CN105528498B - Net connection intelligent electric vehicle integrated modelling and integrated control method - Google Patents
Net connection intelligent electric vehicle integrated modelling and integrated control method Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
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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
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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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
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
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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 invention discloses a kind of net connection intelligent electric vehicle integrated modelling and integrated control methods, it is characterised in that：Including：（1）Intelligent electric vehicle Topology Structure Design, test and theoretical analysis method；（2）The index system of the consistent serviceability of new assessment models；（3）It is theoretical to establish the stability of Predictive Control System and robust analysis under stochastic system frame for comprehensive car networking element, communication delay and the relationship for changing operating condition and real-time integrated control system；（4）Change operating condition with actual traffic digital simulation, has studied the validity of prediction battery management system in real time.The present invention establishes intelligent mixed power electric vehicle integrated modelling and integrated control method based on car networking data, so as to improve vehicle energy utilization efficiency and travel time efficiency, and guarantee safety, effective forecast Control Algorithm is provided for the traffic control system under current car networking environment and big data environment.Invention has important theoretical value and practical significance.
Description
Technical field
The present invention relates to a kind of net connection intelligent electric vehicle integrated modelling and integrated control method, in particular to a kind of realities
When optimal net connection intelligent electric vehicle integrated modelling and integrated control method.
Background technique
With the high speed development of global economy, energy and environmental problem becomes increasingly conspicuous, and energy saving, protection environment has become
The significant challenge of countries in the world facing.Orthodox car leads to oil shortage, tail gas to the dependence of single petroleum resources
Discharge also serious ground contamination environment, research new-energy automobile the relevant technologies have become future automobile industrial expansion direction.Beauty
The countries and regions such as state, Japan, European Union will all develop safety economy and clean transportation and energy as national energy strategy and vapour
The important content of vehicle strategy of industrial development.
The new energy power vehicle that north america is promoted mainly uses hybrid power system, and the U.S. is still the largest new energy
Source sale of car state, cumulative sale 120,000 in 2014.It is European mainly to use hybrid power system and plug-in hybrid system
System starts to promote quantity about 2500 using online fast charging system (lithium titanate anode battery and super capacitor).Japan mainly with
Hybrid power is main technological route, is maximum hybrid power car selling market in the world.
China is also made that specific support to the development of new-energy automobile industry.State Council's publication in 2010《About adding
The fast decision cultivated with development strategy new industry》, determine using new-energy automobile as one of seven great strategy new industries.
2012《Energy conservation and new-energy automobile industrial development planning（2012-the year two thousand twenties）》It proposes using pure electric drive as new-energy automobile
The main strategic orientation of development and auto industry transition, current emphasis promote pure electric automobile and plug-in hybrid-power automobile to produce
Industry.《Planning》It is proposed that pure electric automobile and plug-in hybrid-power automobile added up volume of production and marketing and reach 500,000 by 2015；It arrives
The year two thousand twenty, pure electric automobile and plug-in hybrid-power automobile production capacity reach 2,000,000, and adding up volume of production and marketing is more than 5,000,000
?.
Automobile industry is the important industry of current era world economy, in recent years, new round scientific and technological revolution and Industrial Revolution
Just develop in depth, generation information technology represented by the internet merges with the acceleration of automobile industry and pushed automobile product
The deep reform of form and distribution, automobile have started the direction evolution to Large-scale Mobile intelligent terminal.Automobile, information, internet
Equal colleges and universities of industry and enterprises institute and national governments increase the deployment to intelligent network connection development of automobile one after another, and industry development is presented
New developing direction and trend.In this context, orthodox car enterprise accelerates the development of intelligent automobile, Large-Scale Interconnected net enterprise one after another
Industry accelerates to permeate and be laid out to intelligent automobile industry one after another, and automobile industry value chain accelerates remodeling just under intelligentized promotion.
On the other hand, environment and energy crisis are faced, the intelligent car networking system for establishing green high-efficient safety is that section is implemented in countries in the world
The important measures of energy emission reduction.Compared to traditional internal-combustion engine vehicle, the electric vehicle with more preferable economy, discharge and net connection performance
Just becoming the important component of intelligent car networking system.It is electronic as the essential elements of intelligent electric vehicle networked system
The integrated modelling of vehicle is most important.In view of the property complicated and changeable of vehicle net environment, network internal big data state and biography
The shortcomings that system method, the network management of intelligent electric vehicle and integrated control method based on model are studied to have obtained international academic community
With the extensive concern of engineering circles, development to intelligent electric vehicle networking technology and universal there is great scientific value.It is another
Aspect, as the key energy unit of electric vehicle, the performance of power battery directly affects the fuel economy and power of vehicle
Performance.In order to ensure power battery can be safe under extremely complex vehicle running environment, functions reliably and efficiently run, need
Effective real-time management is implemented to power battery.The key technology of this kind of intelligent electric vehicle networked system is：Electric car system
Unite modeling technique, based on bus or train route/collaborative truck Vehicular intelligent driving technology and automotive self-adaptive cruise control technology etc..This item
Mesh mainly studies intelligent mixed power electric vehicle and plug-in hybrid electric vehicle integrated modelling and integrated controlling party
Method, comprehensively considers safety, energy conservation and environmental objective, carries out real-time dynamic cooperation control to system.Compared with developed countries, existing
Intelligent electric vehicle system there are still following problems：In terms of technical performance, system real time energy and inefficiency, volume and matter
Measure bigger than normal, degree of modularity deficiency；In terms of product integrated level, reliability and system application technology, still there is larger bottleneck.
Summary of the invention
It is an object of the invention to overcome it is above-mentioned in the prior art it is insufficient provide it is a kind of using real-time optimistic control guiding
Reliable performance, net connection intelligent electric vehicle integrated modelling high-efficient, at low cost and the integrated control side that system model is established
Method.
The technical proposal of the invention is realized in this way：A kind of net connection intelligent electric vehicle integrated modelling and integrated control
Method, this method include：（1）The configuration of intelligent electric vehicle model Automatic Optimal；（2）Intelligent electric vehicle based on car networking is pre-
The analysis of observing and controlling system, establishes the index system of the consistent serviceability of new assessment models；（3）Comprehensive car networking element, communication delay and
The relationship for changing operating condition and real-time integrated control system, establishes the stability of Predictive Control System and Shandong under stochastic system frame
Stick analysis theories, the automatic adjustment of weight model parameter；（4）Change operating condition with actual traffic digital simulation, studies prediction in real time
The affecting laws of the validity of battery management system, research model error and forecast interval length to system performance.
It is described（1）In intelligent electric vehicle model Automatic Optimal configuration include intelligent mixed power electric vehicle topology knot
Structure design, test and theoretical analysis method；Network analysis is compared the more existing hybrid vehicle of network analysis first and is opened up first
The characteristics of flutterring structure, then establishes their system dynamics model respectively, and analyzes its sytem matrix, explores its general rule
Rule；Finally designed a model Automatic Optimal configurator and software using the universal law of discovery, and using software obtain it is all can
The vehicle configuration of energy carries out network analysis to them, and carries out parameter optimization configuration.
It is described（2）The middle index system for establishing the consistent serviceability of new assessment models, index system include to model complexity
The overall merit of degree, the precision in training and verify data and the ability on Rate Based On The Extended Creep Model to vehicle platoon；Systematic comparison
The consistent serviceability of two common lumped parameter auto models, optimizes model parameter with advanced particle swarm algorithm
Configuration.
It is described（In 3）Comprehensive car networking element, communication delay and the relationship for changing operating condition and real-time integrated control system, build
The stability of Predictive Control System and robust analysis are theoretical under vertical stochastic system frame, the intelligence electricity of research real-time control guiding
The parsing of motor-car networked system parameter joins the correlation of element with smooth analytical function descriptive model parameter to net,
Keep it more useful in the real-time integrated control system based on model；Based on optimal model structure in real time, advanced design
The two-stage optimization that particle swarm algorithm and Model Predictive Control based on parameter automatic optimal combine, for systematically comparing
Design is systematically analyzed and assessed to optimization performance, robust performance and the efficiency of different control systems in conjunction with a large amount of test data
Control system is relative to the operating condition of variation, the robustness of communication delay and car networking element.
It is described（4）In with actual traffic digital simulation change operating condition, study in real time prediction battery management system validity,
Refer to the battery model using real-time optimistic control guiding, using real road traffic data analog variation operating condition, battery is held
Amount carries out predictive estimation, carries out intelligent recharge and discharge to battery, establishes the battery management system that can be applied to real vehicle real-time control；
Battery service life model is established relative to the analytical function that can efficiently survey parameter, battery capacity estimation device is established by Optimal Fitting,
Then the capability value of all batteries is estimated using the estimator obtained.
It is described（4）Middle research model error and forecast interval length to car-following model, hand over the affecting laws of system performance
Through-flow model, surrounding vehicles model, road grade model and traffic lights information model increase error, search model error
Influence to predictive control strategy；Since the time constant of battery is more many slowly than engine and motor, the length of forecast interval
The short influence to battery is bigger than engine and motor.
Intelligent electric vehicle PREDICTIVE CONTROL analysis based on car networking, this project is quasi- to take predictive control algorithm to intelligent electricity
Motor-car carries out overall-in-one control schema；Under car networking environment, information has big data feature, how to effectively utilize these data pair
The operating condition of vehicle predict most important；It, can be with global excellent to the optimal control of vehicle after knowing the operating condition of vehicle
Change algorithm Dynamic Programming and finds out global optimum's amount；But since we can not obtain whole vehicle working condition information, we are only
Range optimization strategy i.e. Model Predictive Control strategy can be taken.The basic principle of Model Predictive Control is：In each sampling
It carves, system future cost function is predicted according to prediction model, by being carried out to the performance indicator in future anticipation section
Optimization, and feedback compensation is carried out according to the output of actual measurement object, optimization process is converted by control strategy design, passes through solution
The optimization problem of corresponding forecast interval obtains control sequence, and first control amount of sequence is acted on system, realizes feedback
Control, later in next sampling instant, forecast interval is pushed forward, constantly repeats the process.With the mixed of foundation
Power vehicle system model is closed, system optimal control problem is formulated, optimal control problem is solved by Fast numerical method, is obtained
To system optimal control sequence, in system, forecast interval pushes forward first control amount of application sequence, repeats above-mentioned
Process.By the location information of global positioning system acquisition vehicle, fed back as real-time vehicle state；By trailer-mounted radar speed measuring device
Front vehicles speed is acquired, tracing control is used for.Traffic signal information and real-time road condition information are acquired by intelligent transportation system,
For intellectual traffic control.Storage battery charge state is estimated using the battery information of acquisition by Kalman filter.
The battery target state-of-charge of hybrid vehicle is designed according to road slope information, to recycle more free regenerative brakings
Energy；The performance indicator of optimum control is engine fuel economy, system restriction be vehicle safety spacing, rotation be revolving speed and
Torque constraint, battery power and state-of-charge constraint etc..The input quantity of PREDICTIVE CONTROL is engine, motor and friction catch
Torque；Energy-saving principle is to recycle vehicle deceleration regeneration braking energy as far as possible using future trajectory traffic information and prevent vehicle from meeting with
Meet red light damped condition.
The automatic adjustment of weight model parameter automatically adjusts weight coefficient using particle swarm algorithm.It determines with driver characteristics
Determine vehicle future travel operating condition, and explores car-following model and road grade model, traffic flow model, surrounding vehicles model and friendship
Affecting laws of the ventilating signal lamp information model to hybrid vehicle storage battery charge state.Integrated forecasting future trajectory traffic letter
Breath optimizes the energy distribution of hybrid vehicle, inquires into the real time control algorithms that can carry out real vehicle control.
The good effect that technical solution of the present invention generates is as follows：The present invention uses the system mould of real-time optimistic control guiding
Type carries out PREDICTIVE CONTROL to system, carries out intelligent recharge and discharge to battery using real road traffic data analog variation operating condition,
Establish the Full Vehicle System that can be applied to real vehicle real-time control.In addition, technical solution of the present invention also has the advantage that：
First, the present invention is integrated electric Vehicular system, has that high-efficient, to detect and control precision high, at low cost, steady
The features such as qualitative strong.
Second, main control chip of the invention is newest DSP integrated chip, has the characteristics that sampling precision is high, at low cost.
Third, intelligent electric vehicle system of the invention can carry out self adaptive control according to vehicle future operating condition.
4th, parameter On-line Estimation algorithm of the invention carries out online optimal estimation by particle swarm algorithm, improves and estimate
The precision and efficiency of meter.
5th, Robust Control Algorithm of the invention improves the precision and timeliness of control by pattern-recognition operating condition.
Detailed description of the invention
Fig. 1 is the intelligent electric vehicle research technical scheme figure of model-driven of the present invention.
Fig. 2 is the technology of the present invention route map.
Fig. 3 is present system control strategy flow chart.
Fig. 4 is predictive controller structure chart of the present invention.
Specific embodiment
Fig. 1 is technical solution of the present invention figure.That is the intelligent electric vehicle research of model-driven.First to two class different topologies
The vehicle of structure carries out a large amount of test analysis, to establish two multi-functional databases.Then, database, sequence are based on
Ground is for auto model structure compares, the optimal parsingization of model parameter, car networking system real-time optimistic control and battery are managed in real time
The problems such as reason system development system study.
Fig. 2 is the technology of the present invention route map, is tested using Systems Theory modeling, numerical simulation of optimum emulation and experiment porch
The research method that card analysis combines walks the technology path verified in exploitation with the system development technology based on model.It is first
First, intelligent electric vehicle Automatic Optimal configuration software is established on Matlab/Simulink；Then, electric vehicle is established in integration
Full Vehicle System simulation model, including vehicle, engine, motor, battery and transmission model, and integrated design PREDICTIVE CONTROL is calculated
Method, including top layer vehicle predictive controller and bottom feedback controller, to car networking element (traffic lights, traffic flow, surrounding
Vehicle) carry out forecast analysis；Finally, joining to the integrated control system of design relative to vehicle on intelligent electric vehicle experiment porch
Real-time, stability, the robustness of net element, communication delay and variation operating condition are analyzed.
Fig. 3 is system control strategy flow chart.With the hybrid vehicle system model of foundation, system optimal is formulated
Control problem solves optimal control problem by Fast numerical method, obtains system optimal control sequence, and the first of application sequence
A control amount pushes forward in system, forecast interval, repeats the above process.
Fig. 4 is predictive controller structure chart.By the location information of global positioning system acquisition vehicle, as real-time vehicle shape
State feedback.Front vehicles speed is acquired by trailer-mounted radar speed measuring device, is used for tracing control.Traffic is acquired by intelligent transportation system
Signal message and real-time road condition information are used for intellectual traffic control.The battery information of acquisition is utilized by Kalman filter
Storage battery charge state is estimated.The battery target state-of-charge of hybrid vehicle is set according to road slope information
Meter, to recycle more free regenerating braking energies.The performance indicator of optimum control is engine fuel economy, system restriction
It is revolving speed and torque constraint, battery power and state-of-charge constraint etc. for vehicle safety spacing, rotation.The input of PREDICTIVE CONTROL
Amount is engine, motor and friction braking torque.Energy-saving principle is using future trajectory traffic information, and recycling vehicle as far as possible subtracts
Rapid regeneration braking energy and prevent vehicle meet with red light damped condition.
Net connection intelligent electric vehicle integrated modelling and integrated control method, as shown in Figure 1,2,3, 4, this method includes：
（1）The configuration of intelligent electric vehicle model Automatic Optimal；（2）Intelligent electric vehicle PREDICTIVE CONTROL analysis based on car networking, is established
The index system of the consistent serviceability of new assessment models；（3）Comprehensive car networking element, communication delay and variation operating condition and in real time collection
At the relationship of control system, the stability of Predictive Control System and robust analysis theory, power under stochastic system frame are established
The automatic adjustment of molality shape parameter；（4）Change operating condition with actual traffic digital simulation, studies and predict having for battery management system in real time
The affecting laws of effect property, research model error and forecast interval length to system performance.
It is described（1）In intelligent electric vehicle model Automatic Optimal configuration include intelligent mixed power electric vehicle topology knot
Structure design, test and theoretical analysis method；First network analysis more existing hybrid vehicle topological structure the characteristics of, therefrom
Two class topological structures are selected, the energy conservation and net connection performance of these two types of topological structures are analyzed.Then the system for establishing them respectively is dynamic
Mechanical model, and its sytem matrix is analyzed, explore its universal law；It is finally designed a model using the universal law of discovery automatic excellent
Change configurator and software, and obtain all possible vehicle configuration using software, network analysis is carried out to them, and joined
Number is distributed rationally, has been built intelligent electric vehicle topological structure test macro, has been distributed auto model and specifications parameter rationally, is designed
Test program establishes auto model database.
Intelligent electric vehicle PREDICTIVE CONTROL analysis based on car networking, this project is quasi- to take predictive control algorithm to intelligent electricity
Motor-car carries out overall-in-one control schema.Under car networking environment, information has big data feature, how to effectively utilize these data pair
The operating condition of vehicle predict most important.It, can be with global excellent to the optimal control of vehicle after knowing the operating condition of vehicle
Change algorithm Dynamic Programming and finds out global optimum's amount.But since we can not obtain whole vehicle working condition information, we are only
Range optimization strategy i.e. Model Predictive Control strategy can be taken.The basic principle of Model Predictive Control is：In each sampling
It carves, system future cost function is predicted according to prediction model, by being carried out to the performance indicator in future anticipation section
Optimization, and feedback compensation is carried out according to the output of actual measurement object, optimization process is converted by control strategy design, passes through solution
The optimization problem of corresponding forecast interval obtains control sequence, and first control amount of sequence is acted on system, realizes feedback
Control, later in next sampling instant, forecast interval is pushed forward, constantly repeats the process.With the mixed of foundation
Power vehicle system model is closed, system optimal control problem is formulated, optimal control problem is solved by Fast numerical method, is obtained
To system optimal control sequence, in system, forecast interval pushes forward first control amount of application sequence, repeats above-mentioned
Process.By the location information of global positioning system acquisition vehicle, fed back as real-time vehicle state.By trailer-mounted radar speed measuring device
Front vehicles speed is acquired, tracing control is used for.Traffic signal information and real-time road condition information are acquired by intelligent transportation system,
For intellectual traffic control.Storage battery charge state is estimated using the battery information of acquisition by Kalman filter.
The battery target state-of-charge of hybrid vehicle is designed according to road slope information, to recycle more free regenerative brakings
Energy.The performance indicator of optimum control is engine fuel economy, system restriction be vehicle safety spacing, rotation be revolving speed and
Torque constraint, battery power and state-of-charge constraint etc..The input quantity of PREDICTIVE CONTROL is engine, motor and friction catch
Torque.Energy-saving principle is to recycle vehicle deceleration regeneration braking energy as far as possible using future trajectory traffic information and prevent vehicle from meeting with
Meet red light damped condition.
It is described（2）The middle index system for establishing the consistent serviceability of new assessment models, index system include to model complexity
The overall merit of degree, the precision in training and verify data and the ability on Rate Based On The Extended Creep Model to vehicle platoon；Systematic comparison
The consistent serviceability of two common lumped parameter auto models, optimizes model parameter with advanced particle swarm algorithm
Configuration.
It is described（3）Middle comprehensive car networking element, communication delay and the relationship for changing operating condition and real-time integrated control system, build
The stability of Predictive Control System and robust analysis are theoretical under vertical stochastic system frame, the intelligence electricity of research real-time control guiding
The parsing of motor-car networked system parameter joins the correlation of element with smooth analytical function descriptive model parameter to net,
Keep it more useful in the real-time integrated control system based on model；Based on optimal model structure in real time, advanced design
The two-stage optimization that particle swarm algorithm and Model Predictive Control based on parameter automatic optimal combine, for systematically comparing
Design is systematically analyzed and assessed to optimization performance, robust performance and the efficiency of different control systems in conjunction with a large amount of test data
Control system is relative to the operating condition of variation, the robustness of communication delay and car networking element.
It is described（4）In with actual traffic digital simulation change operating condition, study in real time prediction battery management system validity,
Refer to the battery model using real-time optimistic control guiding, using real road traffic data analog variation operating condition, battery is held
Amount carries out predictive estimation, carries out intelligent recharge and discharge to battery, establishes the battery management system that can be applied to real vehicle real-time control；
Battery service life model is established relative to the analytical function that can efficiently survey parameter, battery capacity estimation device is established by Optimal Fitting,
Then the capability value of all batteries is estimated using the estimator obtained.
It is described（4）Middle research model error and forecast interval length to car-following model, hand over the affecting laws of system performance
Through-flow model, surrounding vehicles model, road grade model and traffic lights information model increase error, search model error
Influence to predictive control strategy；Since the time constant of battery is more many slowly than engine and motor, the length of forecast interval
The short influence to battery is bigger than engine and motor.
The present invention is ground using what Systems Theory modeling, numerical simulation of optimum emulation and experiment porch verifying analysis combined
Study carefully method, with the system development technology based on model, walks the technology path verified in exploitation.Firstly, in Matlab/
Intelligent electric vehicle Automatic Optimal configuration software is established on Simulink；Then, the emulation of electric vehicle Full Vehicle System is established in integration
Model, including vehicle, engine, motor, battery and transmission model, and integrated design predictive control algorithm, including top layer are whole
Vehicle predictive controller and bottom feedback controller predict car networking element (traffic lights, traffic flow, surrounding vehicles)
Analysis；Finally, prolonging to the integrated control system of design relative to car networking element, communication on intelligent electric vehicle experiment porch
It is analyzed late with real-time, stability, the robustness of variation operating condition.
Claims (1)
1. a kind of net connection intelligent electric vehicle integrated modelling and integrated control method, it is characterised in that：This method includes：（1）
The configuration of intelligent electric vehicle model Automatic Optimal；（2）Intelligent electric vehicle PREDICTIVE CONTROL analysis based on car networking, is established new
The index system of the consistent serviceability of assessment models；（3）Comprehensive car networking element, communication delay and variation operating condition and in real time integrated control
The relationship of system processed establishes the stability of Predictive Control System and robust analysis theory, weight model under stochastic system frame
Parameter automatic adjustment；（4）Change operating condition with actual traffic digital simulation, study the validity of prediction battery management system in real time,
The affecting laws of research model error and forecast interval length to system performance；（1）In intelligent electric vehicle model it is automatically excellent
Changing configuration includes intelligent mixed power electric vehicle Topology Structure Design, test and theoretical analysis method；Network analysis ratio first
The characteristics of more existing hybrid vehicle topological structure, their system dynamics model is then established respectively, and analyze it and be
System matrix explores its rule；It is finally designed a model Automatic Optimal configurator and software using the rule of discovery, and using soft
Part obtains vehicle configuration, carries out network analysis to them, and carry out parameter optimization configuration；（2）It is middle to establish new assessment models one
The index system of serviceability is caused, index system includes the precision and popularization to model complexity, in training and verify data
The overall merit of ability on model to vehicle platoon；Systematic comparison two common lumped parameter auto models it is consistent useful
Property, configuration is optimized to model parameter with particle swarm algorithm；It is described（3）Middle comprehensive car networking element, communication delay and change
The relationship of chemical industry condition and real-time integrated control system establishes the stability and robustness of Predictive Control System under stochastic system frame
Analysis theories, the parsing of the intelligent electric vehicle networked system parameter of research real-time control guiding, i.e., with smooth parsing letter
Number descriptive model parameter keeps it more useful in the real-time integrated control system based on model the correlation of net connection element；
Based on model structure optimal in real time, design what particle swarm algorithm and Model Predictive Control based on parameter automatic optimal combined
Two-stage optimization is surveyed for optimization performance, robust performance and the efficiency of systematically more different control systems in conjunction with a large amount of
Data are tried, systematically analyze and assess design control system relative to the operating condition of variation, the Shandong of communication delay and car networking element
Stick；It is described（4）In with actual traffic digital simulation change operating condition, study in real time prediction battery management system validity, be
Refer to the battery model using real-time optimistic control guiding, using real road traffic data analog variation operating condition, to battery capacity
It carries out predictive estimation, intelligent recharge and discharge is carried out to battery, establish the battery management system that can be applied to real vehicle real-time control；It builds
Battery service life model is found relative to the analytical function that can efficiently survey parameter, battery capacity estimation device is established by Optimal Fitting, so
The capability value of all batteries is estimated using the estimator obtained afterwards；It is described（4）Middle research model error and forecast interval length pair
The affecting laws of system performance believe car-following model, traffic flow model, surrounding vehicles model, road grade model and traffic
Signal lamp information model increases error, influence of the search model error to predictive control strategy.
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| CN108058615B (en) | 2016-11-09 | 2022-02-25 | 华为技术有限公司 | Method and device for recovering braking energy of vehicle |
| CN108365986B (en) * | 2018-02-07 | 2019-06-21 | 重庆大学 | Hybrid power fleet based on Model Predictive Control cooperates with energy management method |
| CN110007234B (en) * | 2019-03-12 | 2020-02-07 | 清华大学 | Performance test method and system of power battery/battery pack and electric automobile |
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| CN111325983B (en) * | 2020-03-12 | 2021-01-15 | 合肥工业大学 | Intelligent networking automobile power demand online prediction method based on space-time cooperation |
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