CN109888414A - Management method, the device and system of power battery - Google Patents
Management method, the device and system of power battery Download PDFInfo
- Publication number
- CN109888414A CN109888414A CN201811565326.3A CN201811565326A CN109888414A CN 109888414 A CN109888414 A CN 109888414A CN 201811565326 A CN201811565326 A CN 201811565326A CN 109888414 A CN109888414 A CN 109888414A
- Authority
- CN
- China
- Prior art keywords
- charge
- data
- discharge
- power battery
- charging
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- 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
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Landscapes
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The present invention provides a kind of management method of power battery, device and system, this method comprises: obtaining the charge and discharge data of power battery and the environmental data of local environment, according to charge and discharge data and environmental data, using the effective charging and recharging model being previously obtained, obtain the charge and discharge strategy of power battery, charge and discharge strategy includes charging strategy and electric discharge strategy, charging and recharging model is obtained according to machine learning algorithm using multiple charge and discharge are data-optimized, then electric discharge strategy is sent to power battery, and charging strategy is sent to the corresponding charging cabinet of power battery.In such manner, it is possible to remotely be managed power battery, while the charge and discharge strategy of motivation of adjustment battery in real time, the safety in utilization of power battery is improved, while improving the effective rate of utilization of power battery.
Description
Technical field
The present invention relates to technical field of battery management more particularly to a kind of management methods of power battery, device and system.
Background technique
Power battery be powered tools source power supply, Duo Zhiwei electric car, electric train, electric bicycle,
Golf cart provides the battery of power, formulate effective battery management scheme for the service performance of battery, safety with
And it is all quite important for reduction battery failures rate etc..
In current battery management scheme, battery charging and discharging strategy is generated according to laboratory data, then by the charge and discharge
Strategy, which is solidificated in, to be internally embedded in formula software and hardware system, is managed and is protected to battery, wherein in the data of battery are stored in
In the SD card set.
However, battery pack once leaves battery assembly factory, long-range management can not be just provided, once failure, needs power electric
Pond supplier obtains battery data to scene, and carries out accident analysis, and operation maintenance cost is high；Meanwhile the battery formulated in advance fills
Electric discharge strategy can not accomplish the efficiency for charge-discharge of battery under various circumstances optimal, reduce the effective rate of utilization of battery.
Summary of the invention
To solve problems of the prior art, the present invention provides the management method of power battery a kind of, device and is
System, remotely to be managed power battery, while the charge and discharge strategy of motivation of adjustment battery in real time, improve power battery
Safety in utilization, and improve the effective rate of utilization of power battery.
In a first aspect, the embodiment of the present invention provides a kind of management method of power battery, comprising:
Obtain the charge and discharge data of the power battery and the environmental data of local environment；
Institute is obtained using the effective charging and recharging model being previously obtained according to the charge and discharge data and the environmental data
The charge and discharge strategy of power battery is stated, the charge and discharge strategy includes charging strategy and electric discharge strategy, and the charging and recharging model is
It is obtained according to machine learning algorithm using multiple charge and discharge are data-optimized；
The electric discharge strategy is sent to the power battery, and it is corresponding that charging strategy is sent to the power battery
Charging cabinet.
Optionally, the charge and discharge data include charge data and discharge data, it is described according to the charge and discharge data and
The environmental data obtains the charge and discharge strategy of the power battery using the effective charging and recharging model being previously obtained, comprising:
If the charge and discharge data are valid data, according to the charge data and effective charge model, obtain described dynamic
The charging strategy of power battery；
According to the discharge data and effective discharging model, the electric discharge strategy of the power battery is obtained；
Wherein, effective charging and recharging model includes effective charge model and effective discharging model.
Optionally, the method also includes:
According to first condition, determine whether the charge and discharge data and/or the environmental data are invalid data, described
One condition is the condition of preset determining data invalid；
If the charge and discharge data and the environmental data are not invalid data, according to second condition, determine described in
Charge and discharge data are for valid data or fail data, and the second condition includes the effective condition sum number of preset determining data
According to the condition of failure.
Optionally, the method also includes:
If the charge and discharge data predict the power using the failure charging and recharging model being previously obtained for fail data
The probability that battery breaks down in charge and discharge process；
If the probability that the power battery breaks down during the charging process is greater than default first probability, stopping will be carried
The instruction of charging is sent to the charging cabinet；
If the probability that the power battery breaks down during discharge is greater than default second probability, stopping will be carried
Electric discharge is sent to the power battery.
Optionally, the method also includes:
Obtain effectively charging initial model；
Obtain effective charge data set；
Optimize effective charging initial model using effective charge data set according to machine learning algorithm, obtain
Take effective charge model.
Optionally, the method also includes:
Obtain effectively electric discharge initial model；
Obtain effective discharge data set；
Optimize effective electric discharge initial model using effective discharge data set according to machine learning algorithm, obtain
Take effective discharging model.
Optionally, the method also includes:
Obtain failure charging initial model；
Obtain failure charge data set；
Optimize the failure charging initial model using the failure charge data set according to machine learning algorithm, obtain
Take the failure charge model.
Optionally, the method also includes:
Obtain failure electric discharge initial model；
Obtain failure discharge data set；
Optimize the failure electric discharge initial model using the failure discharge data set according to machine learning algorithm, obtain
Take the failure discharging model.
Second aspect, the embodiment of the present invention provide a kind of power battery management device, comprising:
Module is obtained, for obtaining the charge and discharge data of the power battery and the environmental data of local environment；
Institute is obtained using the effective charging and recharging model being previously obtained according to the charge and discharge data and the environmental data
The charge and discharge strategy of power battery is stated, the charge and discharge strategy includes charging strategy and electric discharge strategy, and the charging and recharging model is
It is obtained according to machine learning algorithm using multiple charge and discharge are data-optimized；
Sending module for the electric discharge strategy to be sent to the power battery, and charging strategy is sent to described
The corresponding charging cabinet of power battery.
Optionally, the acquisition module, is also used to:
If the charge and discharge data are valid data, according to the charge data and effective charge model, obtain described dynamic
The charging strategy of power battery；
According to the discharge data and effective discharging model, the electric discharge strategy of the power battery is obtained；
Wherein, effective charging and recharging model includes effective charge model and effective discharging model.
Optionally, described device further include:
Determining module, for determining whether the charge and discharge data and/or the environmental data are nothing according to first condition
Data are imitated, the first condition is the condition of preset determining data invalid；
If the charge and discharge data and the environmental data are not invalid data, according to second condition, determine described in
Charge and discharge data are for valid data or fail data, and the second condition includes the effective condition sum number of preset determining data
According to the condition of failure.
Optionally, described device further include:
Processing module, if being fail data for the charge and discharge data, using the failure charging and recharging model being previously obtained,
Predict the probability that the power battery breaks down in charge and discharge process；
The sending module, if being also used to probability that the power battery breaks down during the charging process is greater than default the
One probability will carry the instruction for stopping charging and be sent to the charging cabinet；
If the probability that the power battery breaks down during discharge is greater than default second probability, stopping will be carried
Electric discharge is sent to the power battery.
Optionally, the acquisition module, is also used to:
Obtain effectively charging initial model；
Obtain effective charge data set；
The processing module, is also used to according to machine learning algorithm, using effective charge data set, described in optimization
Effectively charging initial model, obtains effective charge model.
Optionally, the acquisition module, is also used to:
Obtain effectively electric discharge initial model；
Obtain effective discharge data set；
The processing module, is also used to according to machine learning algorithm, using effective discharge data set, described in optimization
Effectively electric discharge initial model, obtains effective discharging model.
Optionally, the acquisition module, is also used to:
Obtain failure charging initial model；
Obtain failure charge data set；
The processing module, is also used to according to machine learning algorithm, using the failure charge data set, described in optimization
Failure charging initial model, obtains the failure charge model.
Optionally, the acquisition module, is also used to:
Obtain failure electric discharge initial model；
Obtain failure discharge data set；
The processing module, is also used to according to machine learning algorithm, using the failure discharge data set, described in optimization
Failure electric discharge initial model, obtains the failure discharging model.
The third aspect, the embodiment of the present invention provide a kind of power battery management system, comprising:
Power battery, power battery charging cabinet, and realize the power battery management equipment of first aspect the method.
Fourth aspect, the embodiment of the present invention provide a kind of power battery management equipment, comprising:
Processor, memory, transmitter, receiver and computer program；
Wherein, the computer program is stored in the memory, and is configured as being executed by the processor,
The computer program includes the instruction for executing method described in first aspect.
5th aspect, the embodiment of the present invention provide a kind of computer readable storage medium, the computer-readable storage medium
Matter is stored with computer program, and the computer program makes power battery management equipment execute method described in first aspect.
Management method, the device and system of power battery provided in an embodiment of the present invention, this method comprises: obtaining power electric
The charge and discharge data in pond and the environmental data of local environment are had according to charge and discharge data and environmental data using what is be previously obtained
Charging and recharging model is imitated, the charge and discharge strategy of power battery is obtained, charge and discharge strategy includes charging strategy and electric discharge strategy, charge and discharge
Model is obtained according to machine learning algorithm using multiple charge and discharge are data-optimized, and electric discharge strategy is then sent to power electric
Pond, and charging strategy is sent to the corresponding charging cabinet of power battery.In such manner, it is possible to power battery is remotely managed, it is real
When motivation of adjustment battery charge and discharge strategy, improve the safety in utilization of power battery, while improving having for power battery
Imitate utilization rate.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention without any creative labor, may be used also for those of ordinary skill in the art
To obtain other drawings based on these drawings.
Fig. 1 is the schematic diagram of the management system of power battery provided in an embodiment of the present invention；
Fig. 2 is the flow diagram one of the management method of power battery provided in an embodiment of the present invention；
Fig. 3 is the flow diagram two of the management method of power battery provided in an embodiment of the present invention；
Fig. 4 is the flow diagram three of the management method of power battery provided in an embodiment of the present invention；
Fig. 5 is the flow diagram four of the management method of power battery provided in an embodiment of the present invention；
Fig. 6 is the structural schematic diagram one of the managing device of power battery provided in an embodiment of the present invention；
Fig. 7 is the structural schematic diagram two of the managing device of power battery provided in an embodiment of the present invention；
Fig. 8 is the hardware structural diagram of power battery management equipment provided in an embodiment of the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
The management method of power battery provided in an embodiment of the present invention can be applied to the applied field of power battery charge and discharge
Jing Zhong, in electric vehicle charge and discharge, be described below in battery be power battery.In existing battery management scheme, according to reality
The number of chambers is tested according to battery charging and discharging strategy is generated, then the charge and discharge strategy is solidificated in and is internally embedded in formula software and hardware system, it is right
Battery is managed and protects, wherein the data of battery are stored in built-in SD card.However, battery pack once leaves battery
Assembly plant can not just provide long-range management, once failure, needs power battery supplier to obtain battery data to scene, goes forward side by side
Row accident analysis, operation maintenance cost are high；Meanwhile the battery charging and discharging strategy formulated in advance can not be by battery under various circumstances
Efficiency for charge-discharge is accomplished optimal, reduces the effective rate of utilization of battery.
The embodiment of the present invention considers the above problem, proposes a kind of power battery method, apparatus, system, this method comprises:
The charge and discharge data of power battery and the environmental data of local environment are obtained, according to charge and discharge data and environmental data, using pre-
The effective charging and recharging model first obtained, obtains the charge and discharge strategy of power battery, and charge and discharge strategy includes charging strategy and electric discharge
Strategy, charging and recharging model is obtained according to machine learning algorithm using multiple charge and discharge are data-optimized, then by charging strategy
It is sent to power battery, and electric discharge strategy is sent to the corresponding charging cabinet of power battery.In such manner, it is possible to be carried out to power battery
Long-range management, while the charge and discharge strategy of motivation of adjustment battery in real time, improve the effective rate of utilization of power battery.
Technical solution of the present invention is described in detail with specific embodiment below.These specific implementations below
Example can be combined with each other, and the same or similar concept or process may be repeated no more in some embodiments.
Fig. 1 is the schematic diagram of power battery management system provided in an embodiment of the present invention, as shown in Figure 1, the power battery
Management system includes power battery 10, power battery charging cabinet 20 and power battery management equipment 30, in which:
Power battery 10 is the power supply in powered tools source, Duo Zhiwei electric car, electric train, electrical salf-walking
Vehicle, golf cart provide the battery (electric discharge) of power.
10 internal battery management of power battery (Battery Management System, BMS) module 11, global location
System (Global Positioning System, GPS) module 12, temperature-measuring module 13, communication module 14, wherein battery
Management module 11, for acquiring the discharge data of power battery 10, GPS module 12 is used to acquire the sea of power battery local environment
It pulls out, position etc., temperature-measuring module 13 is used to acquire the environment temperature of power battery local environment, and communication module 14 will be for that will adopt
The discharge data and environmental data collected is sent to power battery management equipment 30, and environmental data includes power battery local environment
Height above sea level, position and temperature etc..
Power battery charging cabinet 20 is used to provide electricity (charging) to power battery 10, including communication module 21 and charging plan
Slightly control system 22.
When power battery 10 charges, power battery charging cabinet 20 and power battery 10 can pass through physical connection, power
The BMS module 11 of battery 10 acquires the charge data of power battery 10, and GPS module 12 is for acquiring power battery local environment
Height above sea level, position etc., temperature-measuring module 13 are used to acquire the environment temperature of power battery local environment, then pass through charging cabinet 20
Communication module 21 charge data and environmental data are sent to power battery management equipment 30, charging strategy control system 22.
Effective charging strategy that charging strategy control system 22 is used to be sent to according to power battery management equipment 30 controls
Charging, while charging is stopped for the power battery that high probability breaks down according to failure charging strategy.
Communication module 14 and communication module 21 may include 4G module, WIFI module, Ethernet Ethernet module.
Power battery management equipment 30 includes battery pack data storage center 31, effective charge and discharge strategy machine learning center
32, failure charge and discharge strategy machine learning center 33, data cleansing classified service 34, failure prejudge center 35, charge and discharge strategy
Storage and distribution center 36, battery access server 37, battery cupboard access server 38, in which:
Battery pack data storage center 31 is for storing charge data, discharge data.
Effective charge and discharge strategy machine learning center 32, for learning and generating new effective charge and discharge strategy.
Failure charge and discharge strategy machine learning center 33, for learning and generating new failure charge and discharge strategy.
Data cleansing classified service 34, for choosing invalid data and filtering invalid data, first according to first condition
Condition is the condition of preset determining data invalid.
Failure prejudges center 35, for determining fail data, and according to fail data, prejudges what power battery broke down
Probability is sent by battery access server 37 if the probability that anticipation obtains is greater than predetermined probabilities and carries stopping electric discharge
Instruction is sent by battery cupboard access server 38 and carries the instruction for stopping charging.
Charge and discharge policy store and Distribution Center 36 for storing charge and discharge strategy and failure charge and discharge strategy, and will fill
It is sent to power battery charging cabinet 11 to electric strategy, and electric discharge strategy is sent to power battery 10, by failure charge and discharge strategy hair
Give failure anticipation center 35.
Battery access server 37 establishes communication connection using the first verifying access way and power battery 10, and battery cupboard connects
Enter server 38 and communication connection is established using the second verifying access way and power battery charging cabinet 11.In this way, working as power battery
10 or charging cabinet 20 send data when, by the way of verifying, avoid Illegal Battery data access, influence charge and discharge Policy model
Generation.
Invalid data can be the charge and discharge data of improper electronic battery, such as: original-pack electronic battery is modified privately
Cross running parameter etc..
First verification mode can send an access request, the access to battery access server 36 for power battery 10
Request carries the unique identification of the power battery 10, such as: sequence number, battery access server 36 are locally stored by inquiry
The mark of improper electronic battery determine whether the power battery is improper battery, if so, refuse battery access, if
It is not then to access.
Second verification mode can send the access request for carrying charge data, and battery cupboard access server 37 judges that this is filled
Electric data whether data invalid data, if so, refuse battery access, if it is not, then access, optionally, the second authentication
Formula can also be consistent with the first verification mode, and this programme does not do the specific implementation of the first verification mode and the second verification mode
Limitation.
It should be noted that power battery management equipment 30 can pass through software and or hardware realization.
Electrokinetic cell system provided in this embodiment, comprising: power battery, power battery charging cabinet and power battery
Discharge data and environmental data are sent to power battery management equipment by management equipment, power battery, and charging cabinet is by charge data
It is sent to power battery management equipment with environmental data, power battery management equipment is adopted according to charge and discharge data and environmental data
With the effective charging and recharging model being previously obtained, obtain the charge and discharge strategy of power battery, charge and discharge strategy include charging strategy and
Electric discharge strategy, charging and recharging model are obtained according to machine learning algorithm using multiple charge and discharge are data-optimized, then will charging
Strategy is sent to power battery, and electric discharge strategy is sent to the corresponding charging cabinet of power battery.In such manner, it is possible to power battery
It is remotely managed, while the charge and discharge strategy of motivation of adjustment battery in real time, improves the effective rate of utilization of power battery.
Fig. 2 is the flow diagram one of the management method of power battery provided in an embodiment of the present invention, the embodiment of the present invention
Provide a kind of management method of power battery, this method can be held by the device for arbitrarily executing the management method of power battery
Row, the device can pass through software and or hardware realization.In the present embodiment, which can integrate sets in power battery management
In standby.As shown in Fig. 2, the management method of power battery provided in an embodiment of the present invention includes the following steps:
The environmental data of S101, the charge and discharge data for obtaining power battery and local environment.
Charge and discharge data include charge data and discharge data.
Charge data includes charge frequency, charging times, charging ceiling voltage, charging minimum voltage, current electric quantity etc.,
The temperature of battery when can also include charging.Correspondingly, discharge data includes discharge frequency, discharge time, electric discharge highest electricity
Pressure, electric discharge minimum voltage, current electric quantity etc., the temperature of battery when can also include electric discharge.
The environmental data of local environment includes the environmental parameters such as environment temperature and height above sea level when power battery charge and discharge.
Charge data environmental data corresponding with its in this step can pass through the BMS module 101 of power battery, GPS
Module 102, the acquisition of temperature-measuring module 103 obtain.
Correspondingly, discharge data environmental data corresponding with its can pass through BMS module 101, the GPS module of power battery
102, the acquisition of temperature-measuring module 103 obtains, and is sent to power battery management equipment 12 by the communication module 110 of charging cabinet
Battery access server 126.
S102, power electric is obtained using the effective charging and recharging model being previously obtained according to charge and discharge data and environmental data
The charge and discharge strategy in pond.
Charge and discharge strategy includes charging strategy and electric discharge strategy, and charging and recharging model is according to machine learning algorithm using multiple
Charge and discharge are data-optimized to be obtained, and charging and recharging model can be by the generation at effective charge and discharge policy learning center 32.
Optionally, it if the charge and discharge data obtained are valid data, according to charge data and effective charge model, obtains dynamic
The charging strategy of power battery obtains the electric discharge strategy of power battery according to discharge data and effective discharging model, specifically, will
Charge data is input in effective charge model, obtains the charging strategy of power battery, and discharge data is input to effective electric discharge
In model, the electric discharge strategy of power battery is obtained.
Optionally, when power battery it is fully charged, start to charge when can all send current charge and discharge data, battery management is set
The standby time that can also record each charge and discharge data, according to machine learning algorithm, optimization obtains effective charging and recharging model.
Wherein, valid data can be the number that some normal range (NR)s more than charge and discharge data are rejected in charge and discharge data
According to.
The executing subject of this step can be charge and discharge policy store and the Distribution Center 36 of power battery management equipment 12.
S103, electric discharge strategy is sent to power battery, and charging strategy is sent to the corresponding charging cabinet of power battery.
It, can according to the rule in the electric discharge strategy after power battery receives the electric discharge strategy that power battery management equipment is sent
Complete electric discharge；It, can according to the rule in the charging strategy after charging cabinet receives the charging strategy that power battery management equipment is sent
Complete charging.
In one possible implementation, charging strategy can also be sent to power battery, power battery is not at this time
It is charged using charging cabinet, such as: when solar recharging may be implemented in power battery, directly charging strategy can be sent to dynamic
Power battery.
In one possible implementation, electric discharge strategy can also be sent to charging cabinet, charging cabinet will discharge plan again
It is slightly transmitted to power battery, such as: it, can be by electric discharge strategy when at once fully charged in power battery and charging cabinet physical connection
It is sent to charging cabinet, power battery is transmitted to by charging cabinet.
Optionally, the output of effective charge model can be the charge value after preset duration, charging voltage, charging current,
Then the charging strategy includes the charge value of preset duration, charging voltage, charging current, and charging cabinet can be according to the electricity of preset duration
Magnitude, charging voltage, charging current efficiently accomplish the charging of preset duration.
Optionally, the output of effective discharging model can be the maximum current threshold value and minimum electricity under default residual electric quantity
Threshold value is pressed, then the electric discharge strategy includes the maximum current threshold value and minimum voltage threshold under default residual electric quantity, power battery
Can according under default residual electric quantity maximum current threshold value and minimum voltage threshold efficiently accomplish the electric discharge of preset duration, make
It obtains the maximum current preset under residual electric quantity after preset duration and is no more than maximum current threshold value, minimum voltage is no more than minimum electric
Press threshold value.
The executing subject of this step can be charge and discharge policy store and the Distribution Center 36 of power battery management equipment 12.
Power battery method provided in this embodiment, by obtaining the charge and discharge data of power battery and the ring of local environment
Border data obtain filling for power battery using the effective charging and recharging model being previously obtained according to charge and discharge data and environmental data
Electric discharge strategy, charge and discharge strategy include charging strategy and electric discharge strategy, and charging and recharging model is according to machine learning algorithm using more
A charge and discharge are data-optimized to be obtained, and electric discharge strategy is then sent to power battery, and charging strategy is sent to power electric
The corresponding charging cabinet in pond.In such manner, it is possible to power battery is remotely managed, the charge and discharge strategy of real-time motivation of adjustment battery,
The safety in utilization of power battery is improved, while improving the effective rate of utilization of power battery.
Fig. 3 is the flow diagram two of the management method of power battery provided in an embodiment of the present invention, as shown in figure 3, should
Method further include:
S201, according to first condition, determine whether charge and discharge data and/or environmental data are invalid data.
First condition is the condition of preset determining data invalid.
Optionally, first condition can be more than default environment temperature threshold value for environment temperature, and height above sea level is more than default height above sea level threshold
Value, such as: the environment temperature in environmental data is higher than 100 DEG C, and height above sea level is higher than 9000 meters, due to the environment temperature and height above sea level
More than being more than corresponding threshold value, therefore, it is considered that specifically obtaining environmental data is invalid data, the then corresponding charge and discharge of environmental data
Electric data are also invalid data, then are not available effective charging and recharging model of this programme, would not also get charge and discharge strategy,
This programme is not particularly limited default environment temperature threshold value and default elevation threshold.
Optionally, first condition can also be more than default battery temperature threshold value for battery temperature, such as: battery temperature is more than
1000 DEG C, be more than default battery temperature threshold value, then it is assumed that charge and discharge data are also invalid data, and this programme is to default battery temperature
Threshold value is not particularly limited.
This step executing subject can be data cleansing classified service 34.
If S202, charge and discharge data and environmental data are not invalid data, according to second condition, charge and discharge number is determined
According to being for valid data or fail data.
Second condition includes the condition of the effective conditions and data failure of preset determining data.
Optionally, if the variation tendency of charge data meets default first trend, and the variation tendency of discharge data meets
Default second variation tendency, then the charge and discharge data are valid data, and be not belonging to valid data is then fail data.
Optionally, charge and discharge data are sent to battery management every preset duration in charge and discharge process by power battery
Equipment, can also it is fully charged, start to charge when charge and discharge data are sent to battery management unit, which can be with
The mark of the power battery is carried, such as: sequence number.
Battery management unit can store all charge and discharge data of the power battery, when the charging for getting this transmission
After data and discharge data, to the corresponding all charge datas of the power battery and it can be put according to the identifier lookup of power battery
Electric data, then multiple charge datas according to the pre-stored data and this evidence of making up the number, obtain the variation tendency of charge data, if
Meet first trend, then this charge and discharge data obtained is valid data, is fail data otherwise.
Optionally, first trend and second trend can be the form of upper limit frame, lower limit frame, if in bound frame,
For valid data；If being more than bound frame, for fail data.
The executing subject of this step can prejudge center 35 for failure.
The management method of power battery provided in an embodiment of the present invention, according to first condition, determine charge and discharge data and/or
Whether environmental data is invalid data, if charge and discharge data and environmental data are not invalid data, according to second condition, really
Determining charge and discharge data is for valid data or fail data.Valid data can be filtered out, avoids introducing fail data, influences to have
Imitate the generation of charging and recharging model.
Fig. 4 is the flow diagram three of the management method of power battery provided in an embodiment of the present invention, and the present embodiment is held
Row main body can prejudge center 35 for failure, as shown in figure 4, the method also includes:
If S301, charge and discharge data are fail data, using the failure charging and recharging model being previously obtained, power battery is predicted
The probability to break down in charge and discharge process.
Wherein, failure charging and recharging model includes failure charge model and failure discharging model, for according to machine learning algorithm
It is obtained using multiple failure charge and discharge are data-optimized.
Specifically, if charge data is fail data, according to charge data and failure charge model, prediction power battery exists
The probability to break down in charging process；If discharge data is fail data, according to discharge data and failure discharging model, prediction
The probability that power battery breaks down during discharge.
If the probability that S302, power battery break down during the charging process is greater than default first probability, stop carrying
The instruction only charged is sent to charging cabinet.
If the probability that S303, power battery break down during discharge is greater than default second probability, stop carrying
Only electric discharge is sent to power battery.
If probability of malfunction when charging is greater than default first probability, the instruction for stopping charging being carried and be sent to charging
Cabinet, so that charging cabinet stops giving the power battery charging.
If probability of malfunction when electric discharge is greater than default second probability, stopping electric discharge being carried and be sent to power battery, closed
Close the use of the power battery.
The management method of power battery provided in this embodiment, if charge and discharge data are fail data, using being previously obtained
Failure charging and recharging model, the probability that breaks down in charge and discharge process of prediction power battery, if power battery was charging
The probability to break down in journey is greater than default first probability, will carry the instruction for stopping charging and is sent to charging cabinet, if power
The probability that battery breaks down during discharge is greater than default second probability, will carry stopping electric discharge and is sent to power electric
Pond.The failure of power battery can be predicted, improve the safety in utilization of power battery.
Fig. 5 is the flow diagram four of the management method of power battery provided in an embodiment of the present invention, the execution of this implementation
Main body can be effective charge and discharge plan, machine learning center 32, as shown in figure 5, the method also includes:
S401, effectively charging initial model is obtained.
Effective charge and discharge initial model is obtained according to laboratory data, and laboratory data is in laboratory to power electric
The charge and discharge data that the charge-discharge performance in pond is tested.Existing effective charging initial model be all it is intrinsic, not
Optimization can be updated according to the charge and discharge data of the power battery acquired in real time, this programme is intended to using the charge and discharge acquired in real time
Electric data optimize effective charge and discharge initial model according to machine learning algorithm.It is described in detail and how to obtain in following scheme
Effective charging and recharging model.
S402, effective charge data set is obtained.
The multiple effective charge datas for obtaining multiple power batteries in advance, according to charge frequency, charging-cell temperature, charging
The dimensions such as maximum battery voltage, rechargeable battery minimum voltage divide multiple effective charge datas, then each dimension includes multiple
Data obtain effective charge data set, therefore effectively charging set includes multiple data of multiple dimensions.
Effective charge data set includes effective charge data of preset quantity.
Optionally, the quantity of the data of any dimension is equal to preset quantity；Alternatively, in preset duration, effective charge data
The quantitative proportion of data in set is greater than preset ratio.
S403, the initial model that effectively charges is optimized using effective charge data set according to machine learning algorithm, obtained
Effective charge model.
Optionally, according to machine learning algorithm, data-optimized using at least one dimension in valid data set has
Effect charging initial model, obtains effective charge model.
Optionally, the method also includes:
S404, effectively electric discharge initial model is obtained.
S405, effective discharge data set is obtained.
S406, effective electric discharge introductory die is optimized using effective discharge data set according to machine learning algorithm
Type obtains effective discharging model.
The realization process of S404-S406 and the realization process of S401-S403 are similar, and details are not described herein.
Optionally, the method also includes:
S501, failure charging initial model is obtained.
S502, failure charge data set is obtained.
S503, the failure charging introductory die is optimized using the failure charge data set according to machine learning algorithm
Type obtains the failure charge model.
The realization process of S501-S503 and the realization process of S401-S403, S404-S406 are similar, and difference, which is to fail, fills
Electric data acquisition system include at least one failure charge data, as soon as that is, exist fail a charge data, according to machine learning
Algorithm, using failure charge data set, optimization failure charging initial model, to obtain failure charge model.
Optionally, the method also includes:
S504, failure electric discharge initial model is obtained.
S505, failure discharge data set is obtained.
S506, the failure electric discharge introductory die is optimized using the failure discharge data set according to machine learning algorithm
Type obtains failure discharging model.
The realization process of S504-S506 and the realization process of S501-S503 are similar, and details are not described herein.
The management method of power battery provided in this embodiment obtains effectively charging initial model, obtains effectively charging number
According to set, according to machine learning algorithm, using effective charge data set, optimizes effectively charging initial model, acquisition and effectively fill
Electric model.According to the data-optimized effective charge and discharge initial model of effective charge and discharge, so as to filling for real-time motivation of adjustment battery
Electric discharge strategy, improves the effective rate of utilization of power battery；Similarly, according at the beginning of the data-optimized failure charge and discharge of considered repealed charge and discharge
Beginning new model improves the safety in utilization of power battery so that failure prejudges more accurate.
Fig. 6 is the structural schematic diagram one of the managing device of power battery provided in an embodiment of the present invention, the power battery
Management can be realized by way of software, hardware or software and hardware combining.As shown in fig. 6, the managing device of the power battery
60 include: to obtain module 61, sending module 62, in which:
Module 61 is obtained, for obtaining the charge and discharge data of the power battery and the environmental data of local environment；
Institute is obtained using the effective charging and recharging model being previously obtained according to the charge and discharge data and the environmental data
The charge and discharge strategy of power battery is stated, the charge and discharge strategy includes charging strategy and electric discharge strategy, and the charging and recharging model is
It is obtained according to machine learning algorithm using multiple charge and discharge are data-optimized；
Charging strategy for the electric discharge strategy to be sent to the power battery, and is sent to institute by sending module 62
State the corresponding charging cabinet of power battery.
The managing device of power battery provided in an embodiment of the present invention can execute above method embodiment, realize former
Reason is similar with technical effect, and details are not described herein.
On the basis of Fig. 6 embodiment, Fig. 7 is that the structure of the managing device of power battery provided in an embodiment of the present invention is shown
It is intended to two, as shown in fig. 7, the device further include: determining module 63, processing module 64.
Optionally, the acquisition module 61, is also used to:
If the charge and discharge data are valid data, according to the charge data and effective charge model, obtain described dynamic
The charging strategy of power battery；
According to the discharge data and effective discharging model, the electric discharge strategy of the power battery is obtained；
Wherein, effective charging and recharging model includes effective charge model and effective discharging model.
Determining module 63, for according to first condition, determine the charge and discharge data and/or the environmental data whether be
Invalid data, the first condition are the condition of preset determining data invalid；
If the charge and discharge data and the environmental data are not invalid data, according to second condition, determine described in
Charge and discharge data are for valid data or fail data, and the second condition includes the effective condition sum number of preset determining data
According to the condition of failure.
Processing module 64, if being fail data for the charge and discharge data, using the failure charge and discharge mould being previously obtained
Type predicts the probability that the power battery breaks down in charge and discharge process；
The sending module 62 is preset if being also used to the probability that the power battery breaks down during the charging process and being greater than
First probability will carry the instruction for stopping charging and be sent to the charging cabinet；
If the probability that the power battery breaks down during discharge is greater than default second probability, stopping will be carried
Electric discharge is sent to the power battery.
Optionally, the acquisition module 61, is also used to:
Obtain effectively charging initial model；
Obtain effective charge data set；
The processing module 64 is also used to, using effective charge data set, optimize institute according to machine learning algorithm
Effectively charging initial model is stated, effective charge model is obtained.
Optionally, the acquisition module 61, is also used to:
Obtain effectively electric discharge initial model；
Obtain effective discharge data set；
The processing module 64 is also used to, using effective discharge data set, optimize institute according to machine learning algorithm
Effectively electric discharge initial model is stated, effective discharging model is obtained.
Optionally, the acquisition module 61, is also used to:
Obtain failure charging initial model；
Obtain failure charge data set；
The processing module 64 is also used to, using the failure charge data set, optimize institute according to machine learning algorithm
Failure charging initial model is stated, the failure charge model is obtained.
Optionally, the acquisition module 61, is also used to:
Obtain failure electric discharge initial model；
Obtain failure discharge data set；
The processing module 64 is also used to, using the failure discharge data set, optimize institute according to machine learning algorithm
Failure electric discharge initial model is stated, the failure discharging model is obtained.
The managing device of power battery provided in an embodiment of the present invention can execute above method embodiment, realize former
Reason is similar with technical effect, and details are not described herein.
Fig. 8 is the hardware structural diagram of power battery management equipment provided in an embodiment of the present invention.As shown in figure 8, this
The power battery management equipment 80 of embodiment includes: processor 81, memory 82, transmitter 83, receiver 84.
The processor 81 runs the computer program to execute:
Obtain the charge and discharge data of the power battery and the environmental data of local environment；
Institute is obtained using the effective charging and recharging model being previously obtained according to the charge and discharge data and the environmental data
The charge and discharge strategy of power battery is stated, the charge and discharge strategy includes charging strategy and electric discharge strategy, and the charging and recharging model is
It is obtained according to machine learning algorithm using multiple charge and discharge are data-optimized；
The electric discharge strategy is sent to the power battery, and it is corresponding that charging strategy is sent to the power battery
Charging cabinet.
The embodiment of the present invention also provides a kind of computer readable storage medium, stores in the computer readable storage medium
There are computer executed instructions, when processor executes the computer executed instructions, realizes power battery management as described above
Method.
In several embodiments provided by the present invention, it should be understood that disclosed device and method can pass through it
Its mode is realized.For example, apparatus embodiments described above are merely indicative, for example, the division of the module, only
Only a kind of logical function partition, there may be another division manner in actual implementation, for example, multiple modules can combine or
It is desirably integrated into another system, or some features can be ignored or not executed.Another point, it is shown or discussed it is mutual it
Between coupling, direct-coupling or communication connection can be through some interfaces, the INDIRECT COUPLING or communication link of device or module
It connects, can be electrical property, mechanical or other forms.
Those of ordinary skill in the art will appreciate that: realize that all or part of the steps of above-mentioned each method embodiment can lead to
The relevant hardware of program instruction is crossed to complete.Program above-mentioned can be stored in a computer readable storage medium.The journey
When being executed, execution includes the steps that above-mentioned each method embodiment to sequence；And storage medium above-mentioned include: ROM, RAM, magnetic disk or
The various media that can store program code such as person's CD.
Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention., rather than its limitations；To the greatest extent
Pipe present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that: its according to
So be possible to modify the technical solutions described in the foregoing embodiments, or to some or all of the technical features into
Row equivalent replacement；And these are modified or replaceed, various embodiments of the present invention technology that it does not separate the essence of the corresponding technical solution
The range of scheme.
Claims (10)
1. a kind of management method of power battery, which is characterized in that be applied to power battery management equipment, comprising:
Obtain the charge and discharge data of the power battery and the environmental data of local environment；
It is obtained described dynamic according to the charge and discharge data and the environmental data using the effective charging and recharging model being previously obtained
The charge and discharge strategy of power battery, the charge and discharge strategy includes that charging strategy and electric discharge are tactful, according to the charging and recharging model
Machine learning algorithm is obtained using multiple charge and discharge are data-optimized；
The electric discharge strategy is sent to the power battery, and charging strategy is sent to the corresponding charging of the power battery
Cabinet.
2. the method according to claim 1, wherein the charge and discharge data include charge data and discharge count
According to, it is described according to the charge and discharge data and the environmental data, using the effective charging and recharging model being previously obtained, described in acquisition
The charge and discharge strategy of power battery, comprising:
If the charge and discharge data obtain the power electric according to the charge data and effective charge model for valid data
The charging strategy in pond；
According to the discharge data and effective discharging model, the electric discharge strategy of the power battery is obtained；
Wherein, effective charging and recharging model includes effective charge model and effective discharging model.
3. according to the method described in claim 2, it is characterized in that, the method also includes:
According to first condition, determine whether the charge and discharge data and/or the environmental data are invalid data, described first
Part is the condition of preset determining data invalid；
If the charge and discharge data and the environmental data are not invalid data, according to second condition, the charge and discharge is determined
Electric data are for valid data or fail data, and the second condition includes that the effective conditions and data of preset determining data loses
The condition of effect.
4. according to the method described in claim 3, it is characterized in that, the method also includes:
If the charge and discharge data predict the power battery using the failure charging and recharging model being previously obtained for fail data
The probability to break down in charge and discharge process；
If the probability that the power battery breaks down during the charging process is greater than default first probability, stopping charging being carried
Instruction be sent to the charging cabinet；
If the probability that the power battery breaks down during discharge is greater than default second probability, stopping electric discharge being carried
It is sent to the power battery；Wherein, the failure charging and recharging model includes failure charge model and failure discharging model.
5. according to the method described in claim 2, it is characterized in that, the method also includes:
Obtain effectively charging initial model；
Obtain effective charge data set；
Effective charging initial model is optimized using effective charge data set according to machine learning algorithm, obtains institute
State effective charge model.
6. according to the method described in claim 2, it is characterized in that, the method also includes:
Obtain effectively electric discharge initial model；
Obtain effective discharge data set；
Effective electric discharge initial model is optimized using effective discharge data set according to machine learning algorithm, obtains institute
State effective discharging model.
7. according to the method described in claim 2, it is characterized in that, the method also includes:
Obtain failure charging initial model；
Obtain failure charge data set；
Optimize the failure charging initial model using the failure charge data set according to machine learning algorithm, obtain institute
State failure charge model.
8. according to the method described in claim 2, it is characterized in that, the method also includes:
Obtain failure electric discharge initial model；
Obtain failure discharge data set；
Optimize the failure electric discharge initial model using the failure discharge data set according to machine learning algorithm, obtain institute
State failure discharging model.
9. a kind of power battery management device characterized by comprising
Module is obtained, for obtaining the charge and discharge data of the power battery and the environmental data of local environment；
It is obtained described dynamic according to the charge and discharge data and the environmental data using the effective charging and recharging model being previously obtained
The charge and discharge strategy of power battery, the charge and discharge strategy includes that charging strategy and electric discharge are tactful, according to the charging and recharging model
Machine learning algorithm is obtained using multiple charge and discharge are data-optimized；
Charging strategy for the electric discharge strategy to be sent to the power battery, and is sent to the power by sending module
The corresponding charging cabinet of battery.
10. a kind of power battery management system characterized by comprising power battery, power battery charging cabinet, and realize
The power battery management equipment of any one of claim 1 to 8 the method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811565326.3A CN109888414B (en) | 2018-12-20 | 2018-12-20 | Management method, device and system of power battery |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811565326.3A CN109888414B (en) | 2018-12-20 | 2018-12-20 | Management method, device and system of power battery |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109888414A true CN109888414A (en) | 2019-06-14 |
| CN109888414B CN109888414B (en) | 2021-11-26 |
Family
ID=66925156
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811565326.3A Active CN109888414B (en) | 2018-12-20 | 2018-12-20 | Management method, device and system of power battery |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109888414B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111029669A (en) * | 2019-12-23 | 2020-04-17 | 联想(北京)有限公司 | Control method of intelligent battery, intelligent battery and server |
| CN111833534A (en) * | 2019-08-02 | 2020-10-27 | 北京骑胜科技有限公司 | Battery charging method, charging device, electronic equipment and storage medium |
| CN113829954A (en) * | 2020-06-23 | 2021-12-24 | 博世汽车服务技术(苏州)有限公司 | Charging and discharging control device and method for power battery and charging and discharging equipment |
| CN114633655A (en) * | 2020-12-15 | 2022-06-17 | 北京骑胜科技有限公司 | Charging method of shared vehicle, battery management server and system |
| CN115498750A (en) * | 2022-11-21 | 2022-12-20 | 深圳市恒生智能科技有限公司 | Intelligent charging management method and system for photovoltaic power supply |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090309416A1 (en) * | 2008-06-12 | 2009-12-17 | Bose Anima B | Active electrical power flow control system for optimization of power delivery in electric hybrid vehicles |
| CN101621130A (en) * | 2009-08-07 | 2010-01-06 | 北京新能瑞泰电子技术有限责任公司 | Storage battery management system and method as well as storage battery charging management system and method |
| CN106505260A (en) * | 2016-11-07 | 2017-03-15 | 华为技术有限公司 | A kind of method and device for repairing battery |
| CN107121642A (en) * | 2017-06-28 | 2017-09-01 | 北京新能源汽车股份有限公司 | SOH coefficient adjusting method and device for power battery |
| CN108334900A (en) * | 2018-01-29 | 2018-07-27 | 上海电气分布式能源科技有限公司 | Generation method and system, the sorting technique and system of the disaggregated model of power battery |
| CN108973979A (en) * | 2018-07-18 | 2018-12-11 | 乾碳国际公司 | The mixed predictive power control system scheme of motor-car |
| WO2019162749A1 (en) * | 2017-12-07 | 2019-08-29 | Yazami Ip Pte. Ltd. | Method and system for online assessing state of health of a battery |
-
2018
- 2018-12-20 CN CN201811565326.3A patent/CN109888414B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090309416A1 (en) * | 2008-06-12 | 2009-12-17 | Bose Anima B | Active electrical power flow control system for optimization of power delivery in electric hybrid vehicles |
| CN101621130A (en) * | 2009-08-07 | 2010-01-06 | 北京新能瑞泰电子技术有限责任公司 | Storage battery management system and method as well as storage battery charging management system and method |
| CN106505260A (en) * | 2016-11-07 | 2017-03-15 | 华为技术有限公司 | A kind of method and device for repairing battery |
| CN107121642A (en) * | 2017-06-28 | 2017-09-01 | 北京新能源汽车股份有限公司 | SOH coefficient adjusting method and device for power battery |
| WO2019162749A1 (en) * | 2017-12-07 | 2019-08-29 | Yazami Ip Pte. Ltd. | Method and system for online assessing state of health of a battery |
| CN108334900A (en) * | 2018-01-29 | 2018-07-27 | 上海电气分布式能源科技有限公司 | Generation method and system, the sorting technique and system of the disaggregated model of power battery |
| CN108973979A (en) * | 2018-07-18 | 2018-12-11 | 乾碳国际公司 | The mixed predictive power control system scheme of motor-car |
Non-Patent Citations (1)
| Title |
|---|
| 熊瑞: "《中国博士学位论文全文数据库工程科技II辑》", 15 April 2015 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111833534A (en) * | 2019-08-02 | 2020-10-27 | 北京骑胜科技有限公司 | Battery charging method, charging device, electronic equipment and storage medium |
| CN111029669A (en) * | 2019-12-23 | 2020-04-17 | 联想(北京)有限公司 | Control method of intelligent battery, intelligent battery and server |
| CN111029669B (en) * | 2019-12-23 | 2021-11-16 | 联想(北京)有限公司 | Control method of intelligent battery, intelligent battery and server |
| CN113829954A (en) * | 2020-06-23 | 2021-12-24 | 博世汽车服务技术(苏州)有限公司 | Charging and discharging control device and method for power battery and charging and discharging equipment |
| CN114633655A (en) * | 2020-12-15 | 2022-06-17 | 北京骑胜科技有限公司 | Charging method of shared vehicle, battery management server and system |
| CN114633655B (en) * | 2020-12-15 | 2024-03-29 | 北京骑胜科技有限公司 | Charging method, battery management server and system for shared vehicle |
| CN115498750A (en) * | 2022-11-21 | 2022-12-20 | 深圳市恒生智能科技有限公司 | Intelligent charging management method and system for photovoltaic power supply |
| CN115498750B (en) * | 2022-11-21 | 2023-01-31 | 深圳市恒生智能科技有限公司 | Intelligent charging management method and system for photovoltaic power supply |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109888414B (en) | 2021-11-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109888414A (en) | Management method, the device and system of power battery | |
| US11075528B2 (en) | Server for charge-discharge system, charge-discharge system, and server control method | |
| CN109501630A (en) | A kind of electric car charging scheme real-time recommendation method and system | |
| CN109299833A (en) | Fast charge station energy storage optimization system and its working method based on block chain technology | |
| JP7111078B2 (en) | electric vehicle | |
| US12046905B2 (en) | Multi-technology grid regulation service | |
| CN108399578B (en) | Electrical changing station service ability predictor method and device | |
| CN102879747B (en) | Battery information sensing system and method | |
| CN109813982A (en) | Charging pile down Monitor Unit method, apparatus and charging pile manage platform, storage medium | |
| KR20180091976A (en) | Method and system of mobility-aware vehicle-to-grid (V2G) control | |
| CN105955152A (en) | Charging pile monitoring and management system | |
| CN104124724A (en) | Charging control apparatus and method thereof | |
| US10935948B2 (en) | System and method for managing interactions between a plurality of devices | |
| CN108116251A (en) | A kind of electric vehicle reservation timing charging system and method | |
| CN106740214A (en) | A kind of electric automobile charge and discharge terminal and its control method and device | |
| CN110015162A (en) | Cell health state detection method, device and system and storage medium | |
| CN113169383A (en) | Battery charging system, charging device, information processing device, battery charging method, program, and storage medium | |
| CN107351713A (en) | The control method and device of charging pile | |
| CN114186867A (en) | Ordered charging and discharging control system applied to electric vehicle | |
| CN114801837A (en) | Server, power management system, and energy management method | |
| CN106602681A (en) | Charging system and control method thereof | |
| CN106384436A (en) | Intelligent charging system and intelligent charging method | |
| CN103699088A (en) | Base station oil engine dispatching management system and method on basis of power environment monitoring | |
| CN205853914U (en) | A kind of integral type DC charging motor | |
| CN110323814A (en) | Electric car charge control method and device based on energy-storage battery |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |