CN110135632A - PHEV adaptive optimal energy management method based on routing information - Google Patents
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Abstract
The invention discloses a kind of PHEV adaptive optimal energy management method based on routing information, cooks up driving path by onboard navigation system, generates the prediction operating condition of forward path；Trip mileage predicting strategy is established to predict daily user trip mileage；Prediction data and initial SOC by generation are generated based on SOC planning algorithm and refer to SOC；It carries out APMP optimization algorithm: with the minimum global optimization target of oil consumption, introducing collaboration state value, convert several instantaneous optimization problems for having Hamilton operator for Global Optimal Problem；State initial value is cooperateed with using genetic algorithm optimization；Collaboration state initial value is found out in MAP chart using interpolation method, the work information and reference SOC obtained according to onboard navigation system corrects collaboration state initial value in real time；Power distribution is carried out using PMP optimization algorithm, each execution unit controller is passed to by CAN bus, completes the full-vehicle control of PHEV.
Description
Technical field
The present invention relates to a kind of control method of finished of plug-in parallel hybrid electric, more particularly to one kind to be based on road
The plug-in parallel hybrid electric adaptive optimal energy management method of diameter information belongs to new-energy automobile control technology neck
Domain.
Background technique
The increasingly urgent need of exacerbation and energy-saving and emission-reduction of the problems such as with energy crisis, environmental pollution, global warming,
The development of new-energy automobile is receive more and more attention.Plug-in hybrid-power automobile (Plug-in Hybrid
Electric Vehicle, PHEV) hybrid vehicle (Hybrid Electric Vehicle, HEV) is compared with larger appearance
The battery of amount, and electric energy can be obtained from power grid.PHEV has both HEV and pure electric automobile (Battery Electric
Vehicles, BEV) the advantages of, when battery capacity abundance, PHEV be in charge-depleting mode (Charge Depleting,
CD), vehicle is mainly driven by motor, the advantage with low oil consumption, low emission；When battery capacity is lower, PHEV is in electricity
Maintenance mode (Charge Sustaining, CS), engine drive vehicle as main power source, have with orthodox car and HEV
There is identical continual mileage.PHEV configuration includes the diversified forms such as series, parallel and mixed connection.Parallel architecture has structure simple,
The advantages that processing and manufacturing is easy, dynamic property and good economy performance, and its configuration is not related to patent protection, and the PHEV in China is mostly used
Such configuration.But there is mechanical connection in the engine of parallel architecture PHEV and wheel, economy is affected by operating condition.
Plug-in hybrid-power automobile energy management strategies are the critical issues of PHEV design, currently, actual motion
PHEV is mostly used rule-based Threshold Control Method strategy (Rule-based control strategy, RB), this kind strategy meter
Calculation amount is small, and real-time is good, is easy to vehicle control device programming and realizes.But the control thresholding of RB strategy is often fixed one group
Thresholding, adaptability for working condition are poor.PHEV economy is in by battery charge state (State of Charge, SOC), speed, traveling
The many factors such as journey, road gradient, temperature influence, and are especially affected by battery SOC, speed and mileage travelled.When RB strategy
Control threshold value it is fixed when, then can not adapt to the influence of operating condition variation automatically.This may cause battery capacity and " depletes " in advance
(SOC is in minimum allowable value) or battery capacity are at the end of stroke the case where complete use useless.Studies have shown that this two
Kind situation can all be such that PHEV oil consumption in parallel increases, and economy is deteriorated.Further, since control threshold value is constant, in most of works
Under condition, instantaneous and global oil consumption is frequently not optimal, or even in certain low speed congestion operating conditions, PHEV energy consumption in parallel can be more than
Traditional combustion engine automobile.Therefore, traditional Threshold Control Method strategy does not simply fail to adapt to the variation of operating condition, in global and instantaneous nothing
It is optimal that method reaches oil consumption, this is to cause PHEV energy consumption in parallel high, the one of the major reasons that fuel-economizing potentiality can not play.
Currently, many scholars propose the PHEV energy consumption strategy based on the theory of optimal control, as the dynamic of global optimization is advised
Cost-effective method (Dynamic Programming, DP), the equivalent fuel consumption min algorithm (Equivalent of instantaneous optimization
Consumption Minimum Strategy, ECMS) and Pang Te lia king minimum value-based algorithm (Pontryagin ' s Minimum
Principal, PMP) etc..Under the premise of known to the operating condition, global optimization approach DP can obtain the operating condition by Converse solved
Under theoretical optimal solution, at this point, energy consumption is optimal.But due to be it is Converse solved, the premise of DP algorithm be operating condition it is known that
And calculation amount is huge, this obviously not can be used directly in the energy management of PHEV actual vehicle.ECMS algorithm belongs to instantaneously
Optimal control algorithm, can be realized instantaneous optimal, but still not be global optimum in operating condition variation, also, ECMS is calculated
Method is mainly used in hybrid vehicle, and constraint condition requires SOC to maintain to balance.Therefore, it is difficult to be applied directly to PHEV
Control in.PMP algorithm also belongs to instantaneous self correlation, as ECMS, is also not global optimum in operating condition variation.But
It is to cooperate with state variable by introducing, PMP algorithm can dynamically distribute engine and power of motor, realize to SOC consumption rate
On-line Control.Therefore, PMP can not require to maintain the balance of SOC, this is very suitable to the energy management of PHEV.
It can be with the critical issue to be solved in PHEV energy management is from the above analysis: under any operating condition, identical
Under electric quantity consumption, the overall situation and instantaneous self correlation of PHEV energy consumption are realized, keep energy consumption minimum.Currently, onboard navigation system (including
Intelligent transportation system, electronic map, GPS etc.), the service such as navigation, road condition query and prediction can be not only provided, can also be provided
The floor data of vehicle.PHEV control system can obtain mileage travelled, congestion (speed point from onboard navigation system
Cloth) and the history trip information such as mileage.
Summary of the invention
The present invention provides a kind of plug-in parallel hybrid electric adaptive optimal energy management based on routing information
Method, this method obtains Future Path information based on instantaneous optimal PMP algorithm, by onboard navigation system, so that PMP is controlled
The Harmonious Matrix state value of strategy can carry out on-line tuning according to Future Path information, driving cycle and battery SOC, realize
Energy consumption of the parallel connection PHEV under different type operating condition improves, sufficiently comprehensively in the instantaneous and global optimum of PHEV energy management in parallel
Play the energy-saving potential of parallel connection PHEV.
The purpose of the present invention is what is be achieved through the following technical solutions:
A kind of PHEV adaptive optimal energy management method based on routing information, comprising the following steps:
Step 1: driving cycle and mileage are predicted:
1.1) vehicle position information is obtained by onboard navigation system and cooks up driving path in electronic map, simultaneously
The real-time road condition information for obtaining the planning driving path generates the prediction operating condition of forward path；
1.2) onboard navigation system obtains the history running data of vehicle, establishes trip mileage predicting strategy to daily user
Trip mileage is predicted, and draws vehicle cumulative mean mileage travelled curve；
SOC is referred to Step 2: generating based on SOC planning algorithm:
By step 1 generate prediction data, including prediction operating condition, whole day traveling total kilometrage, this trip mileage and
Initial SOC is generated based on SOC planning algorithm and is referred to SOC；
Step 3: APMP optimization algorithm:
3.1) with the minimum global optimization target of oil consumption, collaboration state value is introduced, is converted Global Optimal Problem to several
A instantaneous optimization problem with Hamilton operator；
3.2) collaboration state initial value optimization: using genetic algorithm optimization cooperate with state initial value, establish collaboration state initial value with
The MAP chart of SOC initial value and trip mileage variation；
3.3) state value on-line amending is cooperateed with: under actual condition, at the beginning of finding out collaboration state in MAP chart using interpolation method
Value, the work information obtained according to onboard navigation system and reference SOC correct collaboration state initial value in real time；
3.4) Hamiltonian is solved, carries out power distribution using PMP optimization algorithm, is passed to by CAN bus
Each execution unit controller, completes the full-vehicle control of PHEV.
A kind of plug-in hybrid-power automobile adaptive optimal energy management method based on path, step 1.2)
Detailed process are as follows: the PHEV Energy Management System speed-time mileage daily to user records, then to speed into
Row integral obtains mileage travelled, by the hour statistics same day mileage travelled, and trip mileage property data base, working day and section is written
The mileage travelled of holiday need to count respectively；The average travel of statistical work day and festivals or holidays per period respectively draws work
Day and festivals or holidays day part cumulative mean mileage travelled curve.
A kind of PHEV adaptive optimal energy management method based on routing information, the detailed process of step 2
Are as follows:
Firstly generate with reference to SOC: using mileage travelled as abscissa, SOC is ordinate, connection (0, SOCini) and (St,
SOCmin) two o'clock, obtain SOCref；
Wherein, SOCrefFor with reference to SOC；SOCiniFor the initial SOC of stroke；SOCminFor CD mode minimum SOC；StFor whole day
Total trip mileage, StInterpolation in the vehicle cumulative mean mileage travelled curve obtained according to the current travel time by the step 1
It obtains；
System can all carry out SOC planning before travel every time, for single trip, at the end of this trip
SOCendCalculation formula is as follows:
Wherein, SiFor this mileage of going on a journey.
A kind of plug-in hybrid-power automobile adaptive optimal energy management method based on path, step 3.1)
With the minimum global optimization target of oil consumption, collaboration state value is introduced, converts several with Chinese Mill for Global Optimal Problem
The detailed process of the instantaneous optimization problem of operator are as follows:
With the minimum Global Optimal Problem of oil consumption, objective function are as follows:
Wherein: x (t) is SOC of the automobile in t moment, the i.e. state variable of controlled system；U (t) is the torque point of t moment
Proportion, i.e. system control variables, it is motor torque TmWith aggregate demand torque TdmdThe ratio between；Automobile is represented to exist
The transient fuel wear rate of t moment, unit: kg/s；
The constraint of the optimization problem includes:
Wherein, f (x (t), u (t), t) is change rate of the automobile in t moment state variable x (t), unit: 1/s；
Wherein, first formula indicates the state transition equation of the system, and second formula indicates the end of the optimization problem
It is worth constraint condition, i.e., at the end of the process, the SOC of vehicle cannot be below xmin；
Using Pang Te lia king extremum principle by introduce collaboration quantity of state formula (2) Global Optimal Problem is converted into it is several
It is a about Hamiltonian instantaneous optimization problem: PMP optimization algorithm defines Hamiltonian H (x (t), u (t), λ (t), t)
As the optimization aim of instantaneous optimization problem, it is shown below:
Wherein, λ (t) is collaboration quantity of state；First item is the instantaneous oil consumption of engine in Hamiltonian, according to starting
Machine revolving speed and torque check in Engine Universal Characteristics MAP chart；Section 2 is collaboration state value multiplied by SOC transient change, association
It is the new state amount that PMP optimization algorithm introduces with quantity of state, has correlation with driving cycle, in different driving events, association
It is different with the initial value of quantity of state；The minimum value for solving the Hamiltonian at each moment, the optimum control variable sequence obtained
Column are optimum results, are shown below:
It cooperates with state transition equation are as follows:
A kind of PHEV adaptive optimal energy management method based on routing information, at the beginning of step 3.2) collaboration state
Value optimization includes following procedure:
Collaboration state initial value λ is solved using genetic algorithm0,
Genetic algorithm is with the minimum optimization aim of oil consumption, to cooperate with state initial value λ0For individual, individual fitness function
Are as follows:
The corresponding collaboration state value of fitness function minimum value is optimum results, and draws collaboration state value MAP chart；?
Before stroke starts, system passes through the total kilometrage of current SOC initial value and the following stroke, true using collaboration state value MAP chart interpolation
Determine system mode initial value λ0。
A kind of PHEV adaptive optimal energy management method based on routing information, step 3.3) cooperate with state value
On-line amending includes following procedure:
According to the reference SOC that the step 2 obtains, and SOC penalty factor and speed penalty factor are introduced, makes practical SOC
It can follow in real time with reference to SOC, revised collaboration state value is calculated by following formula:
λ (t)=λ0+s(ΔSOC,t)+s(ΔV,t)
λ (t) is influenced by two factors of SOC difference DELTA SOC and speed difference DELTA V, Δ SOC=SOC-SOCref, Δ V=V-
Vm；
The penalty factor s (Δ SOC, t) of SOC difference minimalization when deviateing smaller with reference to SOC refers to SOC deviateing
When excessive, value should quickly increase；
In Δ SOC > 0, penalty factor s value is taken just；In Δ SOC < 0, penalty factor s value takes negative；Penalty factor s (Δ
SOC, t) expression formula is as follows:
The penalty factor s (Δ V, t) of speed difference minimalization when deviation average speed is smaller, is deviateing average speed
When excessive, value should quickly increase；
As Δ V > 0, penalty factor s value takes negative；As Δ V < 0, penalty factor s value is taken just；Penalty factor s (Δ V, t)
Expression formula is as follows:
A kind of PHEV adaptive optimal energy management method based on routing information, step 3.4) is to Hamilton
It includes following procedure that function, which solves:
Solve Hamiltonian using numerical solution: first computational dynamics equation seeks aggregate demand torque, further according to this
The trip mileage and initial SOC of secondary trip determine collaboration state initial value with interpolation method, determine SOC difference and vehicle by vehicle-state
Speed difference value corrects current collaboration state value；Hamiltonian is established, the feasible zone of torque distribution ratio u (t) is divided into several
Part；If u (t) > 0, represents motor and engine and drive vehicle or motor that vehicle is operated alone jointly；If u (t) < 0 represents electricity
Machine is in Generator Status；All Hamiltonian values, find out corresponding Hamiltonian after evaluation grid dividing
The smallest torque distribution ratio is required.
The invention has the following advantages:
1) onboard navigation system is introduced into PHEV energy management by the present invention, by onboard navigation system to road conditions feature
Predict and count the history trip information of vehicle, the SOC planing method (referring to SOC) of proposition solves tradition
The problem of PMP algorithm can not adapt to operating condition variation, and oil consumption is not global optimum.
2) a kind of adaptive PMP control strategy of operating condition based on SOC feedback is proposed.To realize real vehicle On-line Control, utilize
Offline MAP chart solves collaboration state initial value.According to work information and with reference to SOC amendment collaboration state value, PMP optimization algorithm is utilized
Reasonable distribution uses electricity, makes PHEV oil consumption can be close to theoretical optimal level under any operating condition.
Detailed description of the invention
A specific embodiment of the invention will be described in detail below by connected applications example.
Fig. 1 is PHEV transmission in parallel and control system hardware structure diagram；
Fig. 2 is the PHEV adaptive Optimal Control policy framework based on routing information；
Fig. 3 is Baidu's intelligent transportation system path planning and traffic information example；
Fig. 4 is the average speed figure converted according to Baidu map real-time traffic system information；
Fig. 5 (a) is working day cumulative mean mileage travelled curve；
Fig. 5 (b) is festivals or holidays cumulative mean mileage travelled curve；
Fig. 6 is with reference to SOC algorithm principle figure；
Fig. 7 is WLTC working condition measurement circulation；
Fig. 8 is collaboration state initial value MAP chart；
Fig. 9 is practical SOC and reference SOC decline curve
Figure 10 is SOC penalty factor curve；
Figure 11 is average speed penalty factor curve；
Figure 12 is the solution flow chart of Hamiltonian.
Specific embodiment
Invention is described further with reference to the accompanying drawing.It is further that following instance will be helpful to those skilled in the art
Understand the present invention, but the invention is not limited in any way.
Application of the invention is parallel architecture PHEV, and Fig. 1 is the hardware knot of its dynamical system and Energy Management System
Structure.PHEV in this example uses coaxial parallel-connection structure.Wherein, motor coaxle is mounted on the input shaft of automatic transmission, battery
It can be charged by external charger.PHEV whole-control system includes: gas pedal (sensor containing pedal opening), brake pedal
(sensor containing pedal opening), entire car controller (HCU), GPS positioning module, remote communication module, engine controller
(ECU), electric machine controller (MCU), automatic transmission controller (TCU), battery management unit (BMU) pass through between each component
CAN bus interactive information.Entire car controller (HCU) obtains current vehicle position by GPS module, and passes through remote communication module
It carries out telecommunication with intelligent transportation system (ITS) with to acquisite approachs information.ITS system include traffic related information service,
Multiple subsystems such as information service and navigation Service are managed, after ITS obtains vehicle position information and navigation destination, pass through navigation
System plans driving path, by the work information in the path, such as path total kilometrage, each non-intersection speed feature, road surface slope
Degree etc. passes to entire car controller (HCU) by remote information module.Meanwhile the memory in subject vehicle HCU can also
The trip data of storage a period of time vehicle user, such as speed, running time, the information such as vehicle location.
The invention mainly comprises " operating condition and mileage prediction algorithm ", " referring to SOC generating algorithm " and " adaptive PMP
(APM) control algolithm " three parts." operating condition and mileage prediction algorithm " is obtained in forward path traveling by onboard navigation system
The work informations such as journey, road grade, traffic light signal and speed distribution, generate the prediction operating condition of forward path；By vehicle-mounted
Navigation system obtains history floor data, generates history trip mileage prediction curve.Pass through prediction operating condition with reference to SOC generating algorithm
And prediction trip mileage, it is generated based on global optimum's principle and refers to SOC (SOCref)." APMP optimization algorithm " is with oil consumption minimum
For global optimization target, collaboration state value is introduced, it is instantaneous excellent with Hamilton operator to convert several for global issue
Change problem.Using the method for offline optimization, collaboration state initial value is established with the MAP chart of SOC initial value and trip mileage variation.?
Under actual condition, collaboration state initial value is found out in MAP chart using interpolation method, is believed according to onboard navigation system operating condition obtained
Breath and reference SOC correct collaboration state initial value in real time.Power distribution is carried out using PMP optimization algorithm, keeps vehicle oily
The close theoretical optimal level of consumption.
Embodiment
Fig. 2 is the PHEV adaptive Optimal Control policy framework based on routing information, in conjunction with Fig. 1, to proposed by the present invention
Energy management method specific implementation process is described below:
Step 1: after vehicle launch, system carries out self-test and initializes.If driver inputs out in onboard navigation system
Capable destination then proposes that control strategy (method) is managed PHEV energy using the present invention.
Step 2: after driver inputs trip purpose ground in onboard navigation system, the GPS in onboard navigation system can be obtained
It takes vehicle position information and cooks up driving path in electronic map.The reality of the planning path is obtained in ITS system simultaneously
When traffic, by electronic map distance measurement function carry out it is smooth, jogging, congestion distance measurement, to calculate gathering around for road conditions
Stifled ratio.This trip mileage and average speed letter needed for adaptive strategy can be respectively obtained as distance measurement function and congestion ratio
Breath.Fig. 3 is Baidu's intelligent transportation system path planning and Real-time Traffic Information example.The friendship in the section is represented using 4 kinds of colors
Logical situation: dark red to represent heavy congestion (average speed is lower than 15km/h)；Red represent it is crowded (average speed 15km/h~
25km/h)；Yellow represents jogging (average speed 25km/h~40km/h)；Green represent it is smooth (average speed 40km/h with
On).Vehicle speed information is on the traffic flow sensor or taxi (mobile cart) in traffic surveillance and control system in the system
GPS.Assuming that the path of driver's traveling as shown in figure 3, also show the real-time road condition information on the path simultaneously.From system
It is 8.2 kilometers that this section of path total kilometres, which can also be obtained, it is contemplated that running time 20min.Being averaged for this section of path is calculated
Speed of operation is 24.6km/h, colouring information is converted into prediction average speed information, as shown in Figure 4.Wherein dark red representative
5km/h；Red represents 20km/h；Yellow represents 30km/h；Green represents 45km/h.
Step 3: trip mileage identification module establishes trip mileage predicting strategy to daily according to vehicle history running data
User's trip mileage is predicted.Since the trip mileage of the private car user of trip rule shows certain convergence, this
Invention statistics and the trip mileage feature of prediction are trip mileage of the single user in not same date and different periods.PHEV energy
The management system driving cycle daily to user (speed-time mileage) data record, then integrate to speed
To mileage travelled, same day mileage travelled is counted by the hour, and trip mileage property data base is written.The row on working day and festivals or holidays
Sailing mileage need to count respectively.When statistics number of days is enough, judge whether user's trip characteristics restrain by the condition of convergence.In this example
In, 90 days mileages travelled of statistics then should if mileage travelled is fallen in average value [- 5km ,+5km] range with 90% probability
The trip mileage of user has convergence characteristic.The travelling characteristic convergence on working day and day off need to be counted respectively.If no
Convergence then continues to count, until convergence；If convergence, statistical work day and festivals or holidays per period are averaged respectively
Mileage travelled draws working day and festivals or holidays day part cumulative mean mileage travelled curve.Fig. 5 (a) and Fig. 5 (b) is certain private savings
The accumulative average travel curve at automobile-used family.
Step 4:SOC planning module according to the obtained data of prediction, including whole day traveling total kilometrage, this trip mileage with
And initial SOC, the information such as electrical accessory state are generated based on SOC planning algorithm and refer to SOC curve, specific generating process is such as
Shown in Fig. 6, process is as follows:
It firstly generates with reference to SOC (SOCref), using mileage travelled as abscissa, SOC is ordinate, connection (0, SOCini) and
(St,SOCmin) two o'clock, linear type is obtained with reference to SOCref(Fig. 6 filament).Wherein, SOCiniFor the initial SOC of stroke；SOCminFor CD
Mode minimum SOC；StIt always goes on a journey mileage for whole day.StThe cumulative mean mileage travelled obtained according to the current travel time by step 3
Interpolation obtains in curve.By taking certain user as an example, when stroke starts, whether system is according to the current travel time and be that section is false
Pass through remaining total kilometrage of the accumulative average travel curve prediction vehicle same day day.Such as judge that the same day for working day, then uses
Working day of Fig. 5 (a) adds up average travel curve, if the current travel time is 6:30, then total trip mileage of whole day
StFor 39.5km；The time gone on a journey next time is 16:30, then StFor 19.8km.System can all carry out SOC rule before travel every time
It draws, the SOC for single trip, at the end of this tripendCalculation formula is as follows:
Wherein SiFor this mileage of going on a journey.The reference SOC line example such as thick line institute in circle in Fig. 6 of this trip mileage
Show.
Step 5:APMP optimization module is according to SOC initial value SOCini, go on a journey mileage Si, cooperate with state initial value λ0, SOC difference DELTA
SOC and speed difference DELTA V determines collaboration state value, calculates the control torque of engine and motor using APMP optimization algorithm, opens
Stop state, clutch state etc. simultaneously, each execution unit controller is passed to by CAN bus, completes the full-vehicle control of PHEV.
APMP optimization module principle and optimization process are as follows:
5.1PMP energy management strategies
The purpose of the present invention is improving the fuel economy under different operating conditions of plug-in parallel hybrid electric,
Under specific operation, make plug-in parallel hybrid electric (t whithin a period of time0~tfSecond) fuel consumption minimum is one
Typical Global Optimal Problem is indicated by formula (2), (3):
Wherein: x (t) is SOC of the automobile in t moment, the i.e. state variable of controlled system；U (t) is the torque point of t moment
Proportion, i.e. system control variables, it is motor torque TmWith aggregate demand torque TdmdThe ratio between；Automobile is represented to exist
The transient fuel wear rate (unit: kg/s) of t moment；F (x (t), u (t), t) is automobile in t moment state variable x (t)
Change rate (unit: 1/s).Therefore formula (2) is the objective function of the Global Optimal Problem, indicates automobile in t0~tfSecond is specific
Total oil consumption in operating condition operational process, the objective function of the optimization problem are to make the total oil consumption of the section minimum；Formula (3) includes two
A formula is the constraint of the optimization problem, wherein first formula indicates the state transition equation of the system, second formula
The final value constraint condition for indicating the optimization problem, i.e., at the end of the process, the SOC of vehicle cannot be below xmin。
The present invention is asked formula (2) global optimization by introducing collaboration quantity of state using Pang Te lia king extremum principle (PMP)
Topic is converted into several about Hamiltonian instantaneous optimization problem.PMP optimization algorithm define Hamiltonian H (x (t),
U (t), λ (t), t) optimization aim as instantaneous optimization problem, as shown in formula (4):
Wherein, λ (t) is collaboration quantity of state.First item is the instantaneous oil consumption of engine in Hamiltonian, can be according to hair
Motivation revolving speed and torque check in Engine Universal Characteristics MAP chart；Section 2 is collaboration state value multiplied by SOC transient change,
Cooperateing with quantity of state is the new state amount that PMP optimization algorithm introduces, and has correlation with driving cycle.In different driving events,
Cooperate with the initial value of quantity of state different.The minimum value for solving the Hamiltonian at each moment, the optimum control variable obtained
Sequence is optimum results, as shown in formula (5):
It cooperates with state transition equation are as follows:
Influence of the power battery SOC to motor instant oil consumption is not considered, therefore motor instant oil consumption is to the local derviation of SOC
Number is 0.
Systematic state transfer equation are as follows:
Assuming that the change rate of battery SOC is approximately 0, the variation that formula (7) regular equation can be found that collaboration state value is solved
Amplitude is very small, it is believed that is the constant not changed over time, i.e.,
To sum up, the smallest Global Optimal Problem of oil consumption is converted to and solves Hamiltonian minimum value by PMP optimization algorithm
Instantaneous optimization problem.In feasible zone, the corresponding torque distribution ratio of Hamiltonian minimum value at all moment is solved, i.e.,
For optimum control Variables Sequence.Plug-in hybrid-power automobile PMP energy management simulation model, this example are established according to above-mentioned principle
In, PMP energy management strategies model is built by Matalb/Simulink, and auto model is built using AVL Cruise, in connection
It states two models and carries out associative simulation, obtain PHEV Dynamic Co-Simulation program, the fuel consumption values under certain operating condition can be obtained.
5.2 collaboration state initial value optimizations
The present invention solves the collaboration state initial value λ of formula (9) using genetic algorithm0.Genetic algorithm is minimum excellent with oil consumption
Change target, to cooperate with state initial value λ0For individual, individual fitness function are as follows:
The size of collaboration state value decides the electric distribution ratio of the oil of whole service operating condition, by the initial SOC of power battery and row
Sailing two factors of mileage influences.Select world's light-duty vehicle standard driving cycle (Worldwide harmonized Light-
Duty driving Test Cycle, WLTC) as emulation operating condition, as shown in Figure 7.WLTC operating condition is divided into four-stage: low speed
Section, middling speed section, high regime, ultrahigh speed section.Its average speed has respectively represented branch (Low3), main line from down to height
(Medium3-1), suburbs (High3-1) and high speed (ExtraHigh3) four kinds of typical travel operating conditions.Single WLTC state of cyclic operation
Mileage is 23.2km, and WLTC can be carried out to times journey to obtain different mileages travelled.Setting SOC value enters electricity maintenance when being 0.35
Mode.Respectively initial SOC be 0.9,0.8,07,0.6,0.5,0.4, emulation operating condition be 1 times, 2 times, 3 times, 4 times, 5 times of WLTC
Operating condition solves the collaboration state value amounted under the conditions of 30 kinds.
Below with SOC initial value for 0.9, for driving cycle is 5 times of WLTC, genetic algorithm optimization collaboration state value is introduced
Process.The value range of collaboration state value is first preset, can accelerate to optimize by reducing the value range of variable in genetic algorithm
Speed.This example is solved with the tool box GA of Matlab, and setting range of variables is [- 1, -4], and maximum genetic algebra is 15, GA tool
Case calls PHEV Dynamic Co-Simulation program to calculate fitness function, and the corresponding collaboration state value of fitness function minimum value is
For optimum results, this example is -1.94kg.The collaboration state value solved under 30 kinds of working conditions is as shown in table 1, and draws collaboration shape
State value MAP chart, as shown in Figure 8.Before stroke starts, system is used by the total kilometrage of current SOC initial value and the following stroke
Collaboration state value MAP chart interpolation determines system mode initial value λ0。
1 oil electrical equivalent factor M AP data (unit: -1 × kg) of table
5.3 collaboration state value on-line amending strategies
The collaboration state value solved above by genetic algorithm optimization is the optimal value under operating condition WLTC.And it is actual
Operating condition of going on a journey is complicated and changeable, therefore operating condition ADAPTIVE CONTROL is realized on real vehicle, needs further amendment in real time
Cooperate with state value λ0.The reference SOC that the present invention is obtained according to step 4ref, and SOC penalty factor and speed penalty factor are introduced,
Practical SOC is set to be followed in real time with reference to SOCref, revised collaboration state value calculates by following formula:
λ (t)=λ0+s(ΔSOC,t)+s(ΔV,t) (11)
It is adaptive that APMP optimization algorithm can be realized operating condition, wherein collaboration state value λ (t) plays key effect, λ (t)
The size of value determines oily electric use ratio.When λ (t) value is bigger than normal, control strategy relatively can use fuel oil (engine) more, when
When λ (t) value is less than normal, control strategy relatively can use electricity (motor) more.Therefore, turn of the adjustable engine of λ (t) and motor
Square distribution ratio.λ (t) is by SOC difference (Δ SOC=SOC-SOCref) and speed difference (Δ V=V-Vm) two factors influence.When
Vehicle is in compared with congested link traveling and preceding speed can be lower than the average speed of whole section of stroke, i.e. Δ V=V-VmValue is negative, can be with
λ (t) value is correspondingly reduced, makes system relatively mostly using motor, low regime is conducive to improve fuel economy using motor driven,
Vice versa.Due to the variation of operating condition, practical SOC decline curve will not be followed with reference to SOC, as shown in Figure 9 completely.λ (t) value
It is influenced by SOC difference, when operating range is S1, Δ SOC is negative value, and actual SOC ratio is small with reference to SOC, in order to reach
SOC follows effect, to reduce electricity consumption, can correspondingly improve λ (t) value, makes system relatively mostly using engine, otherwise also
So.
The penalty factor s (Δ SOC, t) of SOC difference minimalization when deviateing smaller with reference to SOC refers to SOC deviateing
When excessive, value should quickly increase.In Δ SOC > 0, in order to accelerate using electricity, penalty factor s value is taken just.In Δ SOC < 0
When, electricity is used in order to slow down, penalty factor s value takes negative.Therefore penalty factor s (Δ SOC, t) expression formula is as follows:
Setting Δ SOC value range is (- 0.1,0.1), and s (Δ SOC, t) penalty factor curve is as shown in Figure 10.
The penalty factor s (Δ V, t) of speed difference should deviate average speed it is smaller when minimalization, it is average deviateing
When speed is excessive, value should quickly increase.As Δ V > 0, in order to slowly use electricity, penalty factor s value takes negative.When Δ V < 0
When, in order to accelerate using electricity, penalty factor s value is taken just.Therefore penalty factor s (Δ V, t) expression formula is as follows:
Setting Δ V value range is (- 10,10), and s (Δ V, t) penalty factor is as shown in figure 11.
The solution of 5.4 Hamiltonians
Extremely complex functional equation is solved since Hamiltonian is one, so the present invention is solved using numerical solution
Hamiltonian.Hamiltonian solution process is as shown in figure 12, and first computational dynamics equation seeks aggregate demand torque, then
Collaboration state initial value is determined with interpolation method according to the trip mileage of this trip and initial SOC, determines that SOC is poor by vehicle-state
Value and speed difference, correct current collaboration state value.Hamiltonian is then set up, by the feasible of torque distribution ratio u (t)
Domain is divided into 100 parts.If u (t) > 0, represents motor and engine and drive vehicle or motor that vehicle is operated alone jointly.If u (t) <
0, it represents motor and is in Generator Status.Solving precision in feasible zone can be guaranteed using Numerical Grid partitioning.It calculates
All Hamiltonian values after division, it is required for finding out the corresponding the smallest torque distribution ratio of Hamiltonian.
Claims (7)
1. a kind of PHEV adaptive optimal energy management method based on routing information, which comprises the following steps:
Step 1: driving cycle and mileage are predicted:
1.1) vehicle position information is obtained by onboard navigation system and cooks up driving path in electronic map, obtained simultaneously
The real-time road condition information of the planning driving path generates the prediction operating condition of forward path；
1.2) onboard navigation system obtains the history running data of vehicle, establishes trip mileage predicting strategy and goes on a journey to daily user
Mileage is predicted, and draws vehicle cumulative mean mileage travelled curve；
SOC is referred to Step 2: generating based on SOC planning algorithm:
The prediction data generated by step 1, including prediction operating condition, whole day traveling total kilometrage, this trip mileage and initial
SOC is generated based on SOC planning algorithm and is referred to SOC；
Step 3: APMP optimization algorithm:
3.1) with the minimum global optimization target of oil consumption, collaboration state value is introduced, converts several bands for Global Optimal Problem
There is the instantaneous optimization problem of Hamilton operator；
3.2) collaboration state initial value optimization: state initial value is cooperateed with using genetic algorithm optimization, establishes collaboration state initial value at the beginning of SOC
The MAP chart of value and trip mileage variation；
3.3) it cooperates with state value on-line amending: under actual condition, finding out collaboration state initial value in MAP chart using interpolation method,
The work information and reference SOC obtained according to onboard navigation system corrects collaboration state initial value in real time；
3.4) Hamiltonian is solved, carries out power distribution using PMP optimization algorithm, is passed to by CAN bus and respectively held
Row Parts Controller completes the full-vehicle control of PHEV.
2. a kind of PHEV adaptive optimal energy management method based on routing information as described in claim 1, feature exist
In the step 1.2) detailed process are as follows: the PHEV Energy Management System speed-time mileage daily to user is remembered
Record, then integrates speed to obtain mileage travelled, by the hour statistics same day mileage travelled, and trip mileage characteristic is written
According to library, the mileage travelled of working day and festivals or holidays need to count respectively；The average row of statistical work day and festivals or holidays per period respectively
Mileage is sailed, working day and festivals or holidays day part cumulative mean mileage travelled curve are drawn.
3. a kind of PHEV adaptive optimal energy management method based on routing information as described in claim 1, feature exist
In the detailed process of the step 2 are as follows:
Firstly generate with reference to SOC: using mileage travelled as abscissa, SOC is ordinate, connection (0, SOCini) and (St,SOCmin) two
Point, obtains SOCref；
Wherein, SOCrefFor with reference to SOC；SOCiniFor the initial SOC of stroke；SOCminFor CD mode minimum SOC；StIt always goes on a journey for whole day
Mileage, StInterpolation obtains in the vehicle cumulative mean mileage travelled curve obtained according to the current travel time by the step 1；
SOC system can all carry out SOC planning before travel every time, for single is gone on a journey, at the end of this tripendMeter
It is as follows to calculate formula:
Wherein, SiFor this mileage of going on a journey.
4. a kind of PHEV adaptive optimal energy management method based on routing information as described in claim 1, feature exist
In the step 3.1) introduces collaboration state value, if converting Global Optimal Problem to the minimum global optimization target of oil consumption
The detailed process of the dry instantaneous optimization problem with Hamilton operator are as follows:
With the minimum Global Optimal Problem of oil consumption, objective function are as follows:
Wherein: x (t) is SOC of the automobile in t moment, the i.e. state variable of controlled system；U (t) is the torque distribution ratio of t moment,
That is system control variables, it is motor torque TmWith aggregate demand torque TdmdThe ratio between；Automobile is represented in t moment
Transient fuel wear rate, unit: kg/s；
The constraint of the optimization problem includes:
Wherein, f (x (t), u (t), t) is change rate of the automobile in t moment state variable x (t), unit: 1/s；
Wherein, first formula indicates the state transition equation of the system, and second formula indicates the final value of the optimization problem about
Beam condition, i.e., at the end of the process, the SOC of vehicle cannot be below xmin；
Formula (2) Global Optimal Problem is converted into several passes by introducing collaboration quantity of state using Pang Te lia king extremum principle
In Hamiltonian instantaneous optimization problem: PMP optimization algorithm defines Hamiltonian H (x (t), u (t), λ (t), t) conduct
The optimization aim of instantaneous optimization problem, is shown below:
Wherein, λ (t) is collaboration quantity of state；First item is the instantaneous oil consumption of engine in Hamiltonian, is turned according to engine
Speed and torque check in Engine Universal Characteristics MAP chart；Section 2 is collaboration state value multiplied by SOC transient change, cooperates with shape
State amount is the new state amount that PMP optimization algorithm introduces, and has correlation with driving cycle, in different driving events, cooperates with shape
The initial value of state amount is different；The minimum value for solving the Hamiltonian at each moment, the optimum control Variables Sequence obtained is i.e.
For optimum results, it is shown below:
It cooperates with state transition equation are as follows:
5. a kind of PHEV adaptive optimal energy management method based on routing information as described in claim 1, feature exist
In step 3.2) the collaboration state initial value optimization includes following procedure:
Collaboration state initial value λ is solved using genetic algorithm0,
Genetic algorithm is with the minimum optimization aim of oil consumption, to cooperate with state initial value λ0For individual, individual fitness function are as follows:
The corresponding collaboration state value of fitness function minimum value is optimum results, and draws collaboration state value MAP chart；In stroke
Before beginning, system determines system using collaboration state value MAP chart interpolation by the total kilometrage of current SOC initial value and the following stroke
System state initial value λ0。
6. a kind of PHEV adaptive optimal energy management method based on routing information as described in claim 1, feature exist
In step 3.3) the collaboration state value on-line amending includes following procedure:
According to the reference SOC that the step 2 obtains, and SOC penalty factor and speed penalty factor are introduced, keeps practical SOC real
When follow with reference to SOC, revised collaboration state value is calculated by following formula:
λ (t)=λ0+s(ΔSOC,t)+s(ΔV,t)
λ (t) is influenced by two factors of SOC difference DELTA SOC and speed difference DELTA V, Δ SOC=SOC-SOCref, Δ V=V-Vm；
The penalty factor s (Δ SOC, t) of SOC difference minimalization when deviateing smaller with reference to SOC, it is excessive with reference to SOC deviateing
When, value should quickly increase；
In Δ SOC > 0, penalty factor s value is taken just；In Δ SOC < 0, penalty factor s value takes negative；Penalty factor s (Δ SOC,
T) expression formula is as follows:
The penalty factor s (Δ V, t) of speed difference minimalization when deviation average speed is smaller, deviateing, average speed is excessive
When, value should quickly increase；
As Δ V > 0, penalty factor s value takes negative；As Δ V < 0, penalty factor s value is taken just；Penalty factor s (Δ V, t) expression
Formula is as follows:
7. a kind of PHEV adaptive optimal energy management method based on routing information as described in claim 1, feature exist
In it includes following procedure that the step 3.4), which solves Hamiltonian:
Solve Hamiltonian using numerical solution: first computational dynamics equation seeks aggregate demand torque, goes out further according to this
Capable trip mileage and initial SOC determine collaboration state initial value with interpolation method, determine that SOC difference and speed are poor by vehicle-state
Value, corrects current collaboration state value；Hamiltonian is established, the feasible zone of torque distribution ratio u (t) is divided into several pieces；
If u (t) > 0, represents motor and engine and drive vehicle or motor that vehicle is operated alone jointly；If u (t) < 0, represents at motor
In Generator Status；It is minimum to find out corresponding Hamiltonian for all Hamiltonian values after evaluation grid dividing
Torque distribution ratio be required.
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