CN107831441A - Forecasting Methodology, forecasting system and a kind of charging device of lithium cell charging electric current - Google Patents
Forecasting Methodology, forecasting system and a kind of charging device of lithium cell charging electric current Download PDFInfo
- Publication number
- CN107831441A CN107831441A CN201710850730.4A CN201710850730A CN107831441A CN 107831441 A CN107831441 A CN 107831441A CN 201710850730 A CN201710850730 A CN 201710850730A CN 107831441 A CN107831441 A CN 107831441A
- Authority
- CN
- China
- Prior art keywords
- battery
- charge
- charging
- charge cycle
- represent
- 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
- 238000007600 charging Methods 0.000 title claims abstract description 243
- 238000000034 method Methods 0.000 title claims abstract description 66
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 49
- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 49
- 230000036760 body temperature Effects 0.000 claims abstract description 85
- 238000005259 measurement Methods 0.000 claims abstract description 60
- 230000008569 process Effects 0.000 claims description 17
- 230000005611 electricity Effects 0.000 claims description 15
- 230000007246 mechanism Effects 0.000 claims description 13
- 230000008859 change Effects 0.000 claims description 11
- 230000004913 activation Effects 0.000 claims description 6
- 238000007599 discharging Methods 0.000 claims description 6
- 238000012546 transfer Methods 0.000 claims description 6
- 238000010276 construction Methods 0.000 claims description 5
- 238000012544 monitoring process Methods 0.000 claims description 5
- 230000000737 periodic effect Effects 0.000 claims description 5
- 238000005457 optimization Methods 0.000 description 8
- 230000032683 aging Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 239000002253 acid Substances 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 229910017052 cobalt Inorganic materials 0.000 description 1
- 239000010941 cobalt Substances 0.000 description 1
- GUTLYIVDDKVIGB-UHFFFAOYSA-N cobalt atom Chemical compound [Co] GUTLYIVDDKVIGB-UHFFFAOYSA-N 0.000 description 1
- 238000010281 constant-current constant-voltage charging Methods 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000004540 process dynamic Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
-
- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/44—Methods for charging or discharging
- H01M10/443—Methods for charging or discharging in response to temperature
-
- H02J7/0091—
-
- 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
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Manufacturing & Machinery (AREA)
- Chemical & Material Sciences (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Electrochemistry (AREA)
- General Chemical & Material Sciences (AREA)
- Secondary Cells (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The present invention, which discloses a kind of Forecasting Methodology of lithium cell charging electric current, forecasting system and a kind of charging device, Forecasting Methodology, to be included：It is minimum most short for target with the charging interval with battery capacity attenuation, construct charge target function；The battery for obtaining k-th of charge cycle surveys the actual measurement environment temperature of body temperature and battery Service Environment；According to actual measurement body temperature and actual measurement environment temperature, prediction+1 charge cycle of kth between kth+p charge cycles in each charge cycle battery prediction body temperature, the prediction step of p expression model predictive control methods；According to charge target function and each prediction body temperature, the optimal charging current of minimum+1 charge cycle of kth of charge target function is made using model predictive control method prediction.The optimal charging current predicted using the present invention is charged to lithium battery, can shorten the charging interval, improves charging rate, and can also reduce battery capacity attenuation, so as to extend the service life of battery.
Description
Technical field
The present invention relates to battery boosting technology field, more particularly to a kind of Forecasting Methodology of lithium cell charging electric current, pre-
Examining system and a kind of charging device.
Background technology
In throughout the year, variation of ambient temperature is obvious, and the environment temperature difference of different geographical is larger, environment temperature
Change can cause the change of battery temperature, and the aging rate of battery at different temperatures is different.Existing lithium battery
Charging method it is widely used be constant-current constant-voltage charging method, do not account for the shadow of battery temperature and rate of charge to battery life
Ring.If charging current is small, charging interval length, charging rate is slow, if wanting to improve charging rate, it is necessary to increase charging current,
Large current charge is excessive by the polarizing voltage for causing battery, causes the temperature of battery to raise, the increase of battery capacity attenuation, battery
Aging rate accelerate, shorten the service life of battery compared with.
Therefore, how to optimize the charging current of lithium battery, while reach and improve charging rate, extend battery
Purpose, turn into the technical problem of those skilled in the art's urgent need to resolve.
The content of the invention
It is an object of the invention to provide a kind of Forecasting Methodology of lithium cell charging electric current, forecasting system and a kind of charging dress
Put, optimize the charging current of lithium battery, while the purpose for reaching and improving charging rate, extend battery.
To achieve the above object, the invention provides following scheme：
A kind of Forecasting Methodology of lithium cell charging electric current, the Forecasting Methodology include：
It is minimum most short for target with the charging interval with battery capacity attenuation, construct charge target function；
Obtain the actual measurement body temperature of k-th charge cycle battery and k-th of charge cycle battery Service Environment
Survey environment temperature；
According to the actual measurement body temperature and the actual measurement environment temperature, prediction+1 charge cycle of kth fills for+p to kth
Between the electric cycle in each charge cycle battery prediction body temperature, wherein, p represent model predictive control method prediction step
It is long；
According to the charge target function and each prediction body temperature, made using model predictive control method prediction
The optimal charging current of minimum+1 charge cycle of kth of the charge target function.
Optionally, it is described minimum most short for target with the charging interval with battery capacity attenuation, charge target function is constructed,
Specifically include：
According to battery capacity decay mechanism model：It is determined that decayed with battery capacity
The first object function of minimum target is measured, the first object function is：
Wherein, f1Represent first object function, QlossBattery capacity attenuation is represented, B represents coefficient before index, and e represents natural constant, E
Activation energy is represented, a represents battery multiplying power corrected parameter, C_RateThe absolute value of battery charging and discharging current ratio is represented, R represents preferable
Gas constant, T represent the Kelvin of battery, AhThe ampere-hour number of disengaging battery is represented, z represents power law parameter, IkRepresent the
The charging current of k charge cycle, TkThe Kelvin of k-th of charge cycle battery is represented, N represents charge cycle sum,
Qloss,kRepresent the battery capacity attenuation of k-th of charge cycle, Qloss,0=0；
According in battery charging process, battery charge state change mechanism model：It is determined that with
Charging interval most short the second object function for target, second object function are：Its
In, f2The second object function is represented, SOC (k) represents the battery charge state of k-th of charge cycle, and Cap represents the specified of battery
Capacity, IjRepresent the charging current of j-th of charge cycle；
According to the first object function and second object function, the charge target function, the charging are determined
Object function is：Wherein, fkRepresent k-th of charging
The charge target function in cycle, a represent compensating factor, K=1,2 ... ..., N.
Optionally, it is described according to the actual measurement body temperature and the actual measurement environment temperature, predict+1 charge cycle of kth
To between kth+p charge cycles in each charge cycle battery prediction body temperature, specifically include：
To in charging process, the relational expression progress of the body temperature of charging current, the environment temperature of battery and battery is discrete
Change is handled, and obtains battery temperature forecast model, wherein, the relational expression is：
The battery temperature forecast model is：m
Represent battery quality, CpThermal capacitance is represented, T represents battery temperature, and t represents the time, and I represents charging current, VOCRepresent opening for battery
Road voltage, VTThe terminal voltage of battery is represented, h represents convective heat-transfer coefficient, and A represents battery surface product, TambRepresent the environment of battery
Temperature, T 'k+1Represent the prediction body temperature of battery in+1 charge cycle of kth, TkRepresent battery in k-th of charge cycle
Survey body temperature, IkThe charging current of k-th of charge cycle is represented, Δ t represents charge cycle, VOC.kRepresent k-th of charging week
The open-circuit voltage of phase battery, VT, kThe terminal voltage of k-th of charge cycle battery is represented,Represent k-th of charge cycle electricity
The Entropy Changes in pond, TAmb, kRepresent the actual measurement environment temperature of k-th of charge cycle battery；
According to the actual measurement body temperature of k-th of charge cycle battery, k-th of charge cycle battery Service Environment
Actual measurement environment temperature and k-th of charge cycle charging current, using the battery temperature forecast model prediction kth+1 fill
The electric cycle between kth+p charge cycles in each charge cycle battery prediction body temperature, wherein, p represents model prediction
The prediction step of control method.
Optionally, it is described according to the charge target function and each prediction body temperature, using model prediction control
Method prediction processed makes the optimal charging current of minimum+1 charge cycle of kth of the charge target function, specifically includes：
Maximum charging current is determined according to the battery charge state of k-th of charge cycle；
Described+1 charge cycle of kth is obtained to the charging electricity of each charge cycle between the kth+p charge cycles
The predetermined number n of streamk+i, 1≤i≤p；
According to the maximum charging current and the predetermined number nk+i, it is determined that each preset in each charge cycle is filled
Electric current；
According to each preset charged electric current generation n in each charge cyclek+1×nk+2×…×nk+pIndividual difference
Charging current sequence；
According to the charge target function, each prediction body temperature and each charging current sequence, it is determined that
Optimal charge sequence, the optimal charge sequence are the charging current sequence for making the functional value of the charge target function minimum,
First element in the optimal charge sequence is the optimal charging current of+1 charge cycle of kth.
A kind of forecasting system of lithium cell charging electric current, the forecasting system include：
Objective function module, for minimum most short for target with the charging interval with battery capacity attenuation, construction fills
Electric object function；
Temperature acquisition module, actual measurement body temperature and k-th of charging week for k-th of charge cycle battery of acquisition
The actual measurement environment temperature of phase battery Service Environment；
Temperature prediction module, for being filled according to the actual measurement body temperature and the actual measurement environment temperature, prediction kth+1
The electric cycle between kth+p charge cycles in each charge cycle battery prediction body temperature, wherein, p represents model prediction
The prediction step of control method；
Charging current prediction module, for according to the charge target function and each prediction body temperature, using
Model predictive control method prediction makes the optimal charging current of minimum+1 charge cycle of kth of the charge target function.
Optionally, the objective function module specifically includes：
First object function unit, for according to battery capacity decay mechanism model：
It is determined that with the first object function of the minimum target of battery capacity attenuation, the first object function is：
Wherein, f1Represent first object function, QlossRepresent that battery holds
Attenuation is measured, B represents coefficient before index, and e represents natural constant, and E represents activation energy, and a represents battery multiplying power corrected parameter, C_Rate
The absolute value of battery charging and discharging current ratio is represented, R represents ideal gas constant, and T represents the Kelvin of battery, AhRepresent into
Go out the ampere-hour number of battery, z represents power law parameter, IkRepresent the charging current of k-th of charge cycle, TkRepresent k-th of charging
The Kelvin of periodic battery, N represent charge cycle sum, Qloss,kThe battery capacity attenuation of k-th of charge cycle is represented,
Qloss,0=0；
Second object function unit, for according in battery charging process, battery charge state change mechanism model：
It is determined that with charging interval most short the second object function for target, second target
Function is：Wherein, f2The second object function is represented, SOC (k) represents k-th of charging week
The battery charge state of phase, Cap represent the rated capacity of battery, IjRepresent the charging current of j-th of charge cycle；
Charge function unit, for according to the first object function and second object function, determining the charging
Object function, the charge target function are：Its
In, fkThe charge target function of k-th of charge cycle is represented, a represents compensating factor, K=1,2 ... ..., N.
Optionally, the temperature prediction module specifically includes：
Forecast model determining unit, in charging process, the body of charging current, the environment temperature of battery and battery
The relational expression of temperature carries out sliding-model control, obtains battery temperature forecast model, wherein, the relational expression is：
The battery temperature forecast model is：m
Represent battery quality, CpThermal capacitance is represented, T represents battery temperature, and t represents the time, and I represents charging current, VOCRepresent opening for battery
Road voltage, VTThe terminal voltage of battery is represented, h represents convective heat-transfer coefficient, and A represents battery surface product, TambRepresent the environment of battery
Temperature, T 'k+1Represent the prediction body temperature of battery in+1 charge cycle of kth, TkRepresent battery in k-th of charge cycle
Survey body temperature, IkThe charging current of k-th of charge cycle is represented, Δ t represents charge cycle, VOC.kRepresent k-th of charging week
The open-circuit voltage of phase battery, VT, kThe terminal voltage of k-th of charge cycle battery is represented,Represent k-th of charge cycle electricity
The Entropy Changes in pond, TAmb, kRepresent the actual measurement environment temperature of k-th of charge cycle battery；
Body temperature predicting unit, for the actual measurement body temperature according to k-th of charge cycle battery, the kth
The actual measurement environment temperature of individual charge cycle battery Service Environment and the charging current of k-th of charge cycle, using the battery temperature
Spend forecast model prediction+1 charge cycle of kth between the individual charge cycles of kth+p in each charge cycle battery prediction body
Temperature, wherein, p represents the prediction step of model predictive control method.
Optionally, the charging current prediction module specifically includes：
Maximum current determining unit, for determining maximum charging current according to the battery charge state of k-th of charge cycle；
Electric current number acquiring unit, for obtaining described+1 charge cycle of kth between the kth+p charge cycles
The predetermined number n of the charging current of each charge cyclek+i, 1≤i≤p；
Predetermined current determining unit, for according to the maximum charging current and the predetermined number nk+i, it is determined that each filling
Each preset charged electric current in the electric cycle；
Current sequence generation unit, for being generated according to each preset charged electric current in each charge cycle
nk+1×nk+2×…×nk+pIndividual different charging current sequence；
Optimal current determining unit, for according to the charge target function, each prediction body temperature and each
The charging current sequence, determines optimal charge sequence, and the optimal charge sequence is to make the function of the charge target function
It is worth minimum charging current sequence, first element in the optimal charge sequence is optimal the filling of+1 charge cycle of kth
Electric current.
A kind of charging device, the charging device are connected with lithium battery, for the lithium cell charging, the charging dress
Put including：First temperature sensor, second temperature sensor, charge controller, charger and described forecasting system, wherein,
First temperature sensor is used for the actual measurement environment temperature for gathering the lithium battery Service Environment；
The second temperature sensor is connected with the lithium battery, for gathering the actual measurement body temperature of the lithium battery；
The forecasting system is connected with first temperature sensor and the second temperature sensor respectively, for electricity
Tankage attenuation minimum most short with the charging interval is target, constructs charge target function, and according to k-th of charge cycle battery
Actual measurement body temperature and k-th of charge cycle battery Service Environment actual measurement environment temperature, using Model Predictive Control
Method prediction makes the optimal charging current of minimum+1 charge cycle of kth of charge target function；
The charger is connected with the lithium battery, for monitoring the battery charge state of the lithium battery, be additionally operable to
The lithium cell charging；
The charge controller is connected with the forecasting system and the charger respectively, and the charge controller is used for root
According to the battery charge state of the lithium battery of charger monitoring, judge whether present battery state-of-charge reaches default
Battery charge state, obtain the first judged result；
When the first judged result represent present battery state-of-charge be not up to default battery charge state, then will be described pre-
The signal for the optimal charging current that examining system determines is sent to the charger, and the charger is according to the optimal charging current
Signal give the lithium cell charging；
When the first judged result represent present battery state-of-charge reach default battery charge state, then send stopping and fill
Electricity order, the charger stop to the lithium cell charging according to the stopping charge command.
According to specific embodiment provided by the invention, the invention discloses following technique effect：
The present invention is according to the actual measurement body temperature of current time battery and the actual measurement environment of current time battery Service Environment
The prediction body temperature of the next charge cycle battery of temperature prediction, then according to prediction body temperature and the charge target letter of construction
Number, the optimal charging current of next charge cycle is determined using model predictive control method, make charge target functional value minimum.By
The attenuation of charging rate and battery capacity can be taken into account in charge target function, therefore, optimal is filled using what the present invention predicted
Electric current charges to lithium battery, can shorten the charging interval, improves charging rate, and can also reduce battery capacity decay
Amount, so as to extend the service life of battery.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to institute in embodiment
The accompanying drawing needed to use is briefly described, it should be apparent that, drawings in the following description are only some implementations of the present invention
Example, for those of ordinary skill in the art, on the premise of not paying creative work, can also be obtained according to these accompanying drawings
Obtain other accompanying drawings.
Fig. 1 is the flow chart for the Forecasting Methodology that the embodiment of the present invention 1 provides；
Fig. 2 is the flow chart of step 14 in the Forecasting Methodology that the embodiment of the present invention 1 provides；
Fig. 3 is the structured flowchart for the forecasting system that the embodiment of the present invention 2 provides；
Fig. 4 is the structured flowchart of charging current prediction module in the forecasting system that the embodiment of the present invention 2 provides；
Fig. 5 is the structured flowchart for the charging device that the embodiment of the present invention 3 provides；
Fig. 6 is the flow chart for the charging device charging that the embodiment of the present invention 3 provides.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation describes, it is clear that described embodiment is only part of the embodiment of the present invention, rather than whole embodiments.It is based on
Embodiment in the present invention, those of ordinary skill in the art are obtained every other under the premise of creative work is not made
Embodiment, belong to the scope of protection of the invention.
It is an object of the invention to provide a kind of Forecasting Methodology of lithium cell charging electric current, forecasting system and a kind of charging dress
Put, optimize the charging current of lithium battery, while the purpose for reaching and improving charging rate, extend battery.
In order to facilitate the understanding of the purposes, features and advantages of the present invention, it is below in conjunction with the accompanying drawings and specific real
Applying mode, the present invention is further detailed explanation.
Embodiment 1：
As shown in figure 1, a kind of Forecasting Methodology of lithium cell charging electric current includes：
Step 11：It is minimum most short for target with the charging interval with battery capacity attenuation, construct charge target function；
Step 12：The actual measurement body temperature and k-th of charge cycle battery for obtaining k-th charge cycle battery are on active service
The actual measurement environment temperature of environment；
Step 13：According to the actual measurement body temperature and actual measurement environment temperature, prediction+1 charge cycle of kth to the
Between k+p charge cycle in each charge cycle battery prediction body temperature, wherein, p represents model predictive control method
Prediction step；
Step 14：According to the charge target function and each prediction body temperature, using Model Predictive Control side
Method prediction makes the optimal charging current of minimum+1 charge cycle of kth of the charge target function.
Wherein, step 11：It is minimum most short for target with the charging interval with battery capacity attenuation, construct charge target letter
Number, is specifically included：
Step 111：According to battery capacity decay mechanism model：It is determined that with battery
The first object function of the minimum target of capacity attenuation amount, the first object function are：
Wherein, f1Represent first object function, QlossRepresent that battery holds
Attenuation is measured, B represents coefficient before index, and e represents natural constant, and E represents activation energy, and a represents battery multiplying power corrected parameter, C_Rate
The absolute value of battery charging and discharging current ratio is represented, R represents ideal gas constant, and T represents the Kelvin of battery, AhRepresent into
Go out the ampere-hour number of battery, z represents power law parameter, IkRepresent the charging current of k-th of charge cycle, TkRepresent k-th of charging
The Kelvin of periodic battery, N represent charge cycle sum, Qloss,kThe battery capacity attenuation of k-th of charge cycle is represented,
Qloss,0=0.
Specifically, first to battery capacity decay mechanism model：Carry out discretization
After obtain：Wherein Qloss,kAnd Qloss,k+1K-th of charging is represented respectively
The integration loss of+1 charge cycle battery of cycle and kth, Δ AhRepresent k-th of charge cycle to+1 charge cycle of kth this
Total ampere-hour number of disengaging battery in one time interval：Assuming that the time of one charge cycle of battery
For 1 second, the charging process common N seconds, i.e., N number of charge cycle.In order that capacity attenuation of the battery in charging process is minimum, then optimize
Object function f1For：
Step 112：According in battery charging process, battery charge state change mechanism model：
It is determined that with charging interval most short the second object function for target, second object function is：
Wherein, f2The second object function is represented, SOC (k) represents k-th of charge cycle
Battery charge state, Cap represent battery rated capacity, IjRepresent the charging current of j-th of charge cycle.In filling for battery
In electric process, the charging interval is to weigh another good and bad key factor of charging, should if making charging rate fast as far as possible
Meet another object function be：Make the charging of battery in N number of charge cycle
Electricity is maximum.
In order that the charging interval has same variation tendency with battery capacity attenuation, the two is set to pass through same mesh
Scalar functions optimize, willEquivalence transformation is the second object function：Its
In, the second bound for objective function is Ik≤Ik,max, wherein, Ik,maxThe maximum charging current of k-th of charge cycle is represented,
Ik,maxIt is relevant with the SOC of k-th of charge cycle, it can be determined according to the corresponding relation of table 1, wherein,Represent to identify on battery nameplate
Maximum charging current.
The mapping table of the maximum charging current of table 1 and SOC
Step 113：According to the first object function and second object function, the charge target function is determined,
The charge target function is：Wherein, fkRepresent
The charge target function of k-th of charge cycle, a represent compensating factor, and effect is SOC and capacity attenuation is had identical quantity
Level, K=1,2 ... ..., N.
Wherein, step 13：According to the actual measurement body temperature and the actual measurement environment temperature ,+1 charging week of prediction kth
Phase between kth+p charge cycles in each charge cycle battery prediction body temperature, specifically include：
Step 131：To in charging process, the relational expression of the body temperature of charging current, the environment temperature of battery and battery
Sliding-model control is carried out, obtains battery temperature forecast model, wherein, the relational expression is：
The battery temperature forecast model is：m
Battery quality is represented, unit is kilogram CpThermal capacitance is represented, T represents battery temperature, and t represents the time, and I represents charging current, VOCTable
Show the open-circuit voltage of battery, VTThe terminal voltage of battery is represented, h represents convective heat-transfer coefficient, and A represents battery surface product, TambRepresent
The environment temperature of battery, T 'k+1Represent the prediction body temperature of battery in+1 charge cycle of kth, TkRepresent k-th of charging week
The actual measurement body temperature of battery, I in phasekThe charging current of k-th of charge cycle is represented, Δ t represents charge cycle, VOC.kRepresent
The open-circuit voltage of k-th of charge cycle battery, VT, kThe terminal voltage of k-th of charge cycle battery is represented,Represent k-th
The Entropy Changes of charge cycle battery, TAmb, kRepresent the actual measurement environment temperature of k-th of charge cycle battery.Wherein, VOC.kWith
Relevant, the cobalt acid lithium battery using rated capacity as 2.6Ah with current time battery charge state (State ofcharge, SOC)
Exemplified by, its open-circuit voltage and Entropy Changes and SOC corresponding relation are as shown in table 2.
The mapping table of the open-circuit voltage of table 2 and Entropy Changes and SOC
Step 132：According to the actual measurement body temperature of k-th of charge cycle battery, k-th of charge cycle battery
Environment temperature and k-th of charge cycle charging current, the charging week of kth+1 is predicted using the battery temperature forecast model
Phase between kth+p charge cycles in each charge cycle battery prediction body temperature, wherein, p represents Model Predictive Control
The prediction step of method.
As shown in Fig. 2 step 14：According to the charge target function and each prediction body temperature, using model
Forecast Control Algorithm prediction makes the optimal charging current of minimum+1 charge cycle of kth of the charge target function, specific bag
Include：
Step 141：Maximum charging current is determined according to the battery charge state of k-th of charge cycle；
Step 142：Described+1 charge cycle of kth is obtained to each charge cycle between the kth+p charge cycles
Charging current predetermined number nk+i, 1≤i≤p；
Step 143：According to the maximum charging current and the predetermined number nk+i, it is determined that each in each charge cycle
Individual preset charged electric current；
Step 144：According to each preset charged electric current generation n in each charge cyclek+1×nk+2×…×
nk+pIndividual different charging current sequence；
Step 145：According to the charge target function, each prediction body temperature and each charging current sequence
Row, determine optimal charge sequence, and the optimal charge sequence is the charging electricity for making the functional value of the charge target function minimum
Sequence is flowed, first element in the optimal charge sequence is the optimal charging current of+1 charge cycle of kth.
Model Predictive Control replaces global optimum using local optimum, there is that real-time optimization, disturbance rejection are strong,
Extensive application in the engineering of line optimization.Therefore, the present embodiment is rolled using Model Predictive Control (MPC) to charging process
Dynamic Optimization Solution.The Forecasting Methodology that the present embodiment provides, the final form of the object function of optimization are declined using SOC with battery capacity
Subtract the mode of summation, two optimization aims can be made to establish contact, the object function using Model Predictive Control Algorithm to structure
Optimize, requirement of real-time can be met and resist the requirement of the external disturbances such as variation of ambient temperature, according to environment temperature certainly
Dynamic adjustment charging current, reduce the charging interval and extend battery.
Embodiment 2：
As shown in figure 3, a kind of forecasting system 2 of lithium cell charging electric current includes：
Objective function module 21, for minimum most short for target with the charging interval, the construction with battery capacity attenuation
Charge target function；
Temperature acquisition module 22, actual measurement body temperature and k-th of charging for k-th of charge cycle battery of acquisition
The actual measurement environment temperature of periodic battery Service Environment；
Temperature prediction module 23, for according to the actual measurement body temperature and the actual measurement environment temperature, predicting kth+1
Charge cycle between kth+p charge cycles in each charge cycle battery prediction body temperature, wherein, p represents that model is pre-
Survey the prediction step of control method；
Charging current prediction module 24, for according to the charge target function and each prediction body temperature, adopting
Make the optimal charging current of minimum+1 charge cycle of kth of the charge target function with model predictive control method prediction.
Specifically, the objective function module 21 specifically includes：
First object function unit 211, for according to battery capacity decay mechanism model：
It is determined that with the first object function of the minimum target of battery capacity attenuation, the first object function is：
Wherein, f1Represent first object function, QlossRepresent that battery holds
Attenuation is measured, B represents coefficient before index, and e represents natural constant, and E represents activation energy, and a represents battery multiplying power corrected parameter, C_Rate
The absolute value of battery charging and discharging current ratio is represented, R represents ideal gas constant, and T represents the Kelvin of battery, AhRepresent into
Go out the ampere-hour number of battery, z represents power law parameter, IkRepresent the charging current of k-th of charge cycle, TkRepresent k-th of charging
The Kelvin of periodic battery, N represent charge cycle sum, Qloss,kThe battery capacity attenuation of k-th of charge cycle is represented,
Qloss,0=0；
Second object function unit 212, for according in battery charging process, battery charge state change mechanism model：It is determined that with charging interval most short the second object function for target, second object function
For：Wherein, f2The second object function is represented, SOC (k) k-th of charge cycle of expression
Battery charge state, Cap represent the rated capacity of battery, IjRepresent the charging current of j-th of charge cycle；
Charge function unit 213, for according to the first object function and second object function, it is determined that described fill
Electric object function, the charge target function are：
Wherein, fkThe charge target function of k-th of charge cycle is represented, a represents compensating factor, K=1,2 ... ..., N.
Specifically, the temperature prediction module 23 specifically includes：
Forecast model determining unit 231, in charging process, charging current, the environment temperature of battery and battery
The relational expression of body temperature carries out sliding-model control, obtains battery temperature forecast model, wherein, the relational expression is：The battery temperature forecast model is：
M represents battery quality, CpRepresent thermal capacitance,
T represents battery temperature, and t represents the time, and I represents charging current, VOCRepresent the open-circuit voltage of battery, VTRepresent the end electricity of battery
Pressure, h represent convective heat-transfer coefficient, and A represents battery surface product, TambRepresent the environment temperature of battery, T 'k+1Represent that kth+1 is filled
The prediction body temperature of battery, T in the electric cyclekRepresent the actual measurement body temperature of battery in k-th of charge cycle, IkRepresent k-th
The charging current of charge cycle, Δ t represent charge cycle, VOC.kRepresent the open-circuit voltage of k-th of charge cycle battery, VT, kTable
Show the terminal voltage of k-th of charge cycle battery,Represent the Entropy Changes of k-th of charge cycle battery, TAmb, kRepresent k-th
The actual measurement environment temperature of charge cycle battery；
Body temperature predicting unit 232, for the actual measurement body temperature according to k-th of charge cycle battery, described
The environment temperature of k-th charge cycle battery and the charging current of k-th of charge cycle, using the battery temperature forecast model
Predict+1 charge cycle of kth between the individual charge cycles of kth+p in each charge cycle battery prediction body temperature, wherein, p
Represent the prediction step of model predictive control method.
As shown in figure 4, the charging current prediction module 24 specifically includes：
Maximum current determining unit 241, for determining maximum charge electricity according to the battery charge state of k-th of charge cycle
Stream；
Electric current number acquiring unit 242, for obtaining described+1 charge cycle of kth to the kth+p charge cycles
Between each charge cycle charging current predetermined number nk+i, 1≤i≤p；
Predetermined current determining unit 243, for according to the maximum charging current and the predetermined number nk+i, it is determined that often
Each preset charged electric current in individual charge cycle；
Current sequence generation unit 244, for being given birth to according to each preset charged electric current in each charge cycle
Into nk+1×nk+2×…×nk+pIndividual different charging current sequence；
Optimal current determining unit 245, for according to each prediction body temperature and each charging current sequence
Row, determine optimal charge sequence, and the optimal charge sequence is the charging electricity for making the functional value of the charge target function minimum
Sequence is flowed, first element in the optimal charge sequence is the optimal charging current of+1 charge cycle of kth.
The forecasting system that the present embodiment provides, by the real-time monitoring of environmental temperature of two temperature sensors and battery temperature,
And later battery body temperature is constantly predicted by the body temperature and environment temperature of present battery, reduce charging so as to realize
Time and the complex optimum for delaying cell degradation.In optimization process, two temperature sensors constantly carry out feedback to temperature and rectified
Just, the change for making whole forecasting system there is stronger robustness to carry out response environment temperature.
Embodiment 3：
As shown in figure 5, a kind of charging device, the charging device is connected with lithium battery, for being filled to the lithium battery 30
Electricity, the charging device include：First temperature sensor 31, second temperature sensor 32, charge controller 33, the and of charger 34
Forecasting system 2 described in embodiment 2, wherein,
First temperature sensor 31 is used for the actual measurement environment temperature for gathering the Service Environment of lithium battery 30；
The second temperature sensor 32 is connected with the lithium battery 30, for gathering the actual measurement body of the lithium battery 30
Temperature；
The forecasting system 2 is connected with first temperature sensor 31 and the second temperature sensor 32 respectively, is used
In minimum most short for target with the charging interval, the construction charge target function with battery capacity attenuation, and according to k-th of charging week
The actual measurement body temperature of phase battery and the actual measurement environment temperature of k-th of charge cycle battery Service Environment, it is pre- using model
Surveying control method prediction makes the optimal charging current of minimum+1 charge cycle of kth of charge target function；
The charger 34 is connected with the lithium battery 30, for monitoring the battery charge state of the lithium battery 30；
The charge controller 33 is connected with the forecasting system 2 and the charger 34 respectively, is used for
Judge whether the state-of-charge of present battery 30 reaches default battery charge state, obtain the first judged result；
When the first judged result represent present battery state-of-charge be not up to default battery charge state, then will be described pre-
The signal for the optimal charging current that examining system determines is sent to the charger 34, and the charger 34 is according to the optimal charging
The signal of electric current charges to the lithium battery 30；
When the first judged result represent present battery state-of-charge reach default battery charge state, then send stopping and fill
Electricity order, the charger 34 stop charging to the lithium battery 30 according to the stopping charge command.
The charging device that the present embodiment provides, can optimize charging to the battery in varying environment temperature, have
Body step includes：
Step 301：Using the first temperature sensor 31 and second temperature sensor 32 in real time to battery temperature and environment temperature
Degree is acquired, and the data collected are sent to forecasting system.
Step 302：Forecasting system 2 predicts the battery temperature at the moment of kth+1 to k+p moment.
Step 303：Solution is optimized to object function according to the battery temperature of prediction.
Step 304：Obtain optimal charging current sequence.
Step 305：Charge controller takes first element of optimal charging current sequence right to charger 34, charger 34
Battery 30 charges.
Step 306：Charge controller 33 judges whether SOC meets to require；
If so, complete charge, otherwise, performs step 301.
In the present embodiment, battery SOC data that charge controller gathers to charger, the temperature of each temperature sensor collection
Data are stored, and according to optimization of the forecasting system to object function, charging instruction is sent to charger.Forecasting system is to fill
Based on the temperature and battery both end voltage of electric controller storage, using Model Predictive Control Algorithm (MPC), object function is entered
Row optimization, draws the charging current sequence from P moment backward at this moment.
Charging device provided by the invention, temperature requirement that can adaptively under varying environment are carried out excellent to charging process
Change, so as to reach the purpose for reducing the charging interval, extending battery life.
Each embodiment is described by the way of progressive in this specification, what each embodiment stressed be and other
The difference of embodiment, between each embodiment identical similar portion mutually referring to.For system disclosed in embodiment
For, because it is corresponded to the method disclosed in Example, so description is fairly simple, related part is said referring to method part
It is bright.
Specific case used herein is set forth to the principle and embodiment of the present invention, and above example is said
It is bright to be only intended to help the method and its core concept for understanding the present invention；Meanwhile for those of ordinary skill in the art, foundation
The thought of the present invention, in specific embodiments and applications there will be changes.In summary, this specification content is not
It is interpreted as limitation of the present invention.
Claims (9)
1. a kind of Forecasting Methodology of lithium cell charging electric current, it is characterised in that the Forecasting Methodology includes：
It is minimum most short for target with the charging interval with battery capacity attenuation, construct charge target function；
Obtain the actual measurement for surveying body temperature and k-th of charge cycle battery Service Environment of k-th of charge cycle battery
Environment temperature；
According to the actual measurement body temperature and the actual measurement environment temperature, prediction+1 charge cycle of kth to+p charging weeks of kth
Between phase in each charge cycle battery prediction body temperature, wherein, p represent model predictive control method prediction step；
According to the charge target function and each prediction body temperature, made using model predictive control method prediction described
The optimal charging current of minimum+1 charge cycle of kth of charge target function.
2. Forecasting Methodology according to claim 1, it is characterised in that described so that battery capacity attenuation is minimum and during charging
Between it is most short be target, construct charge target function, specifically include：
According to battery capacity decay mechanism model：It is determined that with battery capacity attenuation most
The small first object function for target, the first object function are：
Wherein, f1Represent first object function, QlossBattery capacity attenuation is represented, B represents coefficient before index, and e represents natural constant, E
Activation energy is represented, a represents battery multiplying power corrected parameter, C_RateThe absolute value of battery charging and discharging current ratio is represented, R represents preferable
Gas constant, T represent the Kelvin of battery, AhThe ampere-hour number of disengaging battery is represented, z represents power law parameter, IkRepresent the
The charging current of k charge cycle, TkThe Kelvin of k-th of charge cycle battery is represented, N represents charge cycle sum,
Qloss,kRepresent the battery capacity attenuation of k-th of charge cycle, Qloss,0=0；
According in battery charging process, battery charge state change mechanism model：It is determined that to fill
Most short the second object function for target of electric time, second object function are：Its
In, f2The second object function is represented, SOC (k) represents the battery charge state of k-th of charge cycle, and Cap represents the specified of battery
Capacity, IjRepresent the charging current of j-th of charge cycle；
According to the first object function and second object function, the charge target function, the charge target are determined
Function is：Wherein, fkRepresent k-th of charge cycle
Charge target function, a represent compensating factor, K=1,2 ... ..., N.
3. Forecasting Methodology according to claim 1, it is characterised in that described according to the actual measurement body temperature and the reality
Survey environment temperature, prediction+1 charge cycle of kth between the individual charge cycles of kth+p in each charge cycle battery prediction body
Temperature, specifically include：
To in charging process, the relational expression of the body temperature of charging current, the environment temperature of battery and battery is carried out at discretization
Reason, battery temperature forecast model is obtained, wherein, the relational expression is：
The battery temperature forecast model is：M tables
Show battery quality, CpThermal capacitance is represented, T represents battery temperature, and t represents the time, and I represents charging current, VOCRepresent the open circuit of battery
Voltage, VTThe terminal voltage of battery is represented, h represents convective heat-transfer coefficient, and A represents battery surface product, TambRepresent the environment temperature of battery
Degree, Tk′+1Represent the prediction body temperature of battery in+1 charge cycle of kth, TkRepresent the reality of battery in k-th of charge cycle
Survey body temperature, IkThe charging current of k-th of charge cycle is represented, Δ t represents charge cycle, VOC.kRepresent k-th of charge cycle
The open-circuit voltage of battery, VT, kThe terminal voltage of k-th of charge cycle battery is represented,Represent k-th charge cycle battery
Entropy Changes, TAmb, kRepresent the actual measurement environment temperature of k-th of charge cycle battery；
According to the actual measurement body temperature of k-th of charge cycle battery, the reality of k-th of charge cycle battery Service Environment
Environment temperature and the charging current of k-th of charge cycle are surveyed ,+1 charging week of kth is predicted using the battery temperature forecast model
Phase between kth+p charge cycles in each charge cycle battery prediction body temperature, wherein, p represents Model Predictive Control
The prediction step of method.
4. Forecasting Methodology according to claim 1, it is characterised in that described according to the charge target function and each institute
Prediction body temperature is stated, the charge target function minimum+1 charging week of kth is made using model predictive control method prediction
The optimal charging current of phase, is specifically included：
Maximum charging current is determined according to the battery charge state of k-th of charge cycle；
Described+1 charge cycle of kth is obtained to the charging current of each charge cycle between the kth+p charge cycles
Predetermined number nk+i, 1≤i≤p；
According to the maximum charging current and the predetermined number nk+i, it is determined that each preset charged electricity in each charge cycle
Stream；
According to each preset charged electric current generation n in each charge cyclek+1×nk+2×…×nk+pIndividual different fills
Electric current sequence；
According to the charge target function, each prediction body temperature and each charging current sequence, determine optimal
Charge sequence, the optimal charge sequence is the charging current sequence for making the functional value of the charge target function minimum, described
First element in optimal charge sequence is the optimal charging current of+1 charge cycle of kth.
5. a kind of forecasting system of lithium cell charging electric current, it is characterised in that the forecasting system includes：
Objective function module, for, construction charging mesh minimum most short for target with the charging interval with battery capacity attenuation
Scalar functions；
Temperature acquisition module, for obtaining the actual measurement body temperature and k-th of charge cycle electricity of k-th of charge cycle battery
The actual measurement environment temperature of pond Service Environment；
Temperature prediction module, for according to the actual measurement body temperature and the actual measurement environment temperature ,+1 charging week of prediction kth
Phase between kth+p charge cycles in each charge cycle battery prediction body temperature, wherein, p represents Model Predictive Control
The prediction step of method；
Charging current prediction module, for according to the charge target function and each prediction body temperature, using model
Forecast Control Algorithm prediction makes the optimal charging current of minimum+1 charge cycle of kth of the charge target function.
6. forecasting system according to claim 5, it is characterised in that the objective function module specifically includes：
First object function unit, for according to battery capacity decay mechanism model：
It is determined that with the first object function of the minimum target of battery capacity attenuation, the first object function is：
Wherein, f1Represent first object function, QlossRepresent battery capacity
Attenuation, B represent coefficient before index, and e represents natural constant, and E represents activation energy, and a represents battery multiplying power corrected parameter, C_RateTable
Show the absolute value of battery charging and discharging current ratio, R represents ideal gas constant, and T represents the Kelvin of battery, AhRepresent disengaging
The ampere-hour number of battery, z represent power law parameter, IkRepresent the charging current of k-th of charge cycle, TkRepresent k-th of charging week
The Kelvin of phase battery, N represent charge cycle sum, Qloss,kThe battery capacity attenuation of k-th of charge cycle is represented,
Qloss,0=0；
Second object function unit, for according in battery charging process, battery charge state change mechanism model：
It is determined that with charging interval most short the second object function for target, second target
Function is：Wherein, f2The second object function is represented, SOC (k) represents k-th of charging week
The battery charge state of phase, Cap represent the rated capacity of battery, IjRepresent the charging current of j-th of charge cycle；
Charge function unit, for according to the first object function and second object function, determining the charge target
Function, the charge target function are：Wherein,
fkThe charge target function of k-th of charge cycle is represented, a represents compensating factor, K=1,2 ... ..., N.
7. forecasting system according to claim 5, it is characterised in that the temperature prediction module specifically includes：
Forecast model determining unit, in charging process, the body temperature of charging current, the environment temperature of battery and battery
Relational expression carry out sliding-model control, obtain battery temperature forecast model, wherein, the relational expression is：
The battery temperature forecast model is：M tables
Show battery quality, CpThermal capacitance is represented, T represents battery temperature, and t represents the time, and I represents charging current, VOCRepresent the open circuit of battery
Voltage, VTThe terminal voltage of battery is represented, h represents convective heat-transfer coefficient, and A represents battery surface product, TambRepresent the environment temperature of battery
Degree, Tk′+1Represent the prediction body temperature of battery in+1 charge cycle of kth, TkRepresent the reality of battery in k-th of charge cycle
Survey body temperature, IkThe charging current of k-th of charge cycle is represented, Δ t represents charge cycle, VOC.kRepresent k-th of charge cycle
The open-circuit voltage of battery, VT, kThe terminal voltage of k-th of charge cycle battery is represented,Represent k-th charge cycle battery
Entropy Changes, TAmb, kRepresent the actual measurement environment temperature of k-th of charge cycle battery；
Body temperature predicting unit, filled for the actual measurement body temperature according to k-th of charge cycle battery, described k-th
The actual measurement environment temperature of electric periodic battery Service Environment and the charging current of k-th of charge cycle, it is pre- using the battery temperature
Survey+1 charge cycle of model prediction kth between the individual charge cycles of kth+p in each charge cycle battery prediction body temperature,
Wherein, p represents the prediction step of model predictive control method.
8. forecasting system according to claim 5, it is characterised in that the charging current prediction module specifically includes：
Maximum current determining unit, for determining maximum charging current according to the battery charge state of k-th of charge cycle；
Electric current number acquiring unit, for obtaining described+1 charge cycle of kth to each between the kth+p charge cycles
The predetermined number n of the charging current of charge cyclek+i, 1≤i≤p；
Predetermined current determining unit, for according to the maximum charging current and the predetermined number nk+i, it is determined that each charging week
Each preset charged electric current in phase；
Current sequence generation unit, for generating n according to each preset charged electric current in each charge cyclek+1×
nk+2×…×nk+pIndividual different charging current sequence；
Optimal current determining unit, for according to the charge target function, each prediction body temperature and each described
Charging current sequence, determines optimal charge sequence, and the optimal charge sequence is to make the functional value of the charge target function most
Small charging current sequence, first element in the optimal charge sequence for+1 charge cycle of kth optimal charging electricity
Stream.
A kind of 9. charging device, it is characterised in that the charging device is connected with lithium battery, for the lithium cell charging,
The charging device includes：First temperature sensor, second temperature sensor, charge controller, charger and claim 5~
Forecasting system described in 8 any one, wherein,
First temperature sensor is used for the actual measurement environment temperature for gathering the lithium battery Service Environment；
The second temperature sensor is connected with the lithium battery, for gathering the actual measurement body temperature of the lithium battery；
The forecasting system is connected with first temperature sensor and the second temperature sensor respectively, for being held with battery
It is target that it is most short with the charging interval, which to measure attenuation minimum, constructs charge target function, and according to the reality of k-th of charge cycle battery
The actual measurement environment temperature of body temperature and k-th of charge cycle battery Service Environment is surveyed, using model predictive control method
Prediction makes the optimal charging current of minimum+1 charge cycle of kth of charge target function；
The charger is connected with the lithium battery, for monitoring the battery charge state of the lithium battery, is additionally operable to described
Lithium cell charging；
The charge controller is connected with the forecasting system and the charger respectively, and the charge controller is used to judge to work as
Whether preceding battery charge state reaches default battery charge state, obtains the first judged result；
When the first judged result represent present battery state-of-charge be not up to default battery charge state, then by it is described prediction be
The signal for the optimal charging current determined of uniting is sent to the charger, and the charger is according to the letter of the optimal charging current
Number give the lithium cell charging；
When the first judged result represent present battery state-of-charge reach default battery charge state, then send stopping charging life
Order, the charger stop to the lithium cell charging according to the stopping charge command.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710850730.4A CN107831441B (en) | 2017-09-20 | 2017-09-20 | Prediction technique, forecasting system and a kind of charging unit of lithium battery charging current |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710850730.4A CN107831441B (en) | 2017-09-20 | 2017-09-20 | Prediction technique, forecasting system and a kind of charging unit of lithium battery charging current |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107831441A true CN107831441A (en) | 2018-03-23 |
| CN107831441B CN107831441B (en) | 2019-11-05 |
Family
ID=61643865
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710850730.4A Active CN107831441B (en) | 2017-09-20 | 2017-09-20 | Prediction technique, forecasting system and a kind of charging unit of lithium battery charging current |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107831441B (en) |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109586373A (en) * | 2018-12-27 | 2019-04-05 | 华为技术有限公司 | A kind of method for charging batteries and device |
| CN109802190A (en) * | 2019-01-31 | 2019-05-24 | 南京理工大学 | A kind of battery pack multiple target charging method |
| CN110071308A (en) * | 2019-05-08 | 2019-07-30 | 福州大学 | A kind of open fuel battery temperature Predictive Control System of cathode and method |
| CN110138028A (en) * | 2019-05-06 | 2019-08-16 | 常州猛犸电动科技有限公司 | A kind of battery management system and its battery charge-discharge control method, terminal device |
| CN110364776A (en) * | 2019-06-26 | 2019-10-22 | 上海商米科技集团股份有限公司 | A kind of lithium battery intelligent charging management method and its device |
| CN110492190A (en) * | 2019-09-06 | 2019-11-22 | 深圳市驰普科达科技有限公司 | Battery management method, system and computer readable storage medium |
| CN110515001A (en) * | 2019-09-07 | 2019-11-29 | 创新奇智(广州)科技有限公司 | A kind of two stages battery performance prediction technique based on charge and discharge |
| CN110888070A (en) * | 2018-08-16 | 2020-03-17 | 广州小鹏汽车科技有限公司 | Battery temperature estimation method, device, equipment and medium |
| CN111791754A (en) * | 2020-05-25 | 2020-10-20 | 博雷顿科技有限公司 | Battery charging temperature control method for pure electric vehicle |
| CN111948540A (en) * | 2020-06-29 | 2020-11-17 | 东风商用车有限公司 | Power battery pack temperature prediction method and system |
| CN112018854A (en) * | 2020-09-08 | 2020-12-01 | 广东省华南技术转移中心有限公司 | Battery charging control method, device, terminal and storage medium |
| CN112448053A (en) * | 2019-08-30 | 2021-03-05 | 北京小米移动软件有限公司 | Charging method and device of mobile terminal, terminal and storage medium |
| CN113075558A (en) * | 2021-06-08 | 2021-07-06 | 天津市松正电动科技有限公司 | Battery SOC estimation method, device and system |
| CN113125982A (en) * | 2019-12-31 | 2021-07-16 | 比亚迪股份有限公司 | Battery life prediction method and device |
| CN113386629A (en) * | 2021-06-11 | 2021-09-14 | 北京车和家信息技术有限公司 | Battery thermal management control method, device, medium and equipment |
| CN113507154A (en) * | 2021-07-12 | 2021-10-15 | 海南小鲨鱼智能科技有限公司 | Charging method and device, charger and electronic equipment |
| CN117141262A (en) * | 2023-11-01 | 2023-12-01 | 成都赛力斯科技有限公司 | Battery charging method and device and vehicle |
| CN117284105A (en) * | 2023-11-01 | 2023-12-26 | 赛力斯汽车有限公司 | Battery segmented charging method and device and vehicle |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2753491A1 (en) * | 2011-09-07 | 2014-07-16 | Electricité de France | Method and device for the optimized recharging of an electric battery |
| CN103135057B (en) * | 2011-11-29 | 2015-08-19 | 李革臣 | A kind of method for fast measuring of self-discharge of battery performance |
| CN105552465A (en) * | 2015-12-03 | 2016-05-04 | 北京交通大学 | Lithium ion battery optimized charging method based on time and temperature |
| CN106025410A (en) * | 2016-07-22 | 2016-10-12 | 哈尔滨理工大学 | Battery low-temperature variable current heating method for electric vehicles |
| CN106828161A (en) * | 2017-02-13 | 2017-06-13 | 清华大学 | One kind is applied to multichannel charging socket charging equipment of electric automobile and its control method |
| US20170229891A1 (en) * | 2016-02-05 | 2017-08-10 | Korea Advanced Institute Of Science And Technology | Optimized battery charging method based on thermodynamic information of battery |
| CN107039696A (en) * | 2016-11-29 | 2017-08-11 | 北京交通大学 | A kind of track traffic vehicle carries the optimization charging method of energy storage lithium ion battery |
| CN107139762A (en) * | 2017-06-05 | 2017-09-08 | 吉林大学 | A kind of electric automobile optimization charge control method and its system |
-
2017
- 2017-09-20 CN CN201710850730.4A patent/CN107831441B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2753491A1 (en) * | 2011-09-07 | 2014-07-16 | Electricité de France | Method and device for the optimized recharging of an electric battery |
| CN103135057B (en) * | 2011-11-29 | 2015-08-19 | 李革臣 | A kind of method for fast measuring of self-discharge of battery performance |
| CN105552465A (en) * | 2015-12-03 | 2016-05-04 | 北京交通大学 | Lithium ion battery optimized charging method based on time and temperature |
| US20170229891A1 (en) * | 2016-02-05 | 2017-08-10 | Korea Advanced Institute Of Science And Technology | Optimized battery charging method based on thermodynamic information of battery |
| CN106025410A (en) * | 2016-07-22 | 2016-10-12 | 哈尔滨理工大学 | Battery low-temperature variable current heating method for electric vehicles |
| CN107039696A (en) * | 2016-11-29 | 2017-08-11 | 北京交通大学 | A kind of track traffic vehicle carries the optimization charging method of energy storage lithium ion battery |
| CN106828161A (en) * | 2017-02-13 | 2017-06-13 | 清华大学 | One kind is applied to multichannel charging socket charging equipment of electric automobile and its control method |
| CN107139762A (en) * | 2017-06-05 | 2017-09-08 | 吉林大学 | A kind of electric automobile optimization charge control method and its system |
Non-Patent Citations (2)
| Title |
|---|
| CAIPING ZHANG ET AL.: "Charging optimization in lithium-ion batteries based on temperature rise and charge time", 《APPLIED ENERGY》 * |
| XIAOGANG WU ET AL.: "Multi-Objective Optimal Charging Method for Lithium-Ion Batteries", 《ENERGIES》 * |
Cited By (24)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110888070A (en) * | 2018-08-16 | 2020-03-17 | 广州小鹏汽车科技有限公司 | Battery temperature estimation method, device, equipment and medium |
| CN109586373B (en) * | 2018-12-27 | 2024-05-17 | 华为技术有限公司 | Battery charging method and device |
| CN109586373A (en) * | 2018-12-27 | 2019-04-05 | 华为技术有限公司 | A kind of method for charging batteries and device |
| CN109802190A (en) * | 2019-01-31 | 2019-05-24 | 南京理工大学 | A kind of battery pack multiple target charging method |
| CN110138028A (en) * | 2019-05-06 | 2019-08-16 | 常州猛犸电动科技有限公司 | A kind of battery management system and its battery charge-discharge control method, terminal device |
| CN110071308A (en) * | 2019-05-08 | 2019-07-30 | 福州大学 | A kind of open fuel battery temperature Predictive Control System of cathode and method |
| CN110071308B (en) * | 2019-05-08 | 2022-03-11 | 福州大学 | Temperature prediction control system and method for cathode open type fuel cell |
| CN110364776B (en) * | 2019-06-26 | 2020-11-13 | 上海商米科技集团股份有限公司 | Intelligent charging management method and device for lithium battery |
| CN110364776A (en) * | 2019-06-26 | 2019-10-22 | 上海商米科技集团股份有限公司 | A kind of lithium battery intelligent charging management method and its device |
| CN112448053A (en) * | 2019-08-30 | 2021-03-05 | 北京小米移动软件有限公司 | Charging method and device of mobile terminal, terminal and storage medium |
| CN110492190A (en) * | 2019-09-06 | 2019-11-22 | 深圳市驰普科达科技有限公司 | Battery management method, system and computer readable storage medium |
| CN110515001A (en) * | 2019-09-07 | 2019-11-29 | 创新奇智(广州)科技有限公司 | A kind of two stages battery performance prediction technique based on charge and discharge |
| CN113125982A (en) * | 2019-12-31 | 2021-07-16 | 比亚迪股份有限公司 | Battery life prediction method and device |
| CN111791754A (en) * | 2020-05-25 | 2020-10-20 | 博雷顿科技有限公司 | Battery charging temperature control method for pure electric vehicle |
| CN111948540A (en) * | 2020-06-29 | 2020-11-17 | 东风商用车有限公司 | Power battery pack temperature prediction method and system |
| CN112018854A (en) * | 2020-09-08 | 2020-12-01 | 广东省华南技术转移中心有限公司 | Battery charging control method, device, terminal and storage medium |
| CN113075558A (en) * | 2021-06-08 | 2021-07-06 | 天津市松正电动科技有限公司 | Battery SOC estimation method, device and system |
| CN113075558B (en) * | 2021-06-08 | 2021-08-10 | 天津市松正电动科技有限公司 | Battery SOC estimation method, device and system |
| CN113386629A (en) * | 2021-06-11 | 2021-09-14 | 北京车和家信息技术有限公司 | Battery thermal management control method, device, medium and equipment |
| CN113386629B (en) * | 2021-06-11 | 2023-03-10 | 北京车和家信息技术有限公司 | Battery thermal management control method, device, medium and equipment |
| CN113507154A (en) * | 2021-07-12 | 2021-10-15 | 海南小鲨鱼智能科技有限公司 | Charging method and device, charger and electronic equipment |
| CN117141262A (en) * | 2023-11-01 | 2023-12-01 | 成都赛力斯科技有限公司 | Battery charging method and device and vehicle |
| CN117284105A (en) * | 2023-11-01 | 2023-12-26 | 赛力斯汽车有限公司 | Battery segmented charging method and device and vehicle |
| CN117141262B (en) * | 2023-11-01 | 2024-01-23 | 成都赛力斯科技有限公司 | Battery charging method and device and vehicle |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107831441B (en) | 2019-11-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107831441A (en) | Forecasting Methodology, forecasting system and a kind of charging device of lithium cell charging electric current | |
| Zheng et al. | A novel capacity estimation method for lithium-ion batteries using fusion estimation of charging curve sections and discrete Arrhenius aging model | |
| CN106451600B (en) | For the device and method of quickly charging battery | |
| McCormick et al. | The effect of intermittent solar radiation on the performance of PV systems | |
| Coleman et al. | State-of-charge determination from EMF voltage estimation: Using impedance, terminal voltage, and current for lead-acid and lithium-ion batteries | |
| CN103529399B (en) | Simulation method of lead acid battery-based improved PNGV model | |
| CN102662148B (en) | On-line feedback battery state of charge (SOC) predicting method | |
| CN102540096B (en) | Self-correction method for remaining capacity estimation of lithium iron phosphate power battery | |
| CN108663620A (en) | A kind of power battery pack charge state estimation method and system | |
| CN104617876B (en) | Outdoor test platform of solar photovoltaic modules and electricity generation performance online analysis method thereof | |
| CN105353316B (en) | SOC variable quantities and charge capacity conversion factor measuring method during power battery charging | |
| JPH07192769A (en) | Secondary battery power storage system | |
| CN101615704A (en) | Has the storage battery of automatic detection control function and to the control method of storage battery | |
| CN107169170B (en) | A kind of prediction technique of battery remaining power | |
| CN103869251B (en) | Secondary cell heap(ed) capacity measurement apparatus | |
| CN105116350B (en) | SOC variable quantities and discharge electricity amount conversion factor measuring method when power battery discharges | |
| CN103682508B (en) | A kind of spacecraft lithium-ions battery group state-of-charge defining method | |
| CN103135056A (en) | Battery capacity predicting device and battery capacity predicting method | |
| CN109856545B (en) | The battery group residual capacity detection method and system of solar telephone | |
| CN105051559A (en) | Secondary battery charge status estimation device and secondary battery charge status estimation method | |
| CN105429230B (en) | Battery management system and method based on artificial neural network algorithm | |
| CN109004696A (en) | A kind of substation battery multifunction control system and its control method | |
| CN109031147B (en) | SOC estimation method of lithium iron phosphate battery pack | |
| CN107171035A (en) | The charging method of lithium ion battery | |
| CN105207243B (en) | A kind of battery energy management method for the forecast amendment of wind power plant realtime power |
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 |