CN107284441A - The energy-optimised management method of the adaptive plug-in hybrid-power automobile of real-time working condition - Google Patents

The energy-optimised management method of the adaptive plug-in hybrid-power automobile of real-time working condition Download PDF

Info

Publication number
CN107284441A
CN107284441A CN201710422212.2A CN201710422212A CN107284441A CN 107284441 A CN107284441 A CN 107284441A CN 201710422212 A CN201710422212 A CN 201710422212A CN 107284441 A CN107284441 A CN 107284441A
Authority
CN
China
Prior art keywords
mrow
msub
hybrid
plug
real
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201710422212.2A
Other languages
Chinese (zh)
Other versions
CN107284441B (en
Inventor
赵治国
沈沛鸿
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Tongji University
Original Assignee
Tongji University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Tongji University filed Critical Tongji University
Priority to CN201710422212.2A priority Critical patent/CN107284441B/en
Publication of CN107284441A publication Critical patent/CN107284441A/en
Application granted granted Critical
Publication of CN107284441B publication Critical patent/CN107284441B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W20/00—Control systems specially adapted for hybrid vehicles
    • 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
    • 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
    • B60W20/12—Controlling the power contribution of each of the prime movers to meet required power demand using control strategies taking into account route information
    • 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
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/14—Adaptive cruise control
    • B60W30/143—Speed control
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/105—Speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B60W2050/0028—Mathematical models, e.g. for simulation
    • B60W2050/0029—Mathematical model of the driver
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B60W2050/0028—Mathematical models, e.g. for simulation
    • B60W2050/0031—Mathematical model of the vehicle
    • 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
    • B60W2552/00—Input parameters relating to infrastructure
    • B60W2552/15—Road slope, i.e. the inclination of a road segment in the longitudinal direction
    • 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
    • B60W2555/00—Input parameters relating to exterior conditions, not covered by groups B60W2552/00, B60W2554/00
    • B60W2555/60—Traffic rules, e.g. speed limits or right of way
    • 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
    • B60W2720/00—Output or target parameters relating to overall vehicle dynamics
    • B60W2720/10—Longitudinal speed
    • 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
    • Y02T10/80—Technologies aiming to reduce greenhouse gasses emissions common to all road transportation technologies
    • Y02T10/84—Data processing systems or methods, management, administration

Landscapes

  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)
  • Hybrid Electric Vehicles (AREA)
  • Control Of Vehicle Engines Or Engines For Specific Uses (AREA)

Abstract

The present invention relates to a kind of energy-optimised management method of the adaptive plug-in hybrid-power automobile of real-time working condition, comprise the following steps：1) the real-time working condition information of each segmentation per paths is obtained；2) with the minimum optimization aim of accumulation equivalent fuel consumption of each segmentation of each path, the energy-optimised management strategy of plug-in hybrid-power automobile based on Dynamic Programming is set up using the limit value of the speed limit of each segmentation of every paths, traffic flow velocity and electrokinetic cell work electricity as constraints；3) each corresponding target economic speed of segmentation per paths is obtained, and is sent to Vehicle Controller；4) Vehicle Controller obtains the demand torque sequence at each moment in predicted time yardstick；5) to accumulate the minimum target of equivalent fuel consumption, real-time tracking target economic speed in predicted time yardstick.Compared with prior art, the present invention has according to real-time working condition information, the advantages of by the global optimum of plug-in hybrid-power automobile energy consumption with real-time optimal effective combination.

Description

The energy-optimised management method of the adaptive plug-in hybrid-power automobile of real-time working condition
Technical field
The present invention relates to plug-in hybrid-power automobile real-time power optimum management field, more particularly, to a kind of real-time work The energy-optimised management method of the adaptive plug-in hybrid-power automobile of condition.
Background technology
It is increasingly strict with automobile energy consumption and emission regulation demands, battery technology do not obtain breakthrough it Before, plug-in hybrid-power automobile will gradually replace conventional fuel oil automobile in longer period of time and turn into new-energy automobile Mainstream car design.Energy management strategies are the key technologies of plug-in hybrid vehicle, and it is by coordinating control engine and electricity Machine, so that the fuel economy of vehicle is optimal.
At present, rule-based plug-in hybrid-power automobile energy management strategies have been carried out commercial application, but not It can guarantee that energy consumption is optimal.Cause the equivalent fuel consumption at each moment most based on the minimum energy management strategies of equivalent fuel consumption It is excellent, but do not ensure that global optimum.And the energy management strategies based on dynamic programming algorithm can ensure plug-in hybrid The energy consumption global optimum of automobile, but it cannot be guaranteed that the real-time of energy optimization.
The present invention is in view of energy consumption important of the driving cycle for plug-in hybrid-power automobile.As car joins Network technology, especially electronic map, airmanship are continued to develop, and for plug-in hybrid-power automobile provide real-time working condition information There is provided possibility, meanwhile, also it is the adaptive optimal energy management of real-time working condition with the development and application of Optimized-control Technique Strategy provides new solution.
The content of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide a kind of real-time working condition is adaptive The energy-optimised management method of plug-in hybrid-power automobile answered.
The purpose of the present invention can be achieved through the following technical solutions：
A kind of energy-optimised management method of the adaptive plug-in hybrid-power automobile of real-time working condition, comprises the following steps：
1) cloud server obtains all paths from origin-to-destination, to every road according to the Origin And Destination of stroke Footpath is segmented, and obtains the real-time working condition information of each segmentation per paths, including the gradient of road, radius of curvature, traffic Flow velocity, speed limit and intersection；
2) with the minimum optimization aim of accumulation equivalent fuel consumption of each segmentation of each path, with every paths, each is segmented The limit value of speed limit, traffic flow velocity and electrokinetic cell work electricity sets up the plug-in mixing based on Dynamic Programming as constraints The energy-optimised management strategy of power vehicle；
3) plug-in hybrid-power automobile energy optimization strategy is solved using dynamic programming algorithm, obtains every road Each is segmented corresponding target economic speed in footpath, and the target economic speed is sent into Vehicle Controller；
4) Vehicle Controller is obtained and worked as according to the target economic speed of current time current location and the difference of actual vehicle speed The demand torque of preceding moment current location vehicle, and obtain the demand torque sequence at each moment predicted time yardstick Nei；
5) Vehicle Controller torque sequence according to demand, to accumulate the minimum mesh of equivalent fuel consumption in predicted time yardstick Mark, the torque of distribution engine and motor, real-time tracking target economic speed, and carry out rolling optimization.
Described step 2) in, the optimization of the energy-optimised management strategy of plug-in hybrid-power automobile based on Dynamic Programming Target is：
J=min ∑s BePe+sPbatt/Hfuel
Wherein, BeFor fuel consumption, PeFor engine power, s is the electric conversion coefficient of oil, PbattFor battery electrical power, HfuelFor the low heat value of gasoline.
Obtain equivalent fuel consumption and specifically include following steps：
21) according to Longitudinal Dynamic Model, the driving force F of plug-in hybrid-power automobile demand is calculatedtWith demand torque；
22) demand torque is allocated between engine and motor, obtains torque and the rotating speed of engine and motor；
23) fuel consumption of engine and the electricity of battery are obtained according to the torque of engine and motor and rotation engines Consumption, is changed by oily electricity, obtains the equivalent fuel consumption of plug-in hybrid-power automobile.
Described step 21) in, Longitudinal Dynamic Model is：
Wherein, m is complete vehicle quality, and g is acceleration of gravity, and f is coefficient of rolling resistance, and α is the angle of gradient, A be automobile windward Area, CDFor coefficient of air resistance, u is speed, and δ is vehicle rotary mass conversion coefficient,For pickup.
Described step 4) in, obtained according to the difference e of the target economic speed of current time current location and actual vehicle speed The calculating formula for taking the demand torque of current time current location vehicle is：
Wherein, T is plug-in hybrid-power automobile demand torque, vcycFor target economic speed, vrealFor actual vehicle speed, Kp For PI controller proportionality coefficients, KiFor PI controller integral coefficients, TmaxThe torque capacity that can be provided for current time vehicle.
Described step 4) in, the demand torque sequence at each moment is dynamic by backward longitudinal direction of car in predicted time yardstick Mechanical model is calculated and obtained, i.e.,：
Wherein, T (s) is the demand torque at starting point s, and m is complete vehicle quality, and f is coefficient of rolling resistance, and A is automobile Front face area, CDFor coefficient of air resistance, v (s) is the economic speed at starting point s, and a (s) is the acceleration at starting point s Degree, δ is vehicle equivalent rotary mass conversion coefficient, and r is vehicle wheel roll radius.
Described step 5) in, control is optimized using the model prediction based on stochastic dynamic programming, i.e., in prediction Between in yardstick, according to the vehicle demand torque sequence in time scale, with step 2) in the equivalent fuel consumption of accumulation it is minimum Optimization aim, optimization obtains the torque sequence of engine and motor in predicted time yardstick, take the engine at current time with Motor torque is used as actual controlled quentity controlled variable, during plug-in hybrid-power automobile is run, the mesh in predicted time yardstick Mark economic speed and demand torque sequence and constantly roll renewal, the torque of engine and motor with the rolling of prediction time domain Optimal control is also in rolling optimization.
Compared with prior art, the present invention has advantages below：
First, present invention fusion cloud server, obtains real-time work information using car networking technology, specifically includes road The gradient, radius of curvature, traffic flow velocity, speed limit and intersection, merge real-time work information using global Dynamic Programming most Excellent control strategy decision-making goes out target economic speed, and is transferred to Vehicle Controller so that plug-in hybrid-power automobile is tracked Economic speed, it is ensured that the global optimum of its energy consumption；
2nd, the present invention combines longitudinal vehicle dynamic model at Vehicle Controller end according to the warp obtained from cloud server Ji speed calculates the demand torque obtained in predicted time yardstick.In predicted time yardstick, using based on stochastic dynamic programming Model predictive control method, the torque for optimizing engine and motor distributes real-time online, it is ensured that plug-in hybrid vapour Car energy management it is real-time optimal；
3rd, the present invention can automatic decision go out the vehicle demand torque based on energy economy, and between engine and motor It is allocated, operation of the release driver to accelerator pedal so that vehicle is automatically operated in energy-saving driving pattern, realizes plug-in Formula hybrid vehicle is driven based on the optimal Longitudinal Intelligence of energy consumption, and energy-saving driving is effectively combined with intelligent driving.
Brief description of the drawings
Fig. 1 is the schematic diagram based on the energy-optimised management method of the adaptive plug-in hybrid-power automobile of real-time working condition.
Fig. 2 is single shaft plug-in hybrid-power automobile energy consumption calculation model schematic diagram in parallel.
Fig. 3 is the plug-in hybrid-power automobile economic speed planing method schematic diagram based on Dynamic Programming.
Embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.It should be understood that present embodiment is only For illustrating the present invention rather than limitation the scope of the present invention.In addition, it is to be understood that read the content of the invention lectured it Afterwards, those skilled in the art can be made various changes or modifications to the present invention, and these equivalent form of values are equally fallen into appended by the application Claims limited range.
As shown in Figure 1：This is based on the energy-optimised management method bag of the adaptive plug-in hybrid-power automobile of real-time working condition Include：Real-time working condition information fusion module of the cloud server based on guidance path, plug-in hybrid-power automobile energy consumption prediction mould Block, the plug-in hybrid-power automobile energy consumption Optimum Economic speed planning module based on Dynamic Programming and at Vehicle Controller end Based on the Model Predictive Control module that stochastic dynamic programming, equivalent fuel consumption are minimum.Specifically comprise the steps of：
(1) driver sets the Origin And Destination of stroke by navigation module, and navigation module is by V2I communication transfers to cloud Server is held, interacts, is cooked up from the path of origin-to-destination with electronic map, and obtains real-time working condition information, including The work informations such as the gradient, radius of curvature, traffic flow velocity, speed limit, the intersection of road.If the work information of guidance path on the way As shown in table 1.
The guidance path work information of table 1
(2) server sets up corresponding energy consumption calculation mould for specific plug-in hybrid-power automobile vehicle beyond the clouds Type.Including：Engine mockup, motor model, power accumulator model, clutch model, automatic transmission model and vehicle are vertical To kinetic model.The present invention only focuses on the stable state energy consumption characteristics of plug-in hybrid-power automobile, thus is obtained based on test data The performance data of engine, motor, power accumulator, clutch and automatic transmission is taken, uses and looks into energy consumption forecast model The method of table.For each plug-in hybrid-power automobile vehicle, the dynamical system framework of respective configuration is selected, is imported respective Vehicle and power part parameter, you can set up specific plug-in hybrid-power automobile energy consumption calculation model.Inserted with single shaft parallel connection Exemplified by electric-type hybrid vehicle, its energy consumption calculation model is as shown in Figure 2.
(3) the plug-in hybrid-power automobile energy optimization strategy based on Dynamic Programming global optimum, comprehensive energy consumption is calculated Model, plans the economic speed most saved.Set up the plug-in hybrid-power automobile energy optimization plan based on Dynamic Programming Slightly.Global optimum's energy management strategies are with the minimum optimization aim of accumulation equivalent fuel consumption shown in formula (2).
Constraints is used as using the bound of the speed limit of guidance path each point, traffic flow velocity and electrokinetic cell work electricity. With reference to plug-in hybrid-power automobile energy consumption calculation model, using the algorithm of Dynamic Programming, each section on guidance path of warp is obtained Ji speed, its flow is as shown in Figure 3.It is in parallel with the single shaft shown in the work information and step 2 of the guidance path shown in step (1) Exemplified by plug-in hybrid-power automobile energy consumption calculation model, using global optimum's energy management plan of the Dynamic Programming shown in Fig. 3 Slightly, the economic speed of guidance path is planned, as a result as shown in table 2.
The guidance path economic speed of table 2 is planned
(4) set up at Vehicle Controller end based on the minimum Model Predictive Control of stochastic dynamic programming, equivalent fuel consumption Algorithm, energy consumption optimized control is realized according to driver intention and target economic speed.Vehicle Controller is receiving high in the clouds clothes It is after the target economic speed that business device planning is obtained, the target economic speed at current time and driver or forward direction vehicle is longitudinally dynamic Pilot model in mechanical model is interacted.In operating mode (such as city operating mode that operating mode is relatively complicated, interference is more Deng), target economic speed by driver according to current time of the demand torque of current time vehicle, actual vehicle speed, Yi Jishi The condition of road surface operation on border accelerates to carry out decision-making with brake pedal；And in the operating mode that operating mode is by a relatively simple, interference is less (such as height Fast operating mode etc.), intelligent driving pattern may be selected in driver, by target economic speed of longitudinal drive person's model according to current time The direct decision-making of deviation with actual vehicle speed obtains the demand torque of vehicle, and shown in such as formula (3), the economic car of target is tracked with this Speed.
Wherein：T is plug-in hybrid-power automobile demand torque；vcycFor target economic speed；vrealFor actual vehicle speed；Kp For PI controller proportionality coefficients；KiFor PI controller integral coefficients；TmaxThe torque capacity that can be provided for current time vehicle.
In predicted time yardstick the demand torque at other moment can search obtain the corresponding operating range of future time instance and After economic speed, calculated and obtained by backward longitudinal vehicle dynamic model.
Wherein, T (s) is the demand torque at starting point s, and m is complete vehicle quality, and g is acceleration of gravity, and f hinders to roll Force coefficient, A is front face area of automobile, CDFor coefficient of air resistance, v (s) is the economic speed apart from starting point s at, a (s) for away from From the acceleration at starting point s, δ is vehicle equivalent rotary mass conversion coefficient, and r is vehicle wheel roll radius.
After the demand torque sequence at each moment in predicted time yardstick is obtained, to accumulate equivalent in predicted time yardstick The minimum target of fuel consumption, using stochastic dynamic programming algorithm, obtains current time optimal engine and motor torque point With controlled quentity controlled variable the most.Over time, based on accumulated in predicted time yardstick the minimum engine of equivalent fuel consumption with The torque distribution control of motor is also in rolling optimization.By taking the economic speed shown in step (3) as an example, starting for efficiency optimization is obtained Machine and motor torque distribution are as shown in table 3.
The engine of the efficiency of table 3 optimization is distributed with motor torque

Claims (6)

1. a kind of energy-optimised management method of the adaptive plug-in hybrid-power automobile of real-time working condition, it is characterised in that including Following steps：
1) cloud server obtains all paths from origin-to-destination, every paths is entered according to the Origin And Destination of stroke Row segmentation, and obtain the real-time working condition information of each segmentation per paths, including the gradient of road, radius of curvature, traffic flow velocity, Speed limit and intersection；
2) with the minimum optimization aim of accumulation equivalent fuel consumption of each segmentation of each path, with the limit of each segmentation of every paths The limit value of speed, traffic flow velocity and electrokinetic cell work electricity is set up the plug-in mixing based on Dynamic Programming as constraints and moved Power automobile energy optimum management strategy；
3) plug-in hybrid-power automobile energy optimization strategy is solved using dynamic programming algorithm, obtains each per paths It is individual to be segmented corresponding target economic speed, and the target economic speed is sent to Vehicle Controller；
4) when Vehicle Controller obtains current according to the target economic speed of current time current location and the difference of actual vehicle speed The demand torque of current location vehicle is carved, and obtains the demand torque sequence at each moment predicted time yardstick Nei；
5) Vehicle Controller torque sequence according to demand, to accumulate the minimum target of equivalent fuel consumption in predicted time yardstick, The torque of engine and motor, real-time tracking target economic speed are distributed, and carries out rolling optimization.
2. a kind of adaptive energy-optimised manager of plug-in hybrid-power automobile of real-time working condition according to claim 1 Method, it is characterised in that described step 2) in, the energy-optimised management strategy of plug-in hybrid-power automobile based on Dynamic Programming Optimization aim be：
<mrow> <mi>J</mi> <mo>=</mo> <msub> <mi>min&amp;Sigma;B</mi> <mi>e</mi> </msub> <msub> <mi>P</mi> <mi>e</mi> </msub> <mo>+</mo> <msub> <mi>sP</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> <mi>t</mi> </mrow> </msub> <mo>/</mo> <msub> <mi>H</mi> <mrow> <mi>f</mi> <mi>u</mi> <mi>e</mi> <mi>l</mi> </mrow> </msub> </mrow>
Wherein, BeFor fuel consumption, PeFor engine power, s is the electric conversion coefficient of oil, PbattFor battery electrical power, HfuelFor The low heat value of gasoline.
3. a kind of adaptive energy-optimised manager of plug-in hybrid-power automobile of real-time working condition according to claim 2 Method, it is characterised in that obtain equivalent fuel consumption and specifically include following steps：
21) according to Longitudinal Dynamic Model, the driving force F of plug-in hybrid-power automobile demand is calculatedtWith demand torque；
22) demand torque is allocated between engine and motor, obtains torque and the rotating speed of engine and motor；
23) obtain the fuel consumption of engine according to the torque of engine and motor and rotation engines and the electricity of battery disappears Consumption, is changed by oily electricity, obtains the equivalent fuel consumption of plug-in hybrid-power automobile.
4. a kind of adaptive energy-optimised manager of plug-in hybrid-power automobile of real-time working condition according to claim 3 Method, it is characterised in that described step 21) in, Longitudinal Dynamic Model is：
<mrow> <msub> <mi>F</mi> <mi>t</mi> </msub> <mo>=</mo> <mi>m</mi> <mi>g</mi> <mi>f</mi> <mi> </mi> <mi>c</mi> <mi>o</mi> <mi>s</mi> <mi>&amp;alpha;</mi> <mo>+</mo> <mfrac> <mrow> <msub> <mi>C</mi> <mi>D</mi> </msub> <mi>A</mi> </mrow> <mn>21.15</mn> </mfrac> <msup> <mi>u</mi> <mn>2</mn> </msup> <mo>+</mo> <mi>m</mi> <mi>g</mi> <mi> </mi> <mi>sin</mi> <mi>&amp;alpha;</mi> <mo>+</mo> <mi>&amp;delta;</mi> <mi>m</mi> <mover> <mi>u</mi> <mo>&amp;CenterDot;</mo> </mover> </mrow>
Wherein, m is complete vehicle quality, and g is acceleration of gravity, and f is coefficient of rolling resistance, and α is the angle of gradient, and A is front face area of automobile, CDFor coefficient of air resistance, u is speed, and δ is vehicle rotary mass conversion coefficient,For pickup.
5. a kind of adaptive energy-optimised manager of plug-in hybrid-power automobile of real-time working condition according to claim 1 Method, it is characterised in that described step 4) in, according to the target economic speed and the difference of actual vehicle speed of current time current location The calculating formula of demand torque that value e obtains current time current location vehicle is：
<mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mi>T</mi> <mo>=</mo> <mo>(</mo> <msub> <mi>K</mi> <mi>p</mi> </msub> <mi>e</mi> <mo>+</mo> <msub> <mi>K</mi> <mi>i</mi> </msub> <mstyle> <mrow> <mo>&amp;Integral;</mo> <mrow> <mi>e</mi> <mi>d</mi> <mi>t</mi> </mrow> </mrow> </mstyle> <mo>)</mo> <mo>*</mo> <msub> <mi>T</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mtd> </mtr> <mtr> <mtd> <mi>e</mi> <mo>=</mo> <msub> <mi>v</mi> <mrow> <mi>c</mi> <mi>y</mi> <mi>c</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>v</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>a</mi> <mi>l</mi> </mrow> </msub> </mtd> </mtr> </mtable> </mfenced>
Wherein, T is plug-in hybrid-power automobile demand torque, vcycFor target economic speed, vrealFor actual vehicle speed, KpFor PI Controller proportionality coefficient, KiFor PI controller integral coefficients, TmaxThe torque capacity that can be provided for current time vehicle.
6. a kind of adaptive energy-optimised manager of plug-in hybrid-power automobile of real-time working condition according to claim 1 Method, it is characterised in that described step 4) in, the demand torque sequence at each moment passes through backward vehicle in predicted time yardstick Longitudinal Dynamic Model is calculated and obtained, i.e.,：
<mrow> <mi>T</mi> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> <mo>=</mo> <mrow> <mo>(</mo> <mi>m</mi> <mi>g</mi> <mi>f</mi> <mo>+</mo> <mfrac> <mrow> <msub> <mi>C</mi> <mi>D</mi> </msub> <mi>A</mi> </mrow> <mn>21.15</mn> </mfrac> <mi>v</mi> <msup> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <mi>&amp;delta;</mi> <mi>m</mi> <mi>a</mi> <mo>(</mo> <mi>s</mi> <mo>)</mo> <mo>)</mo> </mrow> <mo>*</mo> <mi>r</mi> </mrow>
Wherein, T (s) is demand torque apart from starting point s at, and m is complete vehicle quality, and f is coefficient of rolling resistance, A for automobile windward Area, CDFor coefficient of air resistance, v (s) is the economic speed at starting point s, and a (s) is the acceleration at starting point s, δ For vehicle equivalent rotary mass conversion coefficient, r is vehicle wheel roll radius.
CN201710422212.2A 2017-06-07 2017-06-07 The energy-optimised management method of the adaptive plug-in hybrid-power automobile of real-time working condition Active CN107284441B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201710422212.2A CN107284441B (en) 2017-06-07 2017-06-07 The energy-optimised management method of the adaptive plug-in hybrid-power automobile of real-time working condition

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201710422212.2A CN107284441B (en) 2017-06-07 2017-06-07 The energy-optimised management method of the adaptive plug-in hybrid-power automobile of real-time working condition

Publications (2)

Publication Number Publication Date
CN107284441A true CN107284441A (en) 2017-10-24
CN107284441B CN107284441B (en) 2019-07-05

Family

ID=60096042

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201710422212.2A Active CN107284441B (en) 2017-06-07 2017-06-07 The energy-optimised management method of the adaptive plug-in hybrid-power automobile of real-time working condition

Country Status (1)

Country Link
CN (1) CN107284441B (en)

Cited By (34)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108106626A (en) * 2017-12-18 2018-06-01 浙江工业大学 A kind of electric vehicle trip route planing method based on driving cycle
CN108528436A (en) * 2018-01-18 2018-09-14 合肥工业大学 A kind of ECMS multiple target dual blank-holders of ectonexine nesting
CN108803333A (en) * 2018-06-20 2018-11-13 福州大学 A kind of consideration energy consumption and the electric vehicle driving strategy of time weigh optimization method
CN108830422A (en) * 2018-06-21 2018-11-16 中国联合网络通信集团有限公司 Optimization method, the apparatus and system of intelligent driving
CN108909702A (en) * 2018-08-23 2018-11-30 北京理工大学 A kind of plug-in hybrid-power automobile energy management method and system
CN108944905A (en) * 2018-05-31 2018-12-07 福州大学 A kind of PHEV traffic light intersection based on Model Predictive Control passes through control method
CN109733378A (en) * 2018-12-19 2019-05-10 江苏大学 Optimize the torque distribution method predicted on line under a kind of line
CN109747625A (en) * 2018-12-11 2019-05-14 同济大学 A kind of hybrid vehicle combined type energy management method
CN109760669A (en) * 2019-01-17 2019-05-17 浙江工业大学 A kind of real-time optimization energy management method of plug-in hybrid-power automobile
CN109808518A (en) * 2018-12-29 2019-05-28 清华大学 Fuel cell car energy management method and system based on geographical location information
CN110135632A (en) * 2019-04-29 2019-08-16 吉林大学 PHEV adaptive optimal energy management method based on routing information
CN110155057A (en) * 2019-05-24 2019-08-23 同济大学 Vehicle energy management system and management method
CN110194177A (en) * 2019-06-28 2019-09-03 重庆大学 A kind of plug-in hybrid passenger car energy management method based on the adaptive equivalent minimum strategy of consumption
CN110341690A (en) * 2019-07-22 2019-10-18 北京理工大学 A kind of PHEV energy management method based on deterministic policy Gradient learning
CN110356398A (en) * 2019-07-24 2019-10-22 重庆长安汽车股份有限公司 A kind of speed method of adjustment, device, controller and automobile
CN110667565A (en) * 2019-09-25 2020-01-10 重庆大学 Intelligent network connection plug-in hybrid electric vehicle collaborative optimization energy management method
CN110717218A (en) * 2019-09-18 2020-01-21 北京理工大学 Electric drive vehicle distributed power drive system reconstruction control method and vehicle
CN111002975A (en) * 2019-12-27 2020-04-14 延锋汽车饰件系统有限公司 Vehicle energy management method, system, electronic device, and storage medium
CN111267829A (en) * 2020-03-11 2020-06-12 宁波工程学院 Hybrid power vehicle energy management method and system
CN111267830A (en) * 2020-02-10 2020-06-12 南京航空航天大学 Hybrid power bus energy management method, device and storage medium
CN111301398A (en) * 2020-02-28 2020-06-19 武汉理工大学 CVT plug-in hybrid electric vehicle energy management method and system
WO2020125470A1 (en) * 2018-12-20 2020-06-25 Ningbo Geely Automobile Research & Development Co., Ltd. A system and method for controlling energy consumption in a vehicle
CN111532264A (en) * 2020-04-17 2020-08-14 东南大学 Intelligent internet automobile cruising speed optimization method for variable-gradient and variable-speed-limit traffic scene
CN111605541A (en) * 2020-04-23 2020-09-01 同济大学 Power distribution system engine starting mu comprehensive robust control method and device
CN111634280A (en) * 2020-06-11 2020-09-08 清华大学 Energy-saving cruise driving application-oriented cloud control platform and prediction cruise control system
CN111775925A (en) * 2020-06-09 2020-10-16 同济大学 Working mode decision method and device for power split hybrid electric vehicle
CN111942394A (en) * 2020-08-25 2020-11-17 潍柴动力股份有限公司 Speed information pushing method, device and system for electric vehicle
CN112622874A (en) * 2021-01-04 2021-04-09 中车株洲电力机车有限公司 Capacity matching method and system for power system of hybrid power vehicle
CN112937547A (en) * 2021-01-28 2021-06-11 北京理工大学 Plug-in hybrid power bus energy management method based on global working conditions
CN113561959A (en) * 2020-04-28 2021-10-29 郑州宇通客车股份有限公司 Hybrid vehicle parameter calibration method and device
CN113682292A (en) * 2021-03-11 2021-11-23 中国科学院广州能源研究所 Real-time prediction energy management method and device for CVT parallel hybrid electric vehicle
AT524003A1 (en) * 2020-07-10 2022-01-15 Avl List Gmbh METHOD FOR PREDICTIVE CONTROL OF A DRIVE SYSTEM
CN114254811A (en) * 2021-12-01 2022-03-29 东南大学 Plug-in hybrid electric vehicle routing method and device, vehicle and storage medium
CN116373843A (en) * 2023-06-05 2023-07-04 国网江西省电力有限公司电力科学研究院 Power distribution method for oil-electricity series-parallel power pole setting operation vehicle

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101602364A (en) * 2008-12-31 2009-12-16 宾洋 Be applied to the quick DP control method of PHEV
KR101260030B1 (en) * 2011-03-04 2013-05-06 한양대학교 산학협력단 Power distribution method for series hybrid electric vehicle
CN104590247A (en) * 2014-12-09 2015-05-06 河南理工大学 Hybrid electric vehicle energy conservation predictive control method based on traffic signal lamp information
CN105539423A (en) * 2015-12-25 2016-05-04 江苏大学 Hybrid vehicle torque distribution control method and system for protecting battery based on environment temperature

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101602364A (en) * 2008-12-31 2009-12-16 宾洋 Be applied to the quick DP control method of PHEV
KR101260030B1 (en) * 2011-03-04 2013-05-06 한양대학교 산학협력단 Power distribution method for series hybrid electric vehicle
CN104590247A (en) * 2014-12-09 2015-05-06 河南理工大学 Hybrid electric vehicle energy conservation predictive control method based on traffic signal lamp information
CN105539423A (en) * 2015-12-25 2016-05-04 江苏大学 Hybrid vehicle torque distribution control method and system for protecting battery based on environment temperature

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
欧阳等: "自适应路况的插电式混合动力汽车能量管理策略", 《中国公路学报》 *

Cited By (49)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108106626A (en) * 2017-12-18 2018-06-01 浙江工业大学 A kind of electric vehicle trip route planing method based on driving cycle
CN108528436A (en) * 2018-01-18 2018-09-14 合肥工业大学 A kind of ECMS multiple target dual blank-holders of ectonexine nesting
CN108528436B (en) * 2018-01-18 2020-05-19 合肥工业大学 Inner-outer nested ECMS multi-target double-layer optimization method
CN108944905A (en) * 2018-05-31 2018-12-07 福州大学 A kind of PHEV traffic light intersection based on Model Predictive Control passes through control method
CN108803333A (en) * 2018-06-20 2018-11-13 福州大学 A kind of consideration energy consumption and the electric vehicle driving strategy of time weigh optimization method
CN108830422A (en) * 2018-06-21 2018-11-16 中国联合网络通信集团有限公司 Optimization method, the apparatus and system of intelligent driving
CN108830422B (en) * 2018-06-21 2021-08-17 中国联合网络通信集团有限公司 Intelligent driving optimization method, device and system
CN108909702A (en) * 2018-08-23 2018-11-30 北京理工大学 A kind of plug-in hybrid-power automobile energy management method and system
CN109747625B (en) * 2018-12-11 2021-02-02 同济大学 Hybrid power vehicle combined type energy management method
CN109747625A (en) * 2018-12-11 2019-05-14 同济大学 A kind of hybrid vehicle combined type energy management method
CN109733378B (en) * 2018-12-19 2020-11-20 江苏大学 Offline optimized online predicted torque distribution method
CN109733378A (en) * 2018-12-19 2019-05-10 江苏大学 Optimize the torque distribution method predicted on line under a kind of line
WO2020125470A1 (en) * 2018-12-20 2020-06-25 Ningbo Geely Automobile Research & Development Co., Ltd. A system and method for controlling energy consumption in a vehicle
CN109808518A (en) * 2018-12-29 2019-05-28 清华大学 Fuel cell car energy management method and system based on geographical location information
CN109760669A (en) * 2019-01-17 2019-05-17 浙江工业大学 A kind of real-time optimization energy management method of plug-in hybrid-power automobile
CN110135632A (en) * 2019-04-29 2019-08-16 吉林大学 PHEV adaptive optimal energy management method based on routing information
CN110135632B (en) * 2019-04-29 2022-11-25 吉林大学 PHEV self-adaptive optimal energy management method based on path information
CN110155057A (en) * 2019-05-24 2019-08-23 同济大学 Vehicle energy management system and management method
CN110155057B (en) * 2019-05-24 2021-04-02 同济大学 Vehicle energy management system and management method
CN110194177A (en) * 2019-06-28 2019-09-03 重庆大学 A kind of plug-in hybrid passenger car energy management method based on the adaptive equivalent minimum strategy of consumption
CN110341690A (en) * 2019-07-22 2019-10-18 北京理工大学 A kind of PHEV energy management method based on deterministic policy Gradient learning
CN110341690B (en) * 2019-07-22 2020-08-04 北京理工大学 PHEV energy management method based on deterministic strategy gradient learning
CN110356398A (en) * 2019-07-24 2019-10-22 重庆长安汽车股份有限公司 A kind of speed method of adjustment, device, controller and automobile
CN110717218B (en) * 2019-09-18 2021-08-03 北京理工大学 Electric drive vehicle distributed power drive system reconstruction control method and vehicle
CN110717218A (en) * 2019-09-18 2020-01-21 北京理工大学 Electric drive vehicle distributed power drive system reconstruction control method and vehicle
CN110667565A (en) * 2019-09-25 2020-01-10 重庆大学 Intelligent network connection plug-in hybrid electric vehicle collaborative optimization energy management method
CN110667565B (en) * 2019-09-25 2021-01-19 重庆大学 Intelligent network connection plug-in hybrid electric vehicle collaborative optimization energy management method
CN111002975B (en) * 2019-12-27 2022-02-08 延锋汽车饰件系统有限公司 Vehicle energy management method, system, electronic device, and storage medium
CN111002975A (en) * 2019-12-27 2020-04-14 延锋汽车饰件系统有限公司 Vehicle energy management method, system, electronic device, and storage medium
CN111267830A (en) * 2020-02-10 2020-06-12 南京航空航天大学 Hybrid power bus energy management method, device and storage medium
CN111301398A (en) * 2020-02-28 2020-06-19 武汉理工大学 CVT plug-in hybrid electric vehicle energy management method and system
CN111267829B (en) * 2020-03-11 2021-08-03 宁波工程学院 Hybrid power vehicle energy management method and system
CN111267829A (en) * 2020-03-11 2020-06-12 宁波工程学院 Hybrid power vehicle energy management method and system
CN111532264A (en) * 2020-04-17 2020-08-14 东南大学 Intelligent internet automobile cruising speed optimization method for variable-gradient and variable-speed-limit traffic scene
CN111605541A (en) * 2020-04-23 2020-09-01 同济大学 Power distribution system engine starting mu comprehensive robust control method and device
CN113561959A (en) * 2020-04-28 2021-10-29 郑州宇通客车股份有限公司 Hybrid vehicle parameter calibration method and device
CN111775925A (en) * 2020-06-09 2020-10-16 同济大学 Working mode decision method and device for power split hybrid electric vehicle
CN111634280A (en) * 2020-06-11 2020-09-08 清华大学 Energy-saving cruise driving application-oriented cloud control platform and prediction cruise control system
AT524003A1 (en) * 2020-07-10 2022-01-15 Avl List Gmbh METHOD FOR PREDICTIVE CONTROL OF A DRIVE SYSTEM
AT524003B1 (en) * 2020-07-10 2022-09-15 Avl List Gmbh METHOD FOR PREDICTIVE CONTROL OF A DRIVE SYSTEM
CN111942394A (en) * 2020-08-25 2020-11-17 潍柴动力股份有限公司 Speed information pushing method, device and system for electric vehicle
CN112622874B (en) * 2021-01-04 2022-02-11 中车株洲电力机车有限公司 Capacity matching method and system for power system of hybrid power vehicle
CN112622874A (en) * 2021-01-04 2021-04-09 中车株洲电力机车有限公司 Capacity matching method and system for power system of hybrid power vehicle
CN112937547A (en) * 2021-01-28 2021-06-11 北京理工大学 Plug-in hybrid power bus energy management method based on global working conditions
CN113682292A (en) * 2021-03-11 2021-11-23 中国科学院广州能源研究所 Real-time prediction energy management method and device for CVT parallel hybrid electric vehicle
CN114254811A (en) * 2021-12-01 2022-03-29 东南大学 Plug-in hybrid electric vehicle routing method and device, vehicle and storage medium
CN114254811B (en) * 2021-12-01 2024-05-07 东南大学 Plug-in hybrid electric vehicle routing method and device, vehicle and storage medium
CN116373843A (en) * 2023-06-05 2023-07-04 国网江西省电力有限公司电力科学研究院 Power distribution method for oil-electricity series-parallel power pole setting operation vehicle
CN116373843B (en) * 2023-06-05 2023-09-12 国网江西省电力有限公司电力科学研究院 Power distribution method for oil-electricity series-parallel power pole setting operation vehicle

Also Published As

Publication number Publication date
CN107284441B (en) 2019-07-05

Similar Documents

Publication Publication Date Title
CN107284441B (en) The energy-optimised management method of the adaptive plug-in hybrid-power automobile of real-time working condition
CN107351840B (en) A kind of vehicle energy saving path and economic speed dynamic programming method based on V2I
CN102729987B (en) Hybrid bus energy management method
WO2022142540A1 (en) New energy vehicle coasting control system and method based on intelligent networking information, and new energy vehicle
CN109291925B (en) Energy-saving intelligent network-connection hybrid electric vehicle following control method
CN106476643A (en) A kind of electricity Trajectory Planning System of stroke-increasing electric automobile and control method
Ganji et al. A study on look-ahead control and energy management strategies in hybrid electric vehicles
US20170320481A1 (en) A hybrid vehicle and a method for energy management of a hybrid vehicle
CN111231930B (en) Multi-target energy management method in HEV adaptive cruise based on MPC
CN112721907B (en) Efficient energy management method of network-connected hybrid electric vehicle considering road gradient
CN106080585A (en) A kind of double planet row-type hybrid vehicle nonlinear model predictive control method
CN111532264A (en) Intelligent internet automobile cruising speed optimization method for variable-gradient and variable-speed-limit traffic scene
Kim et al. Parameterized energy-optimal regenerative braking strategy for connected and autonomous electrified vehicles: A real-time dynamic programming approach
CN106080579A (en) A kind of hybrid electric vehicle complete vehicle control method based on suspension vibration energy regenerating
CN105730439A (en) Power distribution method of mechanical-electric transmission tracked vehicle
Li et al. Methods and applications of energy saving control of in-wheel motor drive system in electric vehicles: A comprehensive review
CN115534929A (en) Plug-in hybrid electric vehicle energy management method based on multi-information fusion
CN113276829B (en) Vehicle running energy-saving optimization weight-changing method based on working condition prediction
Teichert et al. Comparison of eco-driving strategies for different traffic-management measures
Din et al. Operation and energy evaluation of diesel and hybrid trains with smart switching controls
Vajedi Real-time optimal control of a plug-in hybrid electric vehicle using trip information
Zhang et al. An optimal vehicle speed planning algorithm for regenerative braking at traffic lights intersections based on reinforcement learning
Hamednia et al. Time optimal and eco-driving mission planning under traffic constraints
CN106347373A (en) Dynamic planning method based on battery SOC (state of charge) prediction
Kim et al. Energy-optimal regenerative braking strategy for connected and autonomous electrified vehicles: A practical design and implementation for real-world commercial PHEVs

Legal Events

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