CN110422161A - Energize control method, system and its logistics device of application - Google Patents
Energize control method, system and its logistics device of application Download PDFInfo
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- CN110422161A CN110422161A CN201910572629.6A CN201910572629A CN110422161A CN 110422161 A CN110422161 A CN 110422161A CN 201910572629 A CN201910572629 A CN 201910572629A CN 110422161 A CN110422161 A CN 110422161A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W10/00—Conjoint control of vehicle sub-units of different type or different function
- B60W10/04—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units
- B60W10/06—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units including control of combustion engines
-
- 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
- B60W10/00—Conjoint control of vehicle sub-units of different type or different function
- B60W10/24—Conjoint control of vehicle sub-units of different type or different function including control of energy storage means
- B60W10/26—Conjoint control of vehicle sub-units of different type or different function including control of energy storage means for electrical energy, e.g. batteries or capacitors
-
- 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
-
- 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
- B60W2050/0043—Signal treatments, identification of variables or parameters, parameter estimation or state estimation
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2710/00—Output or target parameters relating to a particular sub-units
- B60W2710/06—Combustion engines, Gas turbines
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2710/00—Output or target parameters relating to a particular sub-units
- B60W2710/24—Energy storage means
- B60W2710/242—Energy storage means for electrical energy
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/08—Feature extraction
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- Chemical & Material Sciences (AREA)
- Combustion & Propulsion (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Automation & Control Theory (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
- Secondary Cells (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
It is a kind of to energize control method, system and its logistics device of application, comprising: acquisition obtains mixed dynamic electricity power data；Obtain quantization change data；Dynamic electricity power data is mixed, according to the quantization change data discrete processes to obtain fuzzy characteristics data；Fuzzy characteristics data described in iterative processing, to obtain for can control data；With described for can control data control power battery and engine.The present invention solve logistics transportation equipment of the existing technology energy-saving effect is poor and the short technical problem of battery life.
Description
Technical field
The present invention relates to a kind of hybrid power control technologies, more particularly to a kind of energy supply control method, system and its answer
Logistics device.
Background technique
Hybrid power transporting equipment have at least two power sources, generally including engine, auxiliary power unit and
Source is battery.Energy required for the operational process of hybrid power transporting equipment, a part is from internal combustion engine, by fuel
In chemical energy be converted to mechanical energy；Another part converts electrical energy into mechanical energy from power battery, by motor.Vehicle
The method of salary distribution, that is, logistics transportation equipment energy management strategies of the energy demand between power source.Due to the energy in different dynamic source
Amount conversion pathway and efficiency are all different, and therefore, using different energy management strategies, will lead to the synthesis of logistics transportation equipment
Energy consumption is not also identical.In order to improve the economy of logistics transportation equipment, there is multiple kinds of energy management strategy.It is in the prior art
Energy management strategies and method for on-line optimization are unable to get globally optimal solution, and the energy conservation that cannot give full play to logistics transportation equipment is latent
Power to be unable to get optimal vehicle economy, and does not consider the influence of power of motor loss, selected engine operation
Point is it cannot be guaranteed that the comprehensive energy consumption of dynamical system is minimum.
In conclusion the energy-saving effect that logistics transportation equipment exists in the prior art is poor and the short technology of battery life is asked
Topic.
Summary of the invention
In view of the above prior art there are the energy-saving effect of logistics transporting equipment is poor and the short technical problem of battery life,
The purpose of the present invention is to provide a kind of energy supply control method, system and its logistics devices of application, solve the prior art and exist
Logistics transportation equipment energy-saving effect is poor and the short technical problem of battery life, a kind of energy supply control method, comprising: acquisition
Obtain mixed dynamic electricity power data；Obtain quantization change data；According to the mixed dynamic electricity power number of quantization change data discrete processes
According to obtain fuzzy characteristics data；Iterative processing fuzzy characteristics data, to obtain for can control data；For can control data
Control power battery and engine.
In one embodiment of the present invention, convert fuzzy characteristics data the step of, comprising: extract mixed dynamic electricity power
Engine battery feature in data；Obtain predetermined analog prediction model；Engine battery is handled with predetermined analog prediction model
Feature, to obtain fuzzy characteristics data.
In one embodiment of the present invention, calculate fuzzy characteristics data the step of, comprising: obtain simulative prediction model in
Fuzzy division data；Dividing engine battery feature according to fuzzy division data is fuzzy subset；At simulative prediction model
Fuzzy subset is managed, to obtain fuzzy characteristics data.
In one embodiment of the present invention, iteration was obtained for the step of can control data, comprising: obtains preset iteration
Scale parameter and stopping criterion for iteration；According to iteration scale parameter iteration fuzzy characteristics data, until meeting stopping criterion for iteration；
Saving present Fuzzy characteristic is for can control data.
In one embodiment of the present invention, a kind of energy supply control system, comprising: inductive pick-up device is obtained to acquire
Take mixed dynamic electricity power data；Data converter, to obtain quantization change data；Data processor, to according to the amount
Change and mix dynamic electricity power data described in change data discrete processes, to obtain fuzzy characteristics data, data processor is adopted with induction
Acquisition means connection, data processor are connect with data converter；Data processing equipment is energized, to obscure spy described in iterative processing
Data are levied, to obtain for can control data, energy supply data processing equipment is connect with data processor；Energy controller, for
Described to control power battery and engine for can control data, energy controller is connect with energy supply data processing equipment.
In one embodiment of the present invention, data processor, comprising: power electricity unit, it is described mixed dynamic to extract
Engine power feature and battery capacity feature in electricity power data；Processing unit, to obtain predetermined analog prediction mould
Type, processing unit are connect with power electricity unit；Characteristic unit, described in being handled with the predetermined analog prediction model
Engine power feature and the battery capacity feature, to obtain the fuzzy characteristics data, characteristic unit and processing are single
Member connection.
In one embodiment of the present invention, characteristic unit, comprising: partitioning component, it is pre- to obtain the simulation
Fuzzy division data in model；Engine power component, to divide the engine function according to the fuzzy division data
Rate feature is engine fuzzy subset, and engine power component is connect with partitioning component；Battery capacity component, to according to
It is electricity fuzzy subset that fuzzy division data, which divide the battery capacity feature, and battery capacity component is connect with partitioning component；It is special
Processing component is levied, to handle the engine fuzzy subset and the electricity fuzzy subset with the simulative prediction model, with
The fuzzy characteristics data are obtained, characteristic processing component is connect with battery capacity component.
In one embodiment of the present invention, data processing equipment is energized, comprising: Policy receipt unit, it is pre- to obtain
If Genetic Strategies data；Extraction unit, to extract iteration scale parameter, scale parameter in the default Genetic Strategies data
And stopping criterion for iteration, extraction unit are connect with policing policy receiving unit；Iteration unit, to according to iterative parameter and ratio
Fuzzy characteristics data described in parameter iteration, until the fuzzy characteristics data meet the stopping criterion for iteration, iteration unit with
Extraction unit connection；Storage unit being controlled, for can control data, controlling storage unit to save present Fuzzy characteristic
It is connect with iteration unit.
In one embodiment of the present invention, a kind of logistics device, comprising: apparatus casing；Power supply device is set to described
Inside apparatus casing；The energy supply control system of logistics device is installed in the power supply device, comprising: inductive pick-up device is used
Mixed dynamic electricity power data is obtained with acquisition；Data converter, to obtain quantization change data；Data processor, to root
According to dynamic electricity power data is mixed described in the quantization change data discrete processes, to obtain fuzzy characteristics data；It energizes at data
Device is managed, to fuzzy characteristics data described in iterative processing, to obtain for can control data；Energy controller, for described
For can control data control power battery and engine.
As described above, it is an object of that present invention to provide a kind of energy supply control method, system and its logistics device of application
Overcome the deficiencies in the prior art, proposes a kind of method of determining engine working point, and the method considers engine working point
Influence to the loss of electric machine, in dynamic programming method, for state variable battery SOC (State of Charge, battery electricity
Amount) for a certain transfer path in two neighboring stage, due to the variable quantity of SOC be it is determining, it is available corresponding
Target battery power.
To sum up, the present invention provides the logistics device of a kind of energy supply control method, system and its application, solves the prior art
The energy-saving effect of existing logistics transportation equipment is poor and the short technical problem of battery life.
Detailed description of the invention
Fig. 1 is shown as a kind of energy supply control method step schematic diagram of the invention.
Step S3 in Fig. 2 Fig. 1 is having been carried out the idiographic flow schematic diagram in example.
Fig. 3 is shown as Fuzzy Inference Model data processing schematic diagram of the invention.
The step S33 that Fig. 4 is shown as in Fig. 2 is having been carried out the idiographic flow schematic diagram in example.
Fig. 5 is shown as motor demand power subordinating degree function schematic diagram of the invention.
Fig. 6 is shown as power battery pack subordinating degree function schematic diagram of the invention.
Fig. 7 is shown as auxiliary power unit power subordinating degree function schematic diagram of the invention.
Fig. 8 is shown as the electronic logistic car power distribution fuzzy reasoning MAP schematic diagram of extended-range of the invention.
Fig. 9 is shown as the idiographic flow schematic diagram of step S4 in one embodiment in Fig. 1.
Figure 10 is shown as the electronic logistic car simulation paths processing data flow diagram of extended-range.
Figure 11 is shown as energy supply control system device connection schematic diagram of the invention.
Figure 12 is shown as the specific unit connection schematic diagram of the data processor 3 in Figure 11 in one embodiment.
Figure 13 is shown as the specific component connection schematic diagram of the characteristic unit 33 in Figure 12 in one embodiment
Figure 14 is shown as the specific unit connection schematic diagram of energy supply data processing equipment 4 in one embodiment in Figure 11.
Figure 15 is shown as the logistics device device connection schematic diagram that the present invention applies.
Component label instructions
1 inductive pick-up device
2 data converters
3 data processors
4 energy supply data processing equipments
5 energy controllers
31 power electricity units
32 processing units
33 characteristic units
41 Policy receipt units
42 extraction units
43 iteration units
44 control storage units
331 partitioning components
332 engine power components
333 battery capacity components
334 characteristic processing components
10 apparatus casings
20 power supply devices
30 energy supply control systems
Step numbers explanation
S1~S5 method and step
S31~S33 method and step
S331~S333 method and step
S41~S43 method and step
Specific embodiment
Embodiments of the present invention are illustrated by particular specific embodiment below, those skilled in the art can be by this explanation
Content disclosed by book is understood other advantages and efficacy of the present invention easily.
Fig. 1 is please referred to Figure 11, it should however be clear that this specification structure depicted in this specification institute accompanying drawings, only to cooperate specification
Revealed content is not intended to limit the invention enforceable restriction item so that those skilled in the art understands and reads
Part, therefore do not have technical essential meaning, the modification of any structure, the change of proportionate relationship or the adjustment of size are not influencing
Under the effect of present invention can be generated and the purpose that can reach, should all still fall in disclosed technology contents can contain
In the range of lid.Meanwhile in this specification it is cited such as " on ", " under ", " left side ", " right side ", " centre " and " one " term,
It is merely convenient to being illustrated for narration, rather than to limit the scope of the invention, relativeness is altered or modified, In
It is changed under technology contents without essence, when being also considered as the enforceable scope of the present invention.
Referring to Fig. 1, a kind of energy supply control method step schematic diagram of the invention is shown as, as shown in Figure 1, a kind of energy supply
Control method, comprising:
S1, acquisition obtain mixed dynamic electricity power data, and in one embodiment, the electronic logistic car of extended-range includes APU
(Auxiliary Power Unit, auxiliary power unit) and two energy sources of power battery, the electronic logistic car vehicle of extended-range
Operational mode can work in electric-only mode as needed, increase journey mode or hybrid mode (HEV).Increase Cheng Mo when working in
When formula, it is the smooth transition vehicle of pure electric automobile that rate of economizing gasoline, which increases infinite approach pure electric automobile with battery capacity,；
S2, quantization change data is obtained, in one embodiment, passes through quantization parameter θ hereSOC、By driving motor
Demand power and ternary lithium dynamical battery SOC pass through blurring operation from practical successive value and obtain discrete value；
S3, electricity power data is moved according to quantization change data discrete processes are mixed, it is real one to obtain fuzzy characteristics data
Apply in example, by fuzzy control strategy by driving motor demand power, ternary lithium dynamical battery SOC (State Of Charge,
Battery capacity), the exact values such as APU demand power by defining quantization parameter be converted to fuzzy language value；
S4, iterative processing fuzzy characteristics data, to obtain in one embodiment, passing through proportionality coefficient for can control dataPractical solution will be obtained after the discrete solution de-fuzzy of output variable APU demand power, in the present embodiment, something lost can be used
Propagation algorithm is to the electronic logistic car fuzzy control energy management strategies parameter optimization of extended-range；
S5, power battery and engine are controlled for can control data, in one embodiment, when battery power consumption to one
When determining degree, engine start, engine provides energy for battery and charges to power battery.When battery capacity abundance,
Engine stops working again, by battery driven motor, provides vehicle driving, fuel cell car FCV, mixing in electric car
Power vehicle HEV and pure electric automobile EV three categories will drive wheels travel with motor, and extended-range electric vehicle is by vehicle
Controller completes operation control strategy.Battery pack can be charged by ground charging pile or onboard charger, and fuel oil can be used in engine
Type or gas type.
Fig. 2 and Fig. 3 are please referred to, the step S3 being shown as in Fig. 1 is having been carried out the idiographic flow schematic diagram in example and obscuring
Inference pattern data processing schematic diagram, as shown in Figures 2 and 3, convert fuzzy characteristics data step S3, comprising:
S31, the mixed engine battery feature moved in electricity power data is extracted, in one embodiment, engine battery is special
Sign can be the electricity remaining value and its output power and its corresponding relationship of such as power battery pack；
S32, acquisition predetermined analog prediction model in one embodiment can be according to such as motor demand power degree of membership letters
Number, power battery pack SOC subordinating degree function and APU output power subordinating degree function simulated engine, auxiliary power unit and dynamic
The fuzzy characteristics of power battery pack；
S33, engine battery feature is handled with predetermined analog prediction model, to obtain fuzzy characteristics data, implemented one
In example, as shown in figure 3, Fuzzy control system structure is dual input, single output, using Mamdani inference method.
Fig. 4, Fig. 5, Fig. 6, Fig. 7 and Fig. 8 are please referred to, the step S33 being shown as in Fig. 2 is having been carried out the detailed process in example
Schematic diagram, motor demand power subordinating degree function schematic diagram, power battery pack subordinating degree function schematic diagram, auxiliary power unit function
Rate subordinating degree function schematic diagram and the electronic logistic car power distribution fuzzy reasoning MAP schematic diagram of extended-range, such as Fig. 4 to Fig. 8 institute
Show, calculate the step S33 of fuzzy characteristics data, comprising:
Fuzzy division data in S331, acquisition simulative prediction model, in one embodiment, as shown in figure 5, can will drive
Motor demand power PmotorIt is divided into such as 9 fuzzy subsets: NB, NS, ZE, PS, PM, PB, PVB, PEB, PVEB；
S332, engine battery feature is divided according to fuzzy division data for fuzzy subset, in one embodiment, such as Fig. 6
And shown in Fig. 7, power battery SOC can be divided into such as 7 fuzzy subsets, be EL, VL, LO, ST, HI, VH, EH.APU is needed
Seek performance number PAPU_outIt is divided into 8 fuzzy subsets, respectively ES, VS, SM, MI, BG, VB, EB, VEB；
S333, with simulative prediction model processing fuzzy subset in one embodiment, can root to obtain fuzzy characteristics data
Fuzzy characteristics data are listed according to fuzzy subset's such as subordinating degree function division data to be as follows:
.In one embodiment, the dynamical system of extended-range vehicle can be by electrokinetic cell system, power-driven system, vehicle
Control system and accessory power system (APU) composition, in one embodiment, by fuzzy control strategy by driving motor demand function
The exact values such as rate, ternary lithium dynamical battery SOC (State Of Charge, battery capacity), APU demand power pass through defined amount
Change coefficient and be converted to fuzzy language value, in the present embodiment, in battery capacity abundance, power battery driving motor is provided whole
Vehicle driving power demand, engine is not involved in work at this time.
Fig. 9 and Figure 10 are please referred to, the idiographic flow schematic diagram of step S4 in one embodiment and increasing journey being shown as in Fig. 1
The electronic logistic car simulation paths of formula handle data flow diagram, and as shown in Figure 9 and Figure 10, iteration obtains the step for can control data
Rapid S4, comprising:
S41, preset iteration scale parameter and stopping criterion for iteration are obtained, in one embodiment, with such as vehicle power
Model, wheel model, legacy system model, control strategy model treatment input data such as target vehicle speed, obtain motor model
With the input data of engine mockup, obtained with such as motor model, engine mockup and the processing of battery model and foregoing model
Take actual vehicle speed；
S42, according to iteration scale parameter iteration fuzzy characteristics data, until meeting stopping criterion for iteration, in an embodiment
In, by using genetic algorithm to power distribution fuzzy control energy pipe between the electronic logistic car power battery of extended-range and APU
Reason strategy is optimized to reduce comprehensive oil consumption and promote the power battery service life, in the present embodiment, settable genetic manipulation
Maximum number of iterations is such as 40 generations, and initial population quantity such as 40, optimized variable number is such as 3, right using binary coding
Subordinating degree function and fuzzy control rule coding, single variable code length are such as 20；The total length of individual is such as 60, if
Setting generation gap is such as 0.7.It digitizes the fuzzy data in aforementioned table to obtain following table.To the volume of the fuzzy control rule in following table
Optimized variable parameter is converted into after code:
, in the present embodiment, to solve optimal solution, GAs Toolbox such as GATBX algorithm routine and base can be used
It is integrated for example, by adv_no_gui order in the vehicle simulation model that ADVISOR software is established, realize data interaction.Target letter
Number is ObjV (n)=weight (1) * (200-max_velocity (n))/(200-90)+weight (2) * (235.2/ (oil
(n)+1.85) -12)/(16-12)+weight (3) * (char_coeff (n)-(- 1.1))/((- 0.9)-(- 1.1)), wherein
Weight (i) is respectively logistic car power index max. speed, the comprehensive oil consumption of economic index and power battery charging coefficient
Weight coefficient value.
S43, present Fuzzy characteristic is saved as that in one embodiment, can take based on Simulink for can control data
Build the electronic logistics vehicle model of extended-range, and with Advisor associative simulation, in the present embodiment, using a crossover operation, according to change
Different probability carries out mutation operation, obtains next-generation group.Next-generation group repeats the above process, until evolutionary generation terminates.
Figure 11 is please referred to, energy supply control system device connection schematic diagram of the invention, as shown in figure 11, a kind of confession are shown as
The system of can control includes inductive pick-up device 1, data converter 2, data processor 3, energy supply data processing equipment 4 and energy control
Device 5 processed,
Inductive pick-up device 1 obtains mixed dynamic electricity power data, in one embodiment, the electronic object of extended-range to acquire
Flowing vehicle includes that APU (Auxiliary Power Unit, auxiliary power unit) and two energy sources of power battery, extended-range are electronic
Logistic car vehicle operational mode can work in electric-only mode as needed, increase journey mode or hybrid mode (HEV).Work as work
When making Yu Zengcheng mode, it is the steady mistake of pure electric automobile that rate of economizing gasoline, which increases infinite approach pure electric automobile with battery capacity,
Cross vehicle；Data converter 2 in one embodiment, passes through quantization parameter θ to obtain quantization change data hereSOC、Driving motor demand power and ternary lithium dynamical battery SOC are obtained by blurring operation from practical successive value discrete
Value；Data processor 3, to mix dynamic electricity power data according to the quantization change data discrete processes, to obtain mould
Characteristic is pasted, data processor 3 is connect with inductive pick-up device 1, and data processor 3 is connect with data converter 2, real one
Apply in example, by fuzzy control strategy by driving motor demand power, ternary lithium dynamical battery SOC (State Of Charge,
Battery capacity), the exact values such as APU demand power by defining quantization parameter be converted to fuzzy language value；Energize data processing dress
4 are set, to fuzzy characteristics data described in iterative processing, to obtain energizing at data processing equipment 4 and data for can control data
It manages device 3 to connect, in one embodiment, passes through proportionality coefficientBy the discrete solution de-fuzzy of output variable APU demand power
After obtain practical solution, in the present embodiment, genetic algorithm can be used to the electronic logistic car fuzzy control energy management plan of extended-range
Slightly parameter optimization；Energy controller 5, for controlling power battery and engine, energy controller 5 for can control data with described
It is connect with energy supply data processing equipment 4, in one embodiment, by fuzzy control strategy by driving motor demand power, ternary
The exact values such as lithium dynamical battery SOC (State Of Charge, battery capacity), APU demand power are by defining quantization parameter
It is converted to fuzzy language value, in the present embodiment, in battery capacity abundance, power battery driving motor provides vehicle driving
Power demand, engine is not involved in work at this time.When battery power consumption to a certain extent when, engine start, engine is
Battery provides energy and charges to power battery.When battery capacity abundance, engine stops working again, drives electricity by battery
Machine provides vehicle driving, and fuel cell car FCV, the hybrid vehicle HEV and pure electric automobile EV tri- in electric car are big
Class will drive wheels travel with motor, and extended-range electric vehicle completes operation control strategy by entire car controller.Battery pack
It can be charged by ground charging pile or onboard charger, fuel combustion type or gas type can be used in engine.
Figure 12 is please referred to, the specific unit connection schematic diagram of data processor 3 in one embodiment being shown as in Figure 11,
As shown in figure 12, data processor 3 includes power electricity unit 31, processing unit 32 and characteristic unit 33, power electricity
Unit 31, to extract engine power feature and battery capacity feature in the mixed dynamic electricity power data；Processing unit
32, to obtain predetermined analog prediction model, processing unit 32 is connect with power electricity unit 31, in one embodiment, can root
According to such as motor demand power subordinating degree function, power battery pack SOC subordinating degree function and APU output power subordinating degree function mould
Send out the fuzzy characteristics of motivation, auxiliary power unit and power battery pack；Characteristic unit 33, to the predetermined analog
Prediction model handles the engine power feature and the battery capacity feature, to obtain the fuzzy characteristics data, feature
Data cell 33 is connect with processing unit 32, in one embodiment, as shown in figure 3, Fuzzy control system structure is dual input, list
Output, using Mamdani inference method.
Figure Figure 13 is please referred to, the specific component connection of the characteristic unit 33 being shown as in Figure 12 in one embodiment is shown
It is intended to, as shown in figure 13, characteristic unit 33 includes partitioning component 331, engine power component 332, battery capacity component
333 and characteristic processing component 334, partitioning component 331, to obtain the fuzzy division data in the simulation prescheme；Start
Machine (PCC) power 332 is engine fuzzy subset to divide the engine power feature according to the fuzzy division data,
Engine power component 332 is connect with partitioning component 331；Battery capacity component 333, to be drawn according to the fuzzy division data
Dividing the battery capacity feature is electricity fuzzy subset, and battery capacity component 333 is connect with partitioning component 331；Characteristic processing group
Part 334, to handle the engine fuzzy subset and the electricity fuzzy subset with the simulative prediction model, to obtain
The fuzzy characteristics data, characteristic processing component 334 are connect with battery capacity component 333.In one embodiment, extended-range vehicle
Dynamical system can be made of electrokinetic cell system, power-driven system, whole-control system and accessory power system (APU).
Figure 14 is please referred to, the specific unit connection of energy supply data processing equipment 4 in one embodiment being shown as in Figure 11
Schematic diagram, as shown in figure 14, energy supply data processing equipment 4 include Policy receipt unit 41, extraction unit 42,43 and of iteration unit
Control storage unit 44, Policy receipt unit 41, to obtain default Genetic Strategies data, in one embodiment, with such as vehicle
Dynamic model, wheel model, legacy system model, control strategy model treatment input data such as target vehicle speed obtain electricity
The input data of machine model and engine mockup, with such as motor model, engine mockup and battery model and foregoing model
Processing obtains actual vehicle speed；Extraction unit 42, to extract the iteration scale parameter in the default Genetic Strategies data, ratio
Parameter and stopping criterion for iteration, extraction unit 42 are connect with policing policy receiving unit 41, in one embodiment, by using something lost
Propagation algorithm has carried out power distribution fuzzy control energy management strategies between the electronic logistic car power battery of extended-range and APU excellent
Change to reduce comprehensive oil consumption and promote the power battery service life, in the present embodiment, settable genetic manipulation maximum number of iterations is
Such as 40 generations, initial population quantity such as 40, optimized variable number is such as 3, using binary coding, to subordinating degree function and mould
Paste control rule encoding, single variable code length are such as 20；The total length of individual is such as 60, and setting generation gap is for example
0.7；Iteration unit 43, to the fuzzy characteristics data according to iterative parameter and scale parameter iteration, until the fuzzy spy
Sign data meet the stopping criterion for iteration, and iteration unit 43 is connect with extraction unit 42, in one embodiment, can be based on
Simulink builds the electronic logistics vehicle model of extended-range, and intersects in the present embodiment using with Advisor associative simulation
Operation carries out mutation operation according to mutation probability, obtains next-generation group.Next-generation group repeats the above process, until into
Changing algebra terminates；Storage unit 44 being controlled, for can control data, controlling storage unit to save present Fuzzy characteristic
44 connect with iteration unit 43.
Figure 15 is please referred to, the logistics device device connection schematic diagram that the present invention applies, as shown in figure 15, a kind of object are shown as
Stream device includes apparatus casing 10, power supply device 20 and energy supply control system 30, apparatus casing 10；Power supply device 20, is set to
Inside the apparatus casing；The energy supply control system 30 of logistics device is installed in the power supply device 20, comprising: inductive pick-up
Device obtains mixed dynamic electricity power data to acquire；Data converter, to obtain quantization change data；Data processor,
To mix dynamic electricity power data according to the quantization change data discrete processes, to obtain fuzzy characteristics data；Energy supply
Data processing equipment, to fuzzy characteristics data described in iterative processing, to obtain for can control data；Energy controller is used for
With described for can control data control power battery and engine.
In conclusion having gone out a kind of method of determining engine working point, the method considers engine working point to electricity
The influence of machine loss, in dynamic programming method, a certain item for state variable battery SOC in the two neighboring stage shifts road
For diameter, since the variable quantity of SOC is determining, available corresponding target battery power.
To sum up, the present invention provides the logistics device of a kind of energy supply control method, system and its application, solves the prior art
The energy-saving effect of existing logistics transportation equipment is poor and the short technical problem of battery life.
Claims (9)
1. a kind of energy supply control method characterized by comprising
Acquisition obtains mixed dynamic electricity power data；
Obtain quantization change data；
Dynamic electricity power data is mixed, according to the quantization change data discrete processes to obtain fuzzy characteristics data；
Fuzzy characteristics data described in iterative processing, to obtain for can control data；
With described for can control data control power battery and engine.
2. the method according to claim 1, wherein it is described convert the fuzzy characteristics data the step of, packet
It includes:
Extract the engine battery feature in the mixed dynamic electricity power data；
Obtain predetermined analog prediction model；
The engine battery feature is handled with the predetermined analog prediction model, to obtain the fuzzy characteristics data.
3. method according to claim 1 or 2, which is characterized in that the step of the calculating fuzzy characteristics data, packet
It includes:
Obtain the fuzzy division data in the simulative prediction model；
Dividing the engine battery feature according to the fuzzy division data is fuzzy subset；
The fuzzy subset is handled with the simulative prediction model, to obtain the fuzzy characteristics data.
4. the method according to claim 1, wherein the iteration obtain it is described for can control data the step of,
Include:
Obtain preset iteration scale parameter and stopping criterion for iteration；
According to fuzzy characteristics data described in iteration scale parameter iteration, until meeting the stopping criterion for iteration；
Saving present Fuzzy characteristic is for can control data.
5. a kind of energy supply control system characterized by comprising
Inductive pick-up device obtains mixed dynamic electricity power data to acquire；
Data converter, to obtain quantization change data；
Data processor, to mix dynamic electricity power data according to the quantization change data discrete processes, to obtain mould
Paste characteristic；
Data processing equipment is energized, to fuzzy characteristics data described in iterative processing, to obtain for can control data；
Energy controller, for controlling power battery and engine for can control data with described.
6. system according to claim 5, which is characterized in that the data processor, comprising:
Power electricity unit, to extract engine power feature and battery capacity spy in the mixed dynamic electricity power data
Sign；
Processing unit, to obtain predetermined analog prediction model；
Characteristic unit, to handle the engine power feature and battery electricity with the predetermined analog prediction model
Measure feature, to obtain the fuzzy characteristics data.
7. system according to claim 5 or 6, which is characterized in that the characteristic unit, comprising:
Partitioning component, to obtain the fuzzy division data in the simulation prescheme；
Engine power component is fuzzy for engine to divide the engine power feature according to the fuzzy division data
Subset；
Battery capacity component is electricity fuzzy subset to divide the battery capacity feature according to the fuzzy division data；
Characteristic processing component, to handle the engine fuzzy subset and the fuzzy son of the electricity with the simulative prediction model
Collection, to obtain the fuzzy characteristics data.
8. system according to claim 5, which is characterized in that the energy supply data processing equipment, comprising:
Policy receipt unit, to obtain default Genetic Strategies data；
Extraction unit, to extract iteration scale parameter, scale parameter and iteration ends in the default Genetic Strategies data
Condition；
Iteration unit, to the fuzzy characteristics data according to iterative parameter and scale parameter iteration, until the fuzzy characteristics
Data meet the stopping criterion for iteration；
Storage unit is controlled, to save present Fuzzy characteristic as can control data.
9. a kind of logistics device characterized by comprising
Apparatus casing；
Power supply device is set to inside the apparatus casing；
The energy supply control system of logistics device is installed in the power supply device, comprising:
Inductive pick-up device obtains mixed dynamic electricity power data to acquire；
Data converter, to obtain quantization change data；
Data processor, to mix dynamic electricity power data according to the quantization change data discrete processes, to obtain mould
Paste characteristic；
Data processing equipment is energized, to fuzzy characteristics data described in iterative processing, to obtain for can control data；
Energy controller, for controlling power battery and engine for can control data with described.
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