep layer convolutional neural networks model of breath is to be limited glass by multiple Gauss-Bernoulli Jacob
The neural network is added in the deepness belief network end of the graceful machine superposition composition of Wurz；Training process mainly consists of two parts,
A part is that successively trained multiple Gauss-Bernoulli Jacob are limited Boltzmann machine；Another part is that the parameter under Training is micro-
It adjusts.
6. method as claimed in claim 5, it is characterised in that: the Gauss-Bernoulli Jacob is limited in Boltzmann machine, each
A Gauss-Bernoulli Jacob is limited Boltzmann machine model and is expressed using the joint probability based on energy；Ginseng in the trim process
Number more new formula is as follows:
Wherein J (W, b；X, y) be model loss function, by parameter W, b and input x and output y determine that δ is residual error, and l is represented
The number of plies, w(l)It is weighting parameter, b(l)It is offset parameter, a(l)It is activation value, m is sample value quantity, and α is learning rate, w(l)It is just
Then item, λ are regularization coefficients.
7. method as claimed in claim 6, it is characterised in that: be based on the driver style deep layer convolutional neural networks
The mistake that the deep layer convolutional neural networks model realization of model and traffic information predicts the future of the vehicle short-term operating condition in real time
Journey, which can be used to lower mapping relations and propagated forward formula, to be indicated:
F (traffic information, driving style information；W, b)=v
z(l+1)=W(l)a(l)+b(l)
a(l+1)=f (z(l+1))
Wherein v is Vehicle Speed, and l represents the number of plies, w(l)It is weighting parameter, b(l)It is offset parameter, a(l)It is activation value, z(l +1)Be unit weighted input and.
8. the method as described in claim 1, it is characterised in that: the life model of the power battery in the step 4 is adopted
With the cycle life empirical model of lithium-ion-power cell:
Wherein QlossIt is power battery loss capacity, BexpIt is pre-exponential factor, with CrateInversely, R is gas constant, Tbatt
It is the average absolute temperature of battery, AhIt is power battery accumulation charge and discharge ampere-hour number.
9. the method as described in claim 1, it is characterised in that: the step 4 further include establish about the power battery service life and
Travel the cost function of energy consumption.
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| CN108674411A (en) * | 2018-07-03 | 2018-10-19 | 肖金保 | A kind of Energy Management System for Hybrid Electric Vehicle |
| CN109034391A (en) * | 2018-08-17 | 2018-12-18 | 王玲 | The multi-source heterogeneous information RBM network integration framework and fusion method of automatic Pilot |
| CN109143870B (en) * | 2018-10-23 | 2021-08-06 | 宁波溪棠信息科技有限公司 | Multi-target task control method |
| CN112035942A (en) * | 2019-06-03 | 2020-12-04 | 上海汽车集团股份有限公司 | Energy consumption simulation method and device based on driving behaviors |
| CN110341690B (en) * | 2019-07-22 | 2020-08-04 | 北京理工大学 | PHEV energy management method based on deterministic strategy gradient learning |
| CN110509913B (en) * | 2019-08-29 | 2021-05-21 | 南京智慧光信息科技研究院有限公司 | Hybrid power propulsion method and robot system based on big data and artificial intelligence |
| CN110852482B (en) * | 2019-10-15 | 2020-12-18 | 江苏大学 | Real-time global optimization intelligent control system and method for fuel cell bus |
| KR20210076223A (en) * | 2019-12-13 | 2021-06-24 | 현대자동차주식회사 | Hybrid vehicle and method of controlling the same |
| CN111267830B (en) * | 2020-02-10 | 2021-07-09 | 南京航空航天大学 | Hybrid power bus energy management method, device and storage medium |
| CN111367172B (en) * | 2020-02-28 | 2021-09-21 | 华南理工大学 | Hybrid system energy management strategy based on reverse deep reinforcement learning |
| CN111832881A (en) * | 2020-04-30 | 2020-10-27 | 北京嘀嘀无限科技发展有限公司 | Method, medium and electronic device for predicting electric vehicle energy consumption based on road condition information |
| CN112097783B (en) * | 2020-08-14 | 2022-05-20 | 广东工业大学 | Electric taxi charging navigation path planning method based on deep reinforcement learning |
| 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 |
| CN113246797B (en) * | 2021-06-04 | 2023-05-12 | 广州小鹏汽车科技有限公司 | Method and device for predicting service life of battery |
| CN113264031B (en) * | 2021-07-07 | 2022-04-29 | 重庆大学 | Hybrid power system control method based on road surface identification and deep reinforcement learning |
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| US8225074B2 (en) * | 2008-10-02 | 2012-07-17 | Nec Laboratories America, Inc. | Methods and systems for managing computations on a hybrid computing platform including a parallel accelerator |
| CN102765388B (en) * | 2012-07-03 | 2014-09-10 | 清华大学 | Vehicle control method based on multi-information integration |
| CN103112450B (en) * | 2013-02-27 | 2015-06-24 | 清华大学 | Real-time optimized control method for plug-in parallel hybrid electric vehicle |
| US10474628B2 (en) * | 2015-10-08 | 2019-11-12 | Via Alliance Semiconductor Co., Ltd. | Processor with variable rate execution unit |
| CN105416077B (en) * | 2015-12-01 | 2017-07-28 | 北京理工大学 | The EMS and management method of a kind of electric automobile |
| CN107187442B (en) * | 2017-05-18 | 2019-06-07 | 中国第一汽车股份有限公司 | Plug-in hybrid electric automobile Energy Management System based on operating condition prediction |
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