CN107171035A - The charging method of lithium ion battery - Google Patents
The charging method of lithium ion battery Download PDFInfo
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- CN107171035A CN107171035A CN201710375164.6A CN201710375164A CN107171035A CN 107171035 A CN107171035 A CN 107171035A CN 201710375164 A CN201710375164 A CN 201710375164A CN 107171035 A CN107171035 A CN 107171035A
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- 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
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- 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
- H01M10/446—Initial charging measures
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- 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
- H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
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- 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
- H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
- H01M10/486—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte for measuring temperature
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- 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
Abstract
Include the invention discloses a kind of charging method of lithium ion battery：Pass through the equivalent-circuit model of neural network lithium ion battery；Coupled Heat Transfer model is to set up initial electro thermal coupling model on the basis of equivalent-circuit model；Multigroup constant-current charge scheme is designed, mesuring battary is tested according to constant-current charge scheme, multigroup test data is obtained；Multigroup multistage charging scheme is designed, the multigroup multistage charging scheme that will meet the constraints of Model for Multi-Objective Optimization is sequentially input to target electro thermal coupling model, obtains result data；And iteration optimization is performed to result data based on Model for Multi-Objective Optimization, one group of Pareto optimal solution is chosen from multigroup multistage charging scheme as charging scheme to be selected；By decision-making technique from the charging scheme to be selected selection target charging scheme.The present invention can be charged using limited experiment number for battery provides optimal charging scheme.
Description
Technical field
The present invention relates to cell art, more particularly to a kind of charging method of lithium ion battery.
Background technology
Lithium ion battery possesses that cell voltage is high, specific capacity is big, self discharge effect is low in the secondary battery, cycle life compared with
The long, advantage without memory effect, is applied to classic secondary cell species in portable electronic device and electric automobile.
But lithium ion battery, which also has, easily to be overcharged, and the shortcomings of aging is obvious has certain safety problem, in the course of the work when using
Must scientificlly and effectively control battery parameter, design suitable charging strategy, just can guarantee that cell safety is efficiently run.
The charging strategy of battery is a comprehensive problem, in order to increase the efficiency of charging, to improve charging current；Greatly
Charging current can cause in battery polarization phenomena increase again, accelerate the rate of ageing of circulating battery.Therefore, the charging interval, fill
The performances such as electrical efficiency, cycle life can regard one group of balance of charging process as, for charging strategy, to accomplish to improve in all directions with
Upper charging performance has the difficulty in design.
Secondary battery charging method the most frequently used at present is constant-current constant-voltage charging.But during constant-current charge, battery
Bearing the ability of high current gradually reduces, and fuel factor also gradually increases, and simply carries out one-part form charging in cell safety and energy
Amount efficiency etc. is considered defective, therefore the concept of multistage constant-current charge is suggested.This method is to divide charging process
For multiple stages, the constant-current charge size of current in each stage is allowed to adapt to maximum acceptable charging current in present battery as far as possible
Size.Therefore, the electric current in multiple stages is gradually reduced, and the mesh of cell safety is protected while ensureing charge rate to reach
's.The strategy of studies have shown that multistage constant-current charge has the excellent of extension battery cycle life compared to conventional constant current constant-voltage charge
Point.Implementing multistage constant-current charge method needs to design number of stages, stage current size and the duration of whole process, according to tune
Grind, the design to multistage constant current strategy at present is realized by experience more.But, the numerical Design of this experience is not met
Battery variety is various, the uneven application present situation of performance indications, and cannot also meet energy-saving consumption-reducing, raising charge rate etc. will
Ask.
The content of the invention
Or the technical problem to be solved in the present invention is to overcome the charging modes of lithium ion battery in the prior art to adopt
With constant-current charge, charged or being caused by the numerical value of Experience Design multistage constant-current charge and can not meet energy-saving consumption-reducing, raising
There is provided a kind of charging method of lithium ion battery for the defect of the requirements such as speed.
The present invention is to solve above-mentioned technical problem by following technical proposals：
A kind of charging method of lithium ion battery, its feature is that the charging method comprises the following steps：
S1, pass through the equivalent-circuit model of neural network lithium ion battery；The input parameter bag of the neutral net
The battery charge state data under battery temperature and different battery temperatures are included, output parameter includes the element in equivalent-circuit model
Parameter；
S2, the heat transfer model that couples on the basis of equivalent-circuit model battery free convection set up the coupling of initial electric-thermal
Model；
S3, design multigroup constant-current charge scheme, mesuring battary is tested according to constant-current charge scheme, multigroup survey is obtained
Try data；The test data obtains target electric-thermal coupling model for being fitted initial electric-thermal coupling model；Fitting parameter bag
Include the thermal capacitance and convection transfer rate in the input weight and heat transfer model in neutral net；
Every group of constant-current charge scheme includes charging interval and corresponding current value；Every group of test data is surveyed including mesuring battary
Terminal voltage, charging current, battery temperature and environment temperature during examination；
S4, design multigroup multistage charging scheme, every group of multistage charging scheme includes charging stage number, each charging rank
The charging interval of section and corresponding current value；
The multistage charging scheme that the constraints of Model for Multi-Objective Optimization will be met is sequentially input to the coupling of target electric-thermal
Model, obtains result data corresponding with each multistage charging scheme；And result data is held based on Model for Multi-Objective Optimization
Row iteration optimizes, and one group of Pareto optimal solution is chosen from multigroup multistage charging scheme as charging scheme to be selected；
Every group of result data includes the energy loss in battery temperature difference and charging process before and after charging total time, charging
Rate；The assessment target of Model for Multi-Objective Optimization is included in battery temperature difference and charging process before and after charging total time, charging
Specific energy loss；
S5, by decision-making technique from the charging scheme to be selected selection target charging scheme.
It is preferred that the equivalent-circuit model is single order RC branch road equivalent-circuit models, the single order RC branch road equivalent electrics
Element in the model of road includes D.C. resistance, polarization resistance and polarization capacity；
The polarization resistance and polarization capacity composition parallel branch are simultaneously connected with the D.C. resistance.
It is preferred that in step S1In, component parameters include the electricity of D.C. resistance resistance, polarization resistance resistance and polarization capacity
Capacitance.
It is preferred that in step S1In, the neutral net is ELM neutral nets.
It is preferred that in step S3In, based on Baron (Branch-And-Reduce Optimization Navigator,
Branch, which reduces, optimizes navigation) algorithm is fitted to initial electric-thermal coupling model.
It is preferred that in step S5In, the constraints of Model for Multi-Objective Optimization includes：
The current value of each charging stage is gradually reduced；
Mesuring battary is charged into SOC (charge capacity in present battery accounts for the fraction of maximum battery capacity) more than first
Preset value；
The terminal voltage of mesuring battary is less than or equal to blanking voltage in charging process；
Battery temperature is less than or equal to the second preset value in charging process.
It is preferred that in step S5Before, the charging method also includes：
Model for Multi-Objective Optimization is set up based on multi-objective genetic algorithm.
It is preferred that step S6Specifically include：
By the decision-making technique based on TOPSIS (superior and inferior evaluating algorithm in one) algorithms and the entropy coefficient of mesuring battary from institute
State selection target charging scheme in charging scheme to be selected；
The entropy coefficient is the terminal voltage of mesuring battary and the ratio of battery temperature.
The positive effect of the present invention is：The present invention can be provided most using limited experiment number for battery charging
Good charging scheme, to lift the speed of charging, reduce charging process energy consumption, control battery temperature lifting amplitude.
Brief description of the drawings
Fig. 1 is the flow chart of the charging method of the lithium ion battery of a preferred embodiment of the present invention.
Fig. 2 is the circuit diagram of electric-thermal coupling model in Fig. 1.
Embodiment
The present invention is further illustrated below by the mode of embodiment, but does not therefore limit the present invention to described reality
Apply among a scope.
As shown in figure 1, the charging method of the lithium ion battery of the present embodiment comprises the following steps：
Step 101, the equivalent-circuit model by neural network lithium ion battery.
Wherein, the input parameter of neutral net includes the battery charge state under battery temperature and different battery temperatures, defeated
Going out parameter includes the component parameters in equivalent-circuit model.In the present embodiment, it is modeled using ELM neutral nets.Specifically,
As shown in Fig. 2 equivalent-circuit model is single order RC branch road equivalent-circuit models, single order RC branch roads equivalent-circuit model includes direct current
Resistance, polarization resistance and polarization capacity, and polarization resistance and polarization capacity composition parallel branch and connect with D.C. resistance.It is then first
Part parameter includes the capacitance of D.C. resistance resistance, polarization resistance resistance and polarization capacity.
Step 102, on the basis of equivalent-circuit model couple battery free convection heat transfer model with set up it is initial electricity-
Thermal coupling model.
The influence of temperature and SOC to circuit parameter is considered in order to improve the degree of accuracy of mathematical modeling, during modeling, therefore, also
Equivalent-circuit model is implemented with thermal model and coupled.
Step 103, the multigroup constant-current charge scheme of design, test mesuring battary according to constant-current charge scheme, obtain
Multigroup test data；Test data obtains target electric-thermal coupling model for being fitted initial electric-thermal coupling model.
Wherein, fitting parameter includes the thermal capacitance in the input weight and heat transfer model in neutral net and heat convection system
Number；Every group of constant-current charge scheme includes charging interval and corresponding current value；Every group of test data was tested including mesuring battary
Terminal voltage, charging current, battery temperature and environment temperature in journey.In the present embodiment, based on Baron algorithms to initial electric-thermal
Coupling model is fitted.
The process of mathematical modeling is described in detail below：
Before founding mathematical models, first, investigation obtains the nominal operational parameters of target rechargeable battery, main to include maximum
Charging current, charge cutoff voltage etc.；Secondly, the possible operating temperature range of battery is determined；Finally, according to maximum charge electricity
Stream, the design constant-current constant-voltage charging experiment of battery operating temperature scope, experimental data is more, and mathematical modeling is more accurate, in the present embodiment
3-5 temperature is designed, the current value of 3-5 constant-current phase carries out the CC-CV charging experiments that selected temperature starts, by a timing
Between battery state of charge under the interval record open-circuit voltage of battery, terminal voltage, charging current, real time temperature, different temperatures etc.
Data, so as to obtain UOC- SOC curves, entropy coefficient-SOC curves, in order to further improve the accuracy of model, also to song
Line carries out linear fit.Wherein, SOC is state-of-charge, between 0 and 1, is also often expressed as a percentage, and is referred in present battery
Charged capacity account for the fraction of maximum battery capacity；UOCFor open-circuit voltage；Entropy coefficient is the terminal voltage and battery temperature of battery
Ratio.
Obtain after above-mentioned sample training parameter, be modeled.
Step 1：The single order RC equivalent-circuit models of description battery dynamic characteristic are set up, the model is gone here and there again by R1 after C is in parallel
Join the U of R0 and batteryOCComposition；
Model can be described with below equation：
Ub=UOC+U0+U1
Wherein UbIt is battery terminal voltage, UOCIt is the open-circuit voltage of battery, U0It is D.C. resistance R0The voltage at two ends, U1It is RC
The voltage at parallel branch two ends, this formula describes the terminal voltage that battery directly measures and the ohmically voltage of open-circuit voltage, model
Relation.
Magnitude of voltage in RC parallel branches is relevant with the size of resistance and electric capacity, is derived according to Kirchhoff's current law (KCL),
Meet the following differential equation：
Wherein I is the total current on main road, and above formula can obtain iterative after integrating：
τ=R in formula1*C1, it is the time constant on shunt circuit.
Thermal model part mainly consider inside battery reaction can backheating and can not backheating, the heat generation of battery is not
Can backheating energy, namely energy loss can be expressed as：
The temperature of battery rises the difference dissipated from the heat generation and convection heat transfer' heat-transfer by convection of charging process, thus temperature meet with
The lower differential equation：
Wherein S is entropy coefficient, and m is battery quality, CpFor battery thermal capacitance, h is the battery surface coefficient of heat transfer, and A is heat transfer sheet
Area, TemambRepresent environment temperature；It is identical with voltage, it can obtain the iterative of battery temperature Tem after differential equation integration：
Wherein, Δ t=t (n)-t (n-1).
Step 2：By the ELM neural network structures comprising input layer, output layer and hidden layer, the inputting of system is obtained, defeated
Go out model.
The model of multi output can be represented by the formula：
A in formulaiAnd biIt is the input weight of model, g (ai,bi, x) be ELM activation primitive, βijJoin for certain concealed nodes
Several output weights, n is the quantity of concealed nodes in model, with reference to pair of the activation primitive of following form, and temperature and SOC
Input variable, can derive expression equivalent circuit parameter R0、R1, C formula, and h and CpAs constant：
Due to the multi input of model, input weight aiComprising Iwi1, Iwi2 two parts, obtained after Ow identifications (fitting)
Weight is exported, concealed nodes number n people is design, Iwi1, Iwi2, b in the methodiObtained at random by algorithm.
Step 3：SOC, open-circuit voltage are calculated according to the test data in battery testing, what wherein SOC calculating was used
It is ampere-hour method, formula is expressed as：
CiIt is the total capacity that battery measurement goes out.
Step 4：The above-mentioned survey that obtained SOC, Temh and Q will be calculated in step 1,2 and 3 and is obtained by battery testing
Examination parameter is simultaneously fitted optimization based on Baron algorithms to initial electric-thermal coupling model, can use least square method, optimize mesh
Mark is the minimums such as the terminal voltage and temperature error of match value and test value, and object function can be expressed as with formula：
Wherein,Refer to the terminal voltage data of fitting,Similarly, program calculates the output weight for obtaining one group of ELM model
Optimal solution, the electric-thermal coupling model above-mentioned for characterizing, to obtain target electric-thermal coupling model.
Step 104, the multigroup multistage charging scheme of design, will meet the multistage of the constraints of Model for Multi-Objective Optimization
Charging scheme is sequentially input to target electric-thermal coupling model, obtains result data corresponding with each multistage charging scheme；And
Iteration optimization is performed to result data based on Model for Multi-Objective Optimization, one group of Pareto is chosen from multigroup multistage charging scheme
Optimal solution is used as charging scheme to be selected.
Wherein, every group of multistage charging scheme includes charging stage number, charging interval of each charging stage and corresponding
Current value；Every group of result data includes the energy loss in battery temperature difference and charging process before and after charging total time, charging
Rate；The assessment target of Model for Multi-Objective Optimization is included in battery temperature difference and charging process before and after charging total time, charging
Specific energy loss.
In the present embodiment, Model for Multi-Objective Optimization is set up based on multi-objective genetic algorithm.Model for Multi-Objective Optimization is to result
Data perform iteration optimization, so as to choose one group of Pareto optimal solution for meeting and assessing target from multigroup multistage charging scheme
It is used as charging scheme to be selected.
Specifically, the multiple objective function of Model for Multi-Objective Optimization is expressed as：
minF2=max (Tem)-Tem (0)
F1, F2, F3For the optimization aim in battery charging process, F1When describing all stage chargings in a scheme
Between sum, that is, charge total duration, F2That battery temperature before and after charging is poor, F3 be in charging process can not backheating summation with filling
The ratio between electric gross energy, describes specific energy loss.
The constraint of satisfaction includes：
I1＞ I2＞ ... ＞ Ik
Ub≤Ucutoff Tem≤Temmax
Constraint 1 ensures that electric current successively decreases step by step；Constraint 2 ensures that battery can be charged to SOC and be more than the by the solution strategy that optimization is calculated
One preset value, the first preset value is optimal with 0.99, it is believed that be full of；Constraint 3 requires that battery terminal voltage is no more than in overall process
Blanking voltage (Ucutoff), maximum (Tem of the temperature no more than the second preset value, namely the safety restriction of battery temperaturemax),
The above-mentioned basic restriction considered for cell safety.Optimized variable is the number of stages of multistage constant-current charge, each stage current and each
Phases-time, algorithm target is the overall performance for optimizing multistage constant-current charge in the case where meeting above-mentioned constraints,
Suitable initial charge scheme is searched out in the overall situation as charging scheme to be selected.
Step 105, by decision-making technique from charging scheme to be selected selection target charging scheme.
Specifically, the present embodiment is taken based on entropy coefficient and the last multiple target of the integrated decision-making technique acquisitions of TOPSIS is excellent
Change charging strategy.In multiple target synthtic price index is evaluated, steps in decision-making is as follows：
Step 1：Concentrated in the solution of optimized algorithm, the optimal value of each optimization aim is filtered out；
Wherein, i=1,2 ..., p (P is charging scheme number to be selected)；J=1,2,3.
Optimization aim, which is divided into, to be the bigger the better with the smaller the better two kinds, and target in this problem is all to be the smaller the better；
Step 2：Calculate the target each solved to obtain degree of closeness on optimal value and do normalized, calculate each and refer to
Target entropy weight；
D in formulaijFor element yijNormalization index, EjIt is the entropy of j-th of evaluation index；
Step 3：Entropy weight and the subjective subjective weight (can voluntarily set) determined are combined, each index is finally determined
Weight；
Objective weight：
Final weight：
ω in formulajFor the subjective weight of j targets；
Step 4：The weighted euclidean distance of parameter, distance is more short better, obtains the optimal solution under the decision-making, namely mesh
Mark charging scheme.
The present embodiment intactly devises the scheme that optimization charging strategy is obtained from battery, has obtained multiple target performance more preferable
Charging strategy.The model set up using this method has enough accuracies, and the dynamic that process object can be described well is special
Property, and it is very fast in calculating speed, it is hopeful to be modified to the pattern of on-line optimization.
Although the embodiment of the present invention is the foregoing described, it will be appreciated by those of skill in the art that this is only
For example, protection scope of the present invention is to be defined by the appended claims.Those skilled in the art without departing substantially from
On the premise of the principle and essence of the present invention, various changes or modifications can be made to these embodiments, but these changes and
Modification each falls within protection scope of the present invention.
Claims (8)
1. a kind of charging method of lithium ion battery, it is characterised in that the charging method comprises the following steps：
S1, pass through the equivalent-circuit model of neural network lithium ion battery；The input parameter of the neutral net includes battery
Battery charge state at temperature and different battery temperatures, output parameter includes the component parameters in equivalent-circuit model；
S2, on the basis of equivalent-circuit model couple the heat transfer model of battery free convection to set up initial electric-thermal coupled mode
Type；
S3, design multigroup constant-current charge scheme, mesuring battary is tested according to constant-current charge scheme, the multigroup test number of acquisition
According to；Multigroup test data obtains target electric-thermal coupling model for being fitted initial electric-thermal coupling model；Fitting parameter bag
Include the thermal capacitance and convection transfer rate in the input weight and heat transfer model in neutral net；
Every group of constant-current charge scheme includes charging interval and corresponding current value；Every group of test data was tested including mesuring battary
Terminal voltage, charging current, battery temperature and environment temperature in journey；
S4, design multigroup multistage charging scheme, every group of multistage charging scheme includes charging stage number, each charging stage
Charging interval and corresponding current value；
The multistage charging scheme that the constraints of Model for Multi-Objective Optimization will be met is sequentially input to target electric-thermal coupled mode
Type, obtains result data corresponding with each multistage charging scheme；And result data is performed based on Model for Multi-Objective Optimization
Iteration optimization, chooses one group of Pareto optimal solution as charging scheme to be selected from multigroup multistage charging scheme；
Every group of result data includes the specific energy loss in battery temperature difference and charging process before and after charging total time, charging；
The assessment target of Model for Multi-Objective Optimization includes the energy in battery temperature difference and charging process before and after charging total time, charging
The proportion of goods damageds；
S5, by decision-making technique from the charging scheme to be selected selection target charging scheme.
2. the charging method of lithium ion battery as claimed in claim 1, it is characterised in that the equivalent-circuit model is single order
Element in RC branch road equivalent-circuit models, the single order RC branch road equivalent-circuit models include D.C. resistance, polarization resistance and
Polarization capacity；
The polarization resistance and polarization capacity composition parallel branch are simultaneously connected with the D.C. resistance.
3. the charging method of lithium ion battery as claimed in claim 2, it is characterised in that in step S1In, component parameters include
The capacitance of D.C. resistance resistance, polarization resistance resistance and polarization capacity.
4. the charging method of lithium ion battery as claimed in claim 1, it is characterised in that in step S1In, the neutral net
For ELM neutral nets.
5. the charging method of lithium ion battery as claimed in claim 1, it is characterised in that in step S3In, calculated based on Baron
Method is fitted to initial electric-thermal coupling model.
6. the charging method of lithium ion battery as claimed in claim 1, it is characterised in that in step S5In, multiple-objection optimization mould
The constraints of type includes：
The current value of each charging stage is gradually reduced；
Mesuring battary is charged into SOC more than the first preset value；
The terminal voltage of mesuring battary is less than or equal to blanking voltage in charging process；
Battery temperature is less than or equal to the second preset value in charging process.
7. the charging method of lithium ion battery as claimed in claim 1, it is characterised in that in step S5Before, the charging side
Method also includes：
Model for Multi-Objective Optimization is set up based on multi-objective genetic algorithm.
8. the charging method of lithium ion battery as claimed in claim 1, it is characterised in that step S6Including：
Selected by the decision-making technique based on TOPSIS algorithms and the entropy coefficient of lithium ion battery from the charging scheme to be selected
Target charging scheme；
The entropy coefficient is the terminal voltage of battery and the ratio of battery temperature.
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| CN108199122A (en) * | 2017-12-28 | 2018-06-22 | 哈尔滨工业大学 | Lithium ion battery based on electrochemistry-thermal coupling model is without analysis lithium low-temperature heating method |
| CN108199122B (en) * | 2017-12-28 | 2019-09-13 | 哈尔滨工业大学 | Based on electrochemistry-thermal coupling model lithium ion battery without analysis lithium low-temperature heating method |
| CN109167113A (en) * | 2018-08-24 | 2019-01-08 | 绍兴文理学院 | lithium ion battery charging optimization method |
| CN109346787A (en) * | 2018-09-21 | 2019-02-15 | 北京机械设备研究所 | A kind of electric automobile power battery adaptive optimization charging method |
| CN109802190A (en) * | 2019-01-31 | 2019-05-24 | 南京理工大学 | A kind of battery pack multiple target charging method |
| WO2021077274A1 (en) * | 2019-10-21 | 2021-04-29 | 宁德新能源科技有限公司 | Charging method, electronic device, and storage medium |
| CN111063949B (en) * | 2019-10-25 | 2021-02-09 | 国网黑龙江省电力有限公司电力科学研究院 | Long-life charging method for lithium ion battery |
| CN111063949A (en) * | 2019-10-25 | 2020-04-24 | 国网黑龙江省电力有限公司电力科学研究院 | Long-life charging method for lithium ion battery |
| CN111125894A (en) * | 2019-12-13 | 2020-05-08 | 清华大学 | Method and system for determining optimal capacity of battery |
| CN111082174A (en) * | 2019-12-18 | 2020-04-28 | 湖南大学 | Three-section type charging method for lithium ion battery |
| CN111082174B (en) * | 2019-12-18 | 2023-04-18 | 湖南大学 | Three-section type charging method for lithium ion battery |
| CN113471742A (en) * | 2021-07-13 | 2021-10-01 | 深圳市浮思特科技有限公司 | High-power multi-port wall-entry quick-charging socket and wall-entry quick-charging method |
| CN113471742B (en) * | 2021-07-13 | 2022-03-08 | 深圳市浮思特科技有限公司 | High-power multi-port wall-entry quick-charging socket and wall-entry quick-charging method |
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