CN107139762A - A kind of electric automobile optimization charge control method and its system - Google Patents
A kind of electric automobile optimization charge control method and its system Download PDFInfo
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- CN107139762A CN107139762A CN201710411560.XA CN201710411560A CN107139762A CN 107139762 A CN107139762 A CN 107139762A CN 201710411560 A CN201710411560 A CN 201710411560A CN 107139762 A CN107139762 A CN 107139762A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/14—Plug-in electric vehicles
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- Mechanical Engineering (AREA)
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- Sustainable Development (AREA)
- Sustainable Energy (AREA)
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- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
Optimize charge control method the invention discloses a kind of electric automobile, it is intended to which situation of the user to the different demands of charging interval, charging capacity and charge efficiency can not be met very well by solving existing charging method, be comprised the following steps：User is obtained to charging interval, the relative requirements degree of three charging indexs of charging capacity and charge efficiency, and is embodied by weight coefficient form；Obtain the state parameters such as DC internal resistance, SOC and the open-circuit voltage and SOC corresponding relations of battery pack；According to charge requirement and battery state information, object function is built by optimization aim of charging interval, charging capacity and charge efficiency, charging current is optimized using multi-objective particle, obtain required charging current waveform；Charger is controlled according to charging current waveform, the charging on demand to batteries of electric automobile group is realized.Invention also provides for realizing that electric automobile optimizes the charging system for electric automobile of charge control method.
Description
Technical field
The present invention relates to a kind of technical field of charging electric vehicle, more particularly to a kind of charging electric vehicle optimal control
Method and system.
Background technology
Electric automobile can realize " zero-emission " and have the advantages that environmental protection, energy-conservation, low noise, and electric automobile is with electric generation
Oil, is the important means for solving the energy and environmental problem, is increasingly becoming the vehicles of effective and safe, it is in performance and economy
Aspect starts worldwide gradually popularization and application already close to even better than conventional fuel oil automobile.And current, electronic vapour
The problem of faced one of the further popularization of car is critically important is exactly the charging problems of electric automobile.The presence limit of charging problems
The service efficiency of electric automobile has been made, has been added " the mileage anxiety " of electric automobile user.
During actual use, under different service conditions, electric automobile user often exists different
Use demand.It is badly in need of some using in the case of electric automobiles, user's desirably shorter charging interval；Needing
In the case of travelling at a distance, user's desirably larger charging capacity；And at the same time, in some other
In the case of, user wants to obtain higher charge efficiency to obtain lower charging cost.According to filling for user
Electric demand is different, and optimal charging current waveform is also differed, and this is accomplished by a kind of charge control method can be according to user
Different use demand and produce different charging current waveforms.
The content of the invention
User can not be met very well to charging interval, charging capacity and charging in order to solve existing charging method
The situation of the different demands of efficiency, charge control method is optimized the invention provides a kind of electric automobile.
The purpose of the present invention is realized by following scheme：
A kind of electric automobile optimizes charge control method, comprises the following steps：
Step 1: obtaining user to the relative of three charging indexs such as charging interval, charging capacity and charge efficiency
Desirability, and this desirability is embodied by the form of weight coefficient；
Step 2: obtaining the state parameters such as DC internal resistance, SOC and the open-circuit voltage and SOC corresponding relations of battery pack；
Step 3: according to the charge requirement and battery state parameter that are obtained, with the charging interval, charging capacity and
Charge efficiency is that optimization aim builds object function, and charging current is optimized using multi-objective particle, obtained
To required charging current waveform；
Step 4: the charging current waveform obtained according to multi-objective particle swarm optimization is controlled to charger, and then
Realize the charging on demand to batteries of electric automobile group.
It is used to realize that the electric automobile of the electric automobile optimization charge control method fills invention also provides one
Electric system：It is made up of charging performing module, charge control module, communication module and human-computer interaction module；Charge control module
The battery management system with charging performing module, human-computer interaction module and electric automobile is communicated by communication module respectively
Connection；Charge control module is obtained according to the charge requirement obtained from human-computer interaction module and from cell management system of electric automobile
The batteries of electric automobile group state parameter taken, using charge requirement as optimization aim, using battery status parameter as model parameter, is used
Multi-objective particle swarm optimization determines actual charging current waveform, and charging performing module is controlled, and realizes to electronic vapour
The charging on demand of car battery pack.
The present invention has advantages below：
The charging method of the present invention has fully taken into account the actual charge requirement of user, according to user for the charging interval,
The actual requirement of charging capacity (course continuation mileage) and charge efficiency produces different charge control strategies, so that actual
Charging effect more meet the expection of user.
Brief description of the drawings
Fig. 1 optimizes the flow chart of charge control method for the electric automobile of the present invention
Fig. 2 optimizes the structured flowchart of charging system for the electric automobile of the present invention
Fig. 3 is battery model simulation calculation submodule workflow diagram of the invention
Fig. 4 is the workflow diagram of the multi-objective particle swarm optimization submodule of the present invention
Fig. 5 is multistage constant-current charge control method flow chart of the invention
Embodiment
The core of the present invention is to provide a kind of electric automobile optimization charge control method.In order that those skilled in the art
The solution of the present invention is more fully understood, the present invention is described in further detail with reference to the accompanying drawings and detailed description.
Optimize the flow chart of charge control method as shown in Figure 1 for the electric automobile of the present invention.
Step 1: obtaining user to the relative of three charging indexs such as charging interval, charging capacity and charge efficiency
Desirability, and this desirability is embodied by the form of weight coefficient.User for charging interval, charging capacity with
And the input of the index such as charge efficiency can also be using ambiguity type input using the input of numerical value formula；Input mode can be adopted
With different input modes such as button, touch-screen type, telecommunication formula, phonetic entry formulas.
Step 2: obtaining the state parameters such as DC internal resistance, SOC and the open-circuit voltage and SOC corresponding relations of battery pack；And
By acquired battery parameter, battery pack equivalent-circuit model is built.The equivalent-circuit model is by battery open circuit electricity
Pressure and battery pack DC internal resistance composition；The terminal voltage of battery pack is made up of battery open circuit voltage and DC internal resistance voltage；Work as electricity
When pond group is in charged state, terminal voltage is the open-circuit voltage and DC internal resistance voltage sum.It is equivalent by the battery pack
Model, can calculate the battery pack under corresponding charging current waveform terminal voltage response and charging at the end of charging when
Between, charging capacity and charge efficiency.
Step 3: according to the charge requirement and battery state parameter that are obtained, with the charging interval, charging capacity and
Charge efficiency is that optimization aim builds object function, and charging current is optimized using multi-objective particle, obtained
To required charging current waveform.During multi-objective particle swarm optimization, each particle represents a kind of multistage constant current
The currents combination of charging, can obtain the charging interval that can be realized under the currents combination by battery simulation model, fill
The data such as capacitance and charge efficiency.In iterative process each time, held according to the charging interval, charging that are each obtained
Each particle in the information such as amount and charge efficiency, population is combined with certain rule to optimal charging current evolves, so that
Obtain optimal charging current combination.The multistage constant-current charge is the charge control side being made up of multiple constant-current charging phases
Method；The termination condition of each constant-current phase is default maximum charging voltage value；When the charging voltage in a stage reach it is default
During value, charging process is all done automatically into next stage until all charging stages, then whole charging process terminates.
In multistage constant-current charge, the charging current in each stage is both less than the charging current value of previous stage.
Step 4: the charging current waveform obtained according to multiple-objection optimization is controlled to charger, and then realization pair
The charging on demand of batteries of electric automobile group.
The electric automobile for being illustrated in figure 2 the present invention optimizes the structured flowchart of charging system.
The system is made up of charging performing module 1, charge control module 2, communication module 3 and human-computer interaction module 4.
The battery management of the charge control module 2 respectively with the charging performing module 1, human-computer interaction module 4 and electric automobile
System 5 is communicated by the communication module.The charge control module 2 is according to from filling acquired in human-computer interaction module 4
Electric demand and the batteries of electric automobile group state parameter obtained from cell management system of electric automobile 5, using charge requirement to be excellent
Change target, using battery status parameter as model parameter, actual charging current waveform is determined using multi-objective particle swarm optimization, and
The charging performing module is controlled, so as to realize the charging on demand to batteries of electric automobile group.
Charge control module includes battery model simulation calculation submodule, multi-objective particle swarm optimization submodule.The electricity
Pool model simulation calculation submodule is according to the DC internal resistance of battery pack, open-circuit voltage-SOC relations, charging starting SOC and charging
The information such as electric current carry out simulation calculation to the charging process of battery pack, and obtain the end of the battery pack under corresponding charging current waveform
Charging interval, charging capacity and charge efficiency at the end of voltage responsive and charging.Multi-objective particle swarm optimization
Module according to from the charge requirement acquired in human-computer interaction module and from battery model simulation calculation submodule obtain it is corresponding
The information such as charging interval, charging capacity and charge efficiency under charge waveforms, carry out multiple-objection optimization, when optimization aim is charging
Between, charging capacity and charge efficiency.The charging performing module can be incited somebody to action according to the control instruction of the charge control module
The alternating current of grid side is converted into the direct current with corresponding current value, so as to realize the charging to batteries of electric automobile group.
It is illustrated in figure 3 the battery model simulation calculation submodule workflow diagram of the present invention.
Battery parameter is initialized first.Then emulation step number is updated, and under current emulation step number
Battery SOC is calculated；Charging process terminates if SOC value is more than default maximum SOC value, the root if the condition is invalid
Battery model parameter is updated after present battery status according to being obtained from cell management system of electric automobile, and according to formula
(1)~(6) are calculated the state parameter such as charging interval, charging capacity, charge loss and cell voltage, battery temperature.
Tch=k Δs t (1)
Ccharged=Ccharged+ILΔt (2)
Up,k=exp (- Δ t/ τ) × Up,k-1+(1-exp(-Δt/τ))×ILRr (3)
Ut,k=Uoc+ILRr+Up,k (4)
Wherein, TchFor the charging interval, k is step number, and Δ t is step-length time, CchargedFor charging capacity, ILFor charging electricity
Stream, Up,kPolarizing voltage is walked for kth, τ is circuit time constant, RrFor battery DC internal resistance, Ut,kBattery terminal voltage is walked for kth,
UocFor battery open circuit voltage, QlossIt is lost for rechargeable energy, RpFor polarization resistance, T is battery temperature, REffFor battery heat exchange
Coefficient, TaFor environment temperature, CTFor battery specific heat capacity.
After battery charging state is obtained, battery current voltage is judged, if current voltage is more than preset charged
Voltage then enters next charging stage, otherwise maintains the current charging stage.Then whole charging is imitative after the completion of all charging stages
True process terminates.
It is illustrated in figure 4 the workflow diagram of the multi-objective particle swarm optimization submodule of the present invention.
During multi-objective particle swarm optimization is performed, first to representated by number of particles in population, each particle
The parameters such as charging current value are initialized；Then by battery model simulation calculation submodule battery simulation to each particle institute's generation
The charging current of table is emulated, and obtains the numerical value such as charging interval, charging capacity and charge efficiency；When obtaining each particle
After charging performance, population is updated according to formula (7) and (8), and all non-domination solutions are stored in REP
In (repository, Noninferior Solution Set)；Judge whether population reaches the condition of convergence, terminate optimization process if reaching, it is no
Above-mentioned population renewal process is then repeated until reaching the condition of convergence.
Wherein,For the flying speed of i-th of particle in the population that the step population quantity of kth+1 is n, ω
For inertia weight, Λ1And Λ2It is Studying factors, R1And R2It is two random numbers between 0~1,It is individual extreme value,
REP (h) is global non-dominant extreme value,It is particle current location.
As shown in figure 5, the citation form of electric automobile optimization charge control method of the present invention is multistage constant-current charge, it is excellent
Change the optimum combination that result is each stage charging current value.Whole charging process is made up of several constant-current charge processes, in perseverance
During current charge, charging current remains constant.Meanwhile, whole charging process has a default magnitude of voltage, whenever
Then charging process enters next stage to cell voltage when meeting or exceeding default magnitude of voltage, when all charging processes all terminate
Afterwards, whole charging process terminates.
Embodiment
After charging starts, user passes through one kind in the mode such as button or touch-screen, telecommunication, phonetic entry
Desired charge requirement is inputted to charge control system, assumes that user is to the desirability in charging interval in the present embodiment
0.5, the desirability to charging capacity is 0.3, and the desirability to charge loss is 0.2, then divides in man-machine interactive system
Not Wei charging interval, charging capacity and charge efficiency three be entered as 0.5,0.3,0.2.Description of the user to charge requirement
The concrete numerical value mode in the present embodiment is not limited to, assignment can be also carried out by the way of fuzzy, such as charging interval is shorter, very
Short, charging capacity is very big etc..
Charge control module by communication module from cell management system of electric automobile obtain battery pack DC internal resistance,
The state parameter such as SOC and open-circuit voltage and SOC corresponding relations, and the battery parameter is passed into battery model emulation meter
Operator module.Battery model simulation calculation submodule is originated according to the DC internal resistance of battery pack, open-circuit voltage-SOC relations, charging
The information such as SOC and charging current carries out simulation calculation to the charging process of battery pack, and obtains in corresponding charging current waveform
Charging interval, charging capacity and charge efficiency at the end of the terminal voltage response and charging of lower battery pack.Multi-target particle
Group's optimization submodule carries out parameter initialization first, and each particle in population is entered as to random multistage constant-current charge
Currents combination.The charging current combined information entrained by each particle is so passed into battery model simulation calculation submodule, and
Charging interval, charging capacity and the charge efficiency under corresponding charging current combination are obtained from battery model simulation calculation submodule
Performance.According to from the charge requirement acquired in human-computer interaction module and from battery model simulation calculation submodule obtain it is corresponding
The information such as charging interval, charging capacity and charge efficiency under charge waveforms, carry out multiple-objection optimization, when optimization aim is charging
Between, charging capacity and charge efficiency.In iterative process each time, held according to the charging interval, charging that are each obtained
Each particle in the information such as amount and charge efficiency, population is combined with certain rule to optimal charging current evolves, so that
Obtain optimal charging current combination.
After optimal charging current combination is obtained, multi-objective particle swarm optimization submodule passes through the charging current waveform logical
Letter module passes to charging performing module, and charging performing module is according to the charging current waveform obtained to batteries of electric automobile group
Charged.During charging, when the charging voltage in a stage reaches preset value, charging process is automatically into next
In the stage, all it is done when all charging stages, then whole charging process terminates.
Claims (6)
1. a kind of electric automobile optimizes charge control method, it is characterised in that comprise the following steps：
Step 1: obtaining user to charging interval, the relative requirements journey of three charging indexs of charging capacity and charge efficiency
Degree, and this desirability is embodied by the form of weight coefficient；
Step 2: battery state parameter is obtained, including DC internal resistance, SOC and open-circuit voltage and SOC corresponding relations；
Step 3: according to the charge requirement and battery state parameter that are obtained, with charging interval, charging capacity and charging
Efficiency is that optimization aim builds object function, and charging current is optimized using multi-objective particle, institute is obtained
The charging current waveform needed；
Step 4: the charging current waveform obtained according to multi-objective particle swarm optimization is controlled to charger, realize to electricity
The charging on demand of electrical automobile battery pack.
2. a kind of electric automobile optimization charge control method as claimed in claim 1, it is characterised in that the optimization charging control
The citation form of method processed is multistage constant-current charge, and optimum results are the optimum combination of each stage charging current value, are entirely filled
Electric process is made up of several constant-current charge processes, during constant-current charge, and charging current remains constant.
3. a kind of charging electric vehicle for being used to realize that a kind of electric automobile as claimed in claim 1 optimizes charge control method
System, it is characterised in that be made up of charging performing module, charge control module, communication module and human-computer interaction module；Charging
Battery management system of the control module respectively with charging performing module, human-computer interaction module and electric automobile passes through communication module
Carry out communication connection；Charge control module is according to the charge requirement obtained from human-computer interaction module and from batteries of electric automobile pipe
The batteries of electric automobile group state parameter that reason system is obtained, using charge requirement as optimization aim, using battery status parameter as model
Parameter, actual charging current waveform is determined using multi-objective particle swarm optimization, and charging performing module is controlled, and is realized
Charging on demand to batteries of electric automobile group.
4. a kind of charging system for electric automobile as claimed in claim 3, it is characterised in that the charge control module includes electricity
Pool model simulation calculation submodule, multi-objective particle swarm optimization submodule；The battery model simulation calculation submodule is according to electricity
DC internal resistance, open-circuit voltage-SOC relations, charging starting SOC and the charging current of pond group, the charging process to battery pack are entered
Row simulation calculation, and when obtaining the charging at the end of the terminal voltage response and charging of the battery pack under corresponding charging current waveform
Between, charging capacity and charge efficiency；The multi-objective particle swarm optimization submodule is according to the charging obtained from human-computer interaction module
Charging interval, charging capacity and charging under demand and the corresponding charge waveforms obtained from battery model simulation calculation submodule
Efficiency information, carries out multiple-objection optimization, and optimization aim is charging interval, charging capacity and charge efficiency.
5. a kind of charging system for electric automobile as claimed in claim 4, it is characterised in that battery model simulation calculation
The course of work of module is：
Battery parameter is initialized first；
Then emulation step number is updated, and the battery SOC under current emulation step number is calculated；If SOC value is more than
Then charging process terminates default maximum SOC value, and basis is obtained from cell management system of electric automobile if above-mentioned condition is invalid
Battery model parameter is updated after obtaining current battery status parameter, and battery state parameter carried out according to following equation
Calculate：
Tch=k Δs t
Ccharged=Ccharged+ILΔt
Up,k=exp (- Δ t/ τ) × Up,k-1+(1-exp(-Δt/τ))×ILRr
Ut,k=Uoc+ILRr+Up,k
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Wherein, TchFor the charging interval, k is step number, and Δ t is step-length time, CchargedFor charging capacity, ILFor charging current, Up,k
Polarizing voltage is walked for kth, τ is circuit time constant, RrFor battery DC internal resistance, Ut,kBattery terminal voltage, U are walked for kthocFor electricity
Pond open-circuit voltage, QlossIt is lost for rechargeable energy, RpFor polarization resistance, T is battery temperature, REffFor battery heat exchange coefficient, Ta
For environment temperature, CTFor battery specific heat capacity；
Battery current voltage is judged, next charging stage is entered if current voltage is more than preset charge voltage, it is no
The current charging stage is then maintained, until all charging stages complete, then whole charging simulation process terminates.
6. a kind of charging system for electric automobile as claimed in claim 4, it is characterised in that multi-objective particle swarm optimization
The course of work of module is：
The charge parameter representated by number of particles in population, each particle is initialized first；
Then the charging current representated by each particle is emulated by battery simulation, obtain the charging interval, charging capacity with
And charge efficiency；After the charging performance of each particle is obtained, population is updated according to following equation, and will be all non-
Dominate in solution deposit REP；Judge whether population reaches the condition of convergence, terminate optimization process if reaching, otherwise repeat
Population renewal process is stated until reaching the condition of convergence；
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<mo>)</mo>
</mrow>
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<mrow>
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<mi>x</mi>
<mi>i</mi>
<mi>n</mi>
</msubsup>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msubsup>
<mi>x</mi>
<mi>i</mi>
<mi>n</mi>
</msubsup>
<mrow>
<mo>(</mo>
<mi>k</mi>
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</mrow>
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<msubsup>
<mi>v</mi>
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Wherein,For the flying speed of i-th of particle in the population that the step population quantity of kth+1 is n, ω is used
Property weight, Λ1And Λ2It is Studying factors, R1And R2It is two random numbers between 0~1,It is individual extreme value, REP
(h) it is global non-dominant extreme value,It is particle current location.
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| CN109346787A (en) * | 2018-09-21 | 2019-02-15 | 北京机械设备研究所 | A kind of electric automobile power battery adaptive optimization charging method |
| CN109713762A (en) * | 2019-02-28 | 2019-05-03 | 深圳猛犸电动科技有限公司 | A kind of charge control method, device and terminal device |
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