CN105528498A - Network connection intelligent electric vehicle integration modeling and integrated control method - Google Patents
Network connection intelligent electric vehicle integration modeling and integrated control method Download PDFInfo
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Abstract
The invention discloses a network connection intelligent electric vehicle integration modeling and integrated control method, which is characterized by comprising the steps of (1) topological structural design, test and a theoretical analysis method of an intelligent electric vehicle; (2) an index system of consistent usefulness of a new evaluation model; (3) through combining the relationships among a comprehensive internet of vehicle factor, communication delay, changing operating condition and an integrated control system, and stability and robustness analysis theories of a predictive control system under a random system framework is established; (4) the changing operating condition is simulated by virtue of actual traffic data, and the validity of a real-time predictive battery management system is studied. The invention establishes an internet of vehicle data based intelligent hybrid power electric vehicle integration modeling and integrated control method, so that the energy utilization efficiency and the travel time efficiency of a vehicle can be improved, and the safety is ensured, and the effective predictive control method is provided for a traffic control system under an current internet of vehicle environment and an big data environment. The method disclosed by the invention has important theoretical 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, particularly a kind of net of real-time optimum joins intelligent electric vehicle integrated modelling and integrated control method.
Background technology
Along with the high speed development of global economy, the energy and environmental problem become increasingly conspicuous, and economize energy, protection of the environment have become the significant challenge of countries in the world facing.The dependence of orthodox car to single petroleum resources causes oil shortage, and its exhaust emissions also seriously pollutes environment, and research new-energy automobile correlation technique has become future automobile industrial expansion direction.The countries and regions such as the U.S., Japan, European Union all using development safety economy and clean transportation and energy as national energy strategy and the important content of Automobile Industrial Development Strategies.
The novel energy power vehicle that north america is promoted mainly adopts hybrid power system, and the U.S. is still maximum new forms of energy sale of car state, cumulative sale 120,000 in 2014.Europe is main adopts hybrid power system and plug-in hybrid system, starts the online fast charging system (lithium titanate anode battery and super capacitor) of application, promotes quantity about 2500.Japan is main is main technological route with hybrid power, is hybrid power car selling market maximum in the world.
China has also made clear and definite support to the development of new-energy automobile industry.Within 2010, State Council issues " decision about accelerating cultivation and development strategy new industry ", determines new-energy automobile as one of seven great strategy new industries.Within 2012, " energy-conservation with new-energy automobile industrial development planning (2012-2020) " proposes with pure electric drive for the main strategic orientation that new-energy automobile develops and auto industry makes the transition, and current emphasis advances pure electric automobile and plug-in hybrid-power automobile industrialization." planning " proposed by 2015, and pure electric automobile and plug-in hybrid-power automobile add up volume of production and marketing and reach 500,000; To the year two thousand twenty, pure electric automobile and plug-in hybrid-power automobile productive capacity reach 2,000,000, and accumulative 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, be that the deep reform having promoted automobile product form and distribution is merged in the generation information technology of representative and the acceleration of automobile industry with internet, automobile has started the direction evolution to Large-scale Mobile intelligent terminal.The colleges and universities of industry and enterprise institute such as automobile, information, internet and national governments strengthen the deployment to intelligent network connection development of automobile one after another, and industry development presents new developing direction and trend.In this context, orthodox car enterprise accelerates the development of intelligent automobile one after another, and Large-Scale Interconnected net enterprise accelerates one after another to the infiltration of intelligent automobile industry and layout, and automobile industry value chain is just being accelerated to reinvent under intelligentized promotion.On the other hand, in the face of environment and energy crisis, the intelligent vehicle networked system setting up green high-efficient safety is the important measures that energy-saving and emission-reduction are implemented in countries in the world.Compared to traditional internal-combustion engine vehicle, there is better economy, the electric vehicle of discharge and net connection performance just becoming the important component part of intelligent vehicle networked system.As the essential elements of intelligent electric vehicle networked system, the integrated modelling of electric vehicle is most important.Consider the property complicated and changeable of car net environment, the shortcoming of the large data mode of network internal and classic method, the extensive concern of international academic community and engineering circles is obtained, to the development of intelligent electric vehicle networking technology with universal there is great scientific value based on the intelligent electric vehicle network management of model and integrated control method research.On the other hand, as the key energy unit of electric vehicle, the performance of electrokinetic cell directly affects fuel economy and the power performance of vehicle.Can safety under very complicated vehicle running environment in order to ensure electrokinetic cell, reliably and efficiently run, need to implement effective real-time management to electrokinetic cell.The gordian technique of this kind of intelligent electric vehicle networked system is: vehicle electric system modeling technique, control technology etc. of cruising based on the Vehicular intelligent driving technology of bus or train route/collaborative truck and automotive self-adaptive.This project mainly studies intelligent mixed power electric vehicle and plug-in hybrid electric vehicle integrated modelling and integrated control method, considers safety, energy saving standard target, carries out real-time dynamic cooperation control to system.Compared with developed countries, still there is following problem in existing intelligent electric vehicle system: from technical feature, system real time energy and inefficiency, volume and quality bigger than normal, the degree of modularity is not enough; In product integrated level, reliability and system application technology, still there is larger bottleneck.
Summary of the invention
To the object of the invention is to overcome in above-mentioned prior art not enough dependable performance, efficiency is high, cost the is low net providing a kind of system model adopting real-time optimistic control lead to set up and join intelligent electric vehicle integrated modelling and integrated control method.
Technical scheme of the present invention is achieved in that a kind of net connection intelligent electric vehicle integrated modelling and integrated control method, and the method comprises: (1) intelligent electric vehicle model Automatic Optimal configures; (2) the intelligent electric vehicle PREDICTIVE CONTROL based on car networking is analyzed, and sets up the index system of the consistent serviceability of new assessment models; (3) relation of integrated car networking key element, communication delay and change operating mode and real-time integrated control system, establish stability and the robust analysis theory of Predictive Control System under stochastic system framework, weight model parameter regulates automatically; (4) with actual traffic digital simulation change operating mode, the validity of research real-time estimate battery management system, research model error and forecast interval length are to the affecting laws of system performance.
Intelligent electric vehicle model Automatic Optimal configuration in described (1) comprises intelligent mixed power electric vehicle Topology Structure Design, test and theoretical analysis method; First the feature of the systematic analysis first more existing hybrid vehicle topological structure of systematic analysis, then sets up their system dynamics model respectively, and analyzes its system matrix, explore its universal law; Finally utilize the universal law of discovery to design a model Automatic Optimal configurator and software, and utilize software to obtain all possible vehicle configuration, carry out systematic analysis to them, line parameter of going forward side by side is distributed rationally.
Set up the index system of the consistent serviceability of new assessment models in described (2), index system comprises model complexity, precision in training and verification msg and Rate Based On The Extended Creep Model to the comprehensive evaluation of the ability on vehicle platoon; The consistent serviceability of the lumped parameter auto model that systematic comparison two is conventional, uses advanced particle cluster algorithm to be optimized configuration to model parameter.
The relation of described (in 3) integrated car networking key element, communication delay and change operating mode and real-time integrated control system, stability and the robust analysis of setting up Predictive Control System under stochastic system framework are theoretical, research controls the parsing of the intelligent electric vehicle networked system parameter of guiding in real time, namely by the correlativity of smooth analytical function descriptive model parameter to net connection element, make it more useful in based on the real-time integrated control system of model; Based on model structure optimum in real time, the two-stage optimization that the particle cluster algorithm based on parameter automatic optimal of advanced design and Model Predictive Control combine, for the Optimal performance of systematically more different control system, robust performance and efficiency, in conjunction with a large amount of test datas, systematically assessment and analysis design con-trol system is relative to the robustness of the operating mode changed, communication delay and car networking key element.
With actual traffic digital simulation change operating mode in described (4), the validity of research real-time estimate battery management system, refer to the battery model adopting real-time optimistic control guiding, utilize real road traffic data analog variation operating mode, battery capacity is carried out to predicted estimate, carried out intelligent recharge and discharge to battery, sets up the battery management system that can be applied to real vehicle and control in real time; Set up battery service life model relative to the analytical function efficiently can surveying parameter, set up battery capacity estimation device by Optimal Fitting, then utilize the estimator of acquisition to estimate the capability value of all batteries.
In described (4), research model error and forecast interval length are to the affecting laws of system performance, increase error to car-following model, traffic flow model, surrounding vehicles model, road grade model and traffic lights information model, search model error is on the impact of predictive control strategy; Because the time constant of accumulator is more a lot of slowly than engine and motor, the length of forecast interval on the impact of accumulator than engine and motor large.
Intelligent electric vehicle PREDICTIVE CONTROL based on car networking is analyzed, and this project is intended taking predictive control algorithm to carry out overall-in-one control schema to intelligent electric vehicle; Under car networked environment, information has large data characteristics, and how effectively utilizing these data, to carry out prediction to the operating mode of vehicle most important; After knowing the operating mode of vehicle, global optimization approach dynamic programming just can be used to obtain global optimum's amount to the optimal control of vehicle; But because we can not obtain whole vehicle working condition information, we can only take range optimization strategy and Model Predictive Control strategy.The ultimate principle of Model Predictive Control is: in each sampling instant, according to forecast model, the following cost function of system is predicted, by being optimized the performance index in future anticipation interval, and carry out feedback compensation according to the output of actual measurement object, control strategy design is converted into optimizing process, control sequence is obtained by the optimization problem solving corresponding forecast interval, and first of sequence controlled quentity controlled variable is acted on system, realize FEEDBACK CONTROL, afterwards in next sampling instant, forecast interval is pushed forward, constantly repeats this process.Use the hybrid vehicle system model set up, formulistic system optimal control problem, solves optimal control problem by Fast numerical method, obtain system optimal control sequence, first controlled quentity controlled variable of application sequence is in system, and forecast interval pushes forward, and repeats said process.By the positional information of GPS collection vehicle, as real-time vehicle feedback of status; Front vehicles speed is gathered, for tracing control by trailer-mounted radar speed measuring device.Traffic signal information and real-time road condition information is gathered, for intellectual traffic control by intelligent transportation system.The battery information gathered is utilized to estimate storage battery charge state by Kalman filter.The accumulator target state-of-charge foundation road grade information design of hybrid vehicle, to reclaim more free regenerating braking energy; The performance index of optimum control are Fuel Economy, system restriction is vehicle safety spacing, rotation is rotating speed and torque constraints, accumulator power and state-of-charge constraint etc.The input quantity of PREDICTIVE CONTROL is engine, motor and friction catch moment; Energy-saving principle, for utilizing future trajectory transport information, reclaims vehicle deceleration regeneration braking energy as far as possible and prevents vehicle from meeting with red light damped condition.
Weight model parameter regulates automatically, adopts particle cluster algorithm automatically to regulate weight coefficient.Use driver characteristics to determine vehicle future travel operating mode, and explore car-following model and road grade model, traffic flow model, surrounding vehicles model and traffic lights information model to the affecting laws of hybrid vehicle storage battery charge state.Integrated forecasting future trajectory transport information, is optimized 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 scheme of the present invention produces is as follows: the system model that the present invention adopts real-time optimistic control to lead, utilize real road traffic data analog variation operating mode, system is carried out to PREDICTIVE CONTROL, carried out intelligent recharge and discharge to battery, sets up the Full Vehicle System that can be applied to real vehicle and control in real time.In addition, technical scheme of the present invention also has following advantage:
The first, the present invention is integrated electric Vehicular system, has the features such as efficiency is high, detection and control precision is high, cost is low, stability is strong.
The second, main control chip of the present invention is up-to-date DSP integrated chip, has the features such as sampling precision is high, cost is low.
3rd, intelligent electric vehicle system of the present invention can carry out adaptive control according to the following operating mode of vehicle.
4th, parameter On-line Estimation algorithm of the present invention, carries out online optimal estimation by particle cluster algorithm, improves precision and the efficiency of estimation.
5th, Robust Control Algorithm of the present invention, by pattern-recognition operating mode, improves precision and the timeliness of control.
Accompanying drawing explanation
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 process flow diagram.
Fig. 4 is predictive controller structural drawing of the present invention.
Embodiment
Fig. 1 is technical solution of the present invention figure.The i.e. intelligent electric vehicle research of model-driven.First a large amount of test analysis is carried out to the vehicle of two class different topology structures, thus set up two multi-functional databases.Then, based on database, sequence ground is studied for problem development systems such as auto model structure comparison, model parameter optimum parsingization, car networked system real-time optimistic control and battery real-time management systems.
Fig. 2 is the technology of the present invention route map, and the research method adopting Systems Theory modeling, numerical simulation of optimum emulation and experiment porch check analysis to combine, uses the system development technology based on model, walk the technology path of exploitation limit, limit checking.First, Matlab/Simulink sets up intelligent electric vehicle Automatic Optimal configuration software; Then, electric vehicle Full Vehicle System realistic model is set up in integration, comprise car load, engine, motor, battery and transmission model, and integrated design predictive control algorithm, comprise top layer car load predictive controller and bottom feedback controller, forecast analysis is carried out to car networking key element (traffic lights, traffic flow, surrounding vehicles); Finally, intelligent electric vehicle experiment porch is analyzed relative to the network real-time of key element, communication delay and change operating mode, stability, robustness of car the integrated control system of design.
Fig. 3 is system control strategy process flow diagram.Use the hybrid vehicle system model set up, formulistic system optimal control problem, solves optimal control problem by Fast numerical method, obtain system optimal control sequence, first controlled quentity controlled variable of application sequence is in system, and forecast interval pushes forward, and repeats said process.
Fig. 4 is predictive controller structural drawing.By the positional information of GPS collection vehicle, as real-time vehicle feedback of status.Front vehicles speed is gathered, for tracing control by trailer-mounted radar speed measuring device.Traffic signal information and real-time road condition information is gathered, for intellectual traffic control by intelligent transportation system.The battery information gathered is utilized to estimate storage battery charge state by Kalman filter.The accumulator target state-of-charge foundation road grade information design of hybrid vehicle, to reclaim more free regenerating braking energy.The performance index of optimum control are Fuel Economy, system restriction is vehicle safety spacing, rotation is rotating speed and torque constraints, accumulator power and state-of-charge constraint etc.The input quantity of PREDICTIVE CONTROL is engine, motor and friction catch moment.Energy-saving principle, for utilizing future trajectory transport information, reclaims vehicle deceleration regeneration braking energy as far as possible and prevents vehicle from meeting with red light damped condition.
Net connection intelligent electric vehicle integrated modelling and integrated control method, as shown in Figure 1,2,3, 4, the method comprises: (1) intelligent electric vehicle model Automatic Optimal configures; (2) the intelligent electric vehicle PREDICTIVE CONTROL based on car networking is analyzed, and sets up the index system of the consistent serviceability of new assessment models; (3) relation of integrated car networking key element, communication delay and change operating mode and real-time integrated control system, establish stability and the robust analysis theory of Predictive Control System under stochastic system framework, weight model parameter regulates automatically; (4) with actual traffic digital simulation change operating mode, the validity of research real-time estimate battery management system, research model error and forecast interval length are to the affecting laws of system performance.
Intelligent electric vehicle model Automatic Optimal configuration in described (1) comprises intelligent mixed power electric vehicle Topology Structure Design, test and theoretical analysis method; First the feature of the more existing hybrid vehicle topological structure of systematic analysis, therefrom selects two class topological structures, analyzes the energy-conservation of this two classes topological structure and net connection performance.Then set up their system dynamics model respectively, and analyze its system matrix, explore its universal law; The universal law of discovery is finally utilized to design a model Automatic Optimal configurator and software, and utilize software to obtain all possible vehicle configuration, systematic analysis is carried out to them, line parameter of going forward side by side is distributed rationally, build intelligent electric vehicle topological structure test macro, distribute auto model and specifications parameter rationally, design test program, set up auto model database.
Intelligent electric vehicle PREDICTIVE CONTROL based on car networking is analyzed, and this project is intended taking predictive control algorithm to carry out overall-in-one control schema to intelligent electric vehicle.Under car networked environment, information has large data characteristics, and how effectively utilizing these data, to carry out prediction to the operating mode of vehicle most important.After knowing the operating mode of vehicle, global optimization approach dynamic programming just can be used to obtain global optimum's amount to the optimal control of vehicle.But because we can not obtain whole vehicle working condition information, we can only take range optimization strategy and Model Predictive Control strategy.The ultimate principle of Model Predictive Control is: in each sampling instant, according to forecast model, the following cost function of system is predicted, by being optimized the performance index in future anticipation interval, and carry out feedback compensation according to the output of actual measurement object, control strategy design is converted into optimizing process, control sequence is obtained by the optimization problem solving corresponding forecast interval, and first of sequence controlled quentity controlled variable is acted on system, realize FEEDBACK CONTROL, afterwards in next sampling instant, forecast interval is pushed forward, constantly repeats this process.Use the hybrid vehicle system model set up, formulistic system optimal control problem, solves optimal control problem by Fast numerical method, obtain system optimal control sequence, first controlled quentity controlled variable of application sequence is in system, and forecast interval pushes forward, and repeats said process.By the positional information of GPS collection vehicle, as real-time vehicle feedback of status.Front vehicles speed is gathered, for tracing control by trailer-mounted radar speed measuring device.Traffic signal information and real-time road condition information is gathered, for intellectual traffic control by intelligent transportation system.The battery information gathered is utilized to estimate storage battery charge state by Kalman filter.The accumulator target state-of-charge foundation road grade information design of hybrid vehicle, to reclaim more free regenerating braking energy.The performance index of optimum control are Fuel Economy, system restriction is vehicle safety spacing, rotation is rotating speed and torque constraints, accumulator power and state-of-charge constraint etc.The input quantity of PREDICTIVE CONTROL is engine, motor and friction catch moment.Energy-saving principle, for utilizing future trajectory transport information, reclaims vehicle deceleration regeneration braking energy as far as possible and prevents vehicle from meeting with red light damped condition.
Set up the index system of the consistent serviceability of new assessment models in described (2), index system comprises model complexity, precision in training and verification msg and Rate Based On The Extended Creep Model to the comprehensive evaluation of the ability on vehicle platoon; The consistent serviceability of the lumped parameter auto model that systematic comparison two is conventional, uses advanced particle cluster algorithm to be optimized configuration to model parameter.
The relation of integrated car networking key element in described (3), communication delay and change operating mode and real-time integrated control system, stability and the robust analysis of setting up Predictive Control System under stochastic system framework are theoretical, research controls the parsing of the intelligent electric vehicle networked system parameter of guiding in real time, namely by the correlativity of smooth analytical function descriptive model parameter to net connection element, make it more useful in based on the real-time integrated control system of model; Based on model structure optimum in real time, the two-stage optimization that the particle cluster algorithm based on parameter automatic optimal of advanced design and Model Predictive Control combine, for the Optimal performance of systematically more different control system, robust performance and efficiency, in conjunction with a large amount of test datas, systematically assessment and analysis design con-trol system is relative to the robustness of the operating mode changed, communication delay and car networking key element.
With actual traffic digital simulation change operating mode in described (4), the validity of research real-time estimate battery management system, refer to the battery model adopting real-time optimistic control guiding, utilize real road traffic data analog variation operating mode, battery capacity is carried out to predicted estimate, carried out intelligent recharge and discharge to battery, sets up the battery management system that can be applied to real vehicle and control in real time; Set up battery service life model relative to the analytical function efficiently can surveying parameter, set up battery capacity estimation device by Optimal Fitting, then utilize the estimator of acquisition to estimate the capability value of all batteries.
In described (4), research model error and forecast interval length are to the affecting laws of system performance, increase error to car-following model, traffic flow model, surrounding vehicles model, road grade model and traffic lights information model, search model error is on the impact of predictive control strategy; Because the time constant of accumulator is more a lot of slowly than engine and motor, the length of forecast interval on the impact of accumulator than engine and motor large.
The research method that the present invention adopts Systems Theory modeling, numerical simulation of optimum emulation and experiment porch check analysis to combine, uses the system development technology based on model, walks the technology path of exploitation limit, limit checking.First, Matlab/Simulink sets up intelligent electric vehicle Automatic Optimal configuration software; Then, electric vehicle Full Vehicle System realistic model is set up in integration, comprise car load, engine, motor, battery and transmission model, and integrated design predictive control algorithm, comprise top layer car load predictive controller and bottom feedback controller, forecast analysis is carried out to car networking key element (traffic lights, traffic flow, surrounding vehicles); Finally, intelligent electric vehicle experiment porch is analyzed relative to the network real-time of key element, communication delay and change operating mode, stability, robustness of car the integrated control system of design.
Claims (6)
1. net connection intelligent electric vehicle integrated modelling and an integrated control method, is characterized in that: the method comprises: (1) intelligent electric vehicle model Automatic Optimal configures; (2) the intelligent electric vehicle PREDICTIVE CONTROL based on car networking is analyzed, and sets up the index system of the consistent serviceability of new assessment models; (3) relation of integrated car networking key element, communication delay and change operating mode and real-time integrated control system, establish stability and the robust analysis theory of Predictive Control System under stochastic system framework, weight model parameter regulates automatically; (4) with actual traffic digital simulation change operating mode, the validity of research real-time estimate battery management system, research model error and forecast interval length are to the affecting laws of system performance.
2. net connection intelligent electric vehicle integrated modelling according to claim 1 and integrated control method, is characterized in that: the intelligent electric vehicle model Automatic Optimal configuration in described (1) comprises intelligent mixed power electric vehicle Topology Structure Design, test and theoretical analysis method; First the feature of the more existing hybrid vehicle topological structure of systematic analysis, then sets up their system dynamics model respectively, and analyzes its system matrix, explore its universal law; Finally utilize the universal law of discovery to design a model Automatic Optimal configurator and software, and utilize software to obtain all possible vehicle configuration, carry out systematic analysis to them, line parameter of going forward side by side is distributed rationally.
3. net connection intelligent electric vehicle integrated modelling according to claim 1 and integrated control method, it is characterized in that: the index system setting up the consistent serviceability of new assessment models in described (2), index system comprises model complexity, precision in training and verification msg and Rate Based On The Extended Creep Model to the comprehensive evaluation of the ability on vehicle platoon; The consistent serviceability of the lumped parameter auto model that systematic comparison two is conventional, uses advanced particle cluster algorithm to be optimized configuration to model parameter.
4. net connection intelligent electric vehicle integrated modelling according to claim 1 and integrated control method, it is characterized in that: the relation of described (in 3) integrated car networking key element, communication delay and change operating mode and real-time integrated control system, stability and the robust analysis of setting up Predictive Control System under stochastic system framework are theoretical, research controls the parsing of the intelligent electric vehicle networked system parameter of guiding in real time, namely by the correlativity of smooth analytical function descriptive model parameter to net connection element, make it more useful in based on the real-time integrated control system of model; Based on model structure optimum in real time, the two-stage optimization that the particle cluster algorithm based on parameter automatic optimal of advanced design and Model Predictive Control combine, for the Optimal performance of systematically more different control system, robust performance and efficiency, in conjunction with a large amount of test datas, systematically assessment and analysis design con-trol system is relative to the robustness of the operating mode changed, communication delay and car networking key element.
5. net connection intelligent electric vehicle integrated modelling according to claim 1 and integrated control method, it is characterized in that: with actual traffic digital simulation change operating mode in described (4), the validity of research real-time estimate battery management system, refer to the battery model adopting real-time optimistic control guiding, utilize real road traffic data analog variation operating mode, battery capacity is carried out to predicted estimate, carried out intelligent recharge and discharge to battery, sets up the battery management system that can be applied to real vehicle and control in real time; Set up battery service life model relative to the analytical function efficiently can surveying parameter, set up battery capacity estimation device by Optimal Fitting, then utilize the estimator of acquisition to estimate the capability value of all batteries.
6. net joins intelligent electric vehicle integrated modelling and integrated control method according to claim 1 or 5, it is characterized in that: in described (4), research model error and forecast interval length are to the affecting laws of system performance, increase error to car-following model, traffic flow model, surrounding vehicles model, road grade model and traffic lights information model, search model error is on the impact of predictive control strategy; Because the time constant of accumulator is more a lot of slowly than engine and motor, the length of forecast interval on the impact of accumulator than engine and motor large.
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