CN107942261A - The method of estimation and system of battery charge state - Google Patents
The method of estimation and system of battery charge state Download PDFInfo
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- CN107942261A CN107942261A CN201711479919.3A CN201711479919A CN107942261A CN 107942261 A CN107942261 A CN 107942261A CN 201711479919 A CN201711479919 A CN 201711479919A CN 107942261 A CN107942261 A CN 107942261A
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- 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]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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- 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]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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Abstract
The invention discloses the method for estimation and system of a kind of battery charge state.The method of estimation comprises the following steps：S1, obtain the discharge and recharge data of mesuring battary, and the characteristic variable of the mesuring battary is calculated according to the discharge and recharge data；S2, the characteristic variable of the mesuring battary inputted to correlation model, to obtain the maximum available of the mesuring battary；The quantitative relationship of the correlation model characteristic feature variable and maximum available；S3, obtain the residual capacity of the mesuring battary, and the state-of-charge of the mesuring battary is estimated according to the residual capacity and the maximum available.The present invention realizes the current maximum available of estimation on line battery by correlation model, realizes that the SOC of battery Life cycle is calculated on this basis, solves the problems, such as battery capacity decay SOC estimation errors become larger.And the algorithm of the present invention is simple and practical, complexity is low, suitable for embedded BMS systems.
Description
Technical field
The present invention relates to battery technology field, more particularly to a kind of battery charge state suitable for embedded BMS systems
Method of estimation and system.
Background technology
Battery nuclear power state (SOC) is most important parameter in battery management system, and the accuracy of SOC estimations is to system
Stable operation and security are of great significance.SOC is defined as follows：SOC=residual capacities/maximum available, generally will
Battery dispatches from the factory capacity as maximum available, and estimates SOC.To dispatch from the factory the moment, maximum available is equal to the capacity that dispatches from the factory, with
Battery uses, and maximum available can be less than the capacity that dispatches from the factory.
In the prior art, SOC estimation method seldom considers influence of the cycle life to capacity, can not determine battery capacity with
The quantitative relationship of maximum capacity, and the capacity of lithium battery can decay with the increase of access times, it is simple to be held using remaining
Amount and battery dispatch from the factory estimate of the ratio as SOC of capacity, this way when battery dispatches from the factory with higher accuracy, with
Circulating battery using capacity progressively to decay, the estimate error of SOC can be increasing.Also have in the prior art old for battery
The SOC estimation method of change, but it needs to carry out substantial amounts of data computing, and method is complicated, due to the calculating of embedded system BMS
Limited with storage capacity, the above method is not suitable for embedded BMS systems.
The content of the invention
Or the technical problem to be solved in the present invention is in order to overcome the SOC estimation method of the prior art in the presence of due to not examining
To consider influences of the cycle life to capacity cause the state-of-charge that cannot accurately estimate battery, or method is complicated, operand is greatly
Defect, there is provided a kind of accuracy is high, algorithm complex is low, suitable for the estimation side of the battery charge state of embedded BMS systems
Method and system.
The present invention is to solve above-mentioned technical problem by following technical proposals：
A kind of method of estimation of battery charge state, the method for estimation comprise the following steps：
S1, obtain the discharge and recharge data of mesuring battary, and the spy of the mesuring battary is calculated according to the discharge and recharge data
Levy variable；
S2, the characteristic variable of the mesuring battary inputted to correlation model, can to obtain the maximum of the mesuring battary
Use capacity；
The quantitative relationship of the correlation model characteristic feature variable and maximum available；
S3, obtain the residual capacity of the mesuring battary, and estimated according to the residual capacity and the maximum available
Count the state-of-charge of the mesuring battary.
It is preferred that step S2Before, the method for estimation further includes the step of establishing correlation model；
Described the step of establishing correlation model, specifically include：
S11, obtain the life time decay data of multiple batteries identical with the model of the mesuring battary；
S12, according to the life time decay data build battery characteristic variable and calculate capacity data sequence；
S13, based on capacity data sequence and characteristic variable described in least square fitting, to build correlation model.
It is preferred that step S12Before, further include：
Based on digital filtering to the life time decay data smoothing denoising.
It is preferred that the characteristic variable includes：Deng pressure drop electric discharge ampere-hour and/or wait pressure drop charging ampere-hour；
It is described that to wait pressure drop electric discharge ampere-hour to characterize corresponding after pressure drop electric discharge according to waiting by battery in each charging-discharging cycle
Ampere-hour number；
It is described that to wait pressure drop charging ampere-hour to characterize corresponding after pressure drop charging according to waiting by battery in each charging-discharging cycle
Ampere-hour number.
It is preferred that step S3In, the step of residual capacity for obtaining the mesuring battary, specifically includes：
The capacity initial value of the mesuring battary is calculated according to SOC-OCV curves；
The volume change value of the mesuring battary is calculated based on current integration method；
The residual capacity is calculated according to the capacity initial value and the volume change value.
It is preferred that the discharge and recharge data and the life time decay data include following parameter：
In charge and discharge process, voltage, electric current and the temperature of battery.
The present invention also provides a kind of estimating system of battery charge state, the estimating system includes：
Data acquisition module, institute is calculated for obtaining the discharge and recharge data of mesuring battary, and according to the discharge and recharge data
State the characteristic variable of mesuring battary；
Computing module, for inputting the characteristic variable of the mesuring battary to correlation model, to obtain the electricity to be measured
The maximum available in pond；
The quantitative relationship of the correlation model characteristic feature variable and maximum available；
The computing module is additionally operable to obtain the residual capacity of the mesuring battary, and according to the residual capacity and described
Maximum available estimates the state-of-charge of the mesuring battary.
It is preferred that the estimating system further includes：Model building module；
The model building module, specifically includes：
Data capture unit, for obtaining the life time decay number of the multiple batteries identical with the model of the mesuring battary
According to；
Computing unit, for building the characteristic variable of battery according to the life time decay data and calculating capacity data sequence
Row；
Model construction unit, for based on capacity data sequence and characteristic variable described in least square fitting, with structure
Correlation model.
It is preferred that the model building module further includes：
Data processing unit, for based on digital filtering to the life time decay data smoothing denoising.
It is preferred that the characteristic variable includes：Deng pressure drop electric discharge ampere-hour and/or wait pressure drop charging ampere-hour；
It is described that to wait pressure drop electric discharge ampere-hour to characterize corresponding after pressure drop electric discharge according to waiting by battery in each charging-discharging cycle
Ampere-hour number；
It is described that to wait pressure drop charging ampere-hour to characterize corresponding after pressure drop charging according to waiting by battery in each charging-discharging cycle
Ampere-hour number.
It is preferred that the computing module is specifically used for the capacity initial value that the mesuring battary is calculated according to SOC-OCV curves,
The volume change value of the mesuring battary is calculated based on current integration method, and according to the capacity initial value and the volume change value
Calculate the residual capacity.
It is preferred that the discharge and recharge data and the life time decay data include following parameter：
In charge and discharge process, voltage, electric current and the temperature of battery.
The positive effect of the present invention is：The present invention by correlation model realize estimation on line battery it is current most
Big active volume, realizes that the SOC of battery Life cycle is calculated on this basis, solves as battery capacity decay SOC estimates
Calculate the problem of error becomes larger.And the algorithm of the present invention is simple and practical, complexity is low, suitable for embedded BMS systems.
Brief description of the drawings
Fig. 1 is the flow chart of the method for estimation of the battery charge state of the embodiment of the present invention 1.
Fig. 2 is circulating battery number and available capacity and waits pressure drop ampere-hour to change comparison diagram.
Fig. 3 is that capacity data sequence and feature change are fitted in the method for estimation of the battery charge state of the embodiment of the present invention 1
The result schematic diagram of amount.
Fig. 4 is the module diagram of the method for estimation of the battery charge state of the embodiment of the present invention 2.
Embodiment
The present invention is further illustrated below by the mode of embodiment, but does not therefore limit the present invention to the reality
Apply among a scope.
Embodiment 1
As shown in Figure 1, the method for estimation of the battery charge state of the present embodiment, comprises the following steps：
Step 101, the discharge and recharge data for obtaining mesuring battary, and become according to the feature of discharge and recharge data calculating mesuring battary
Amount.
Wherein, discharge and recharge data include following parameter：In charge and discharge process, voltage, electric current and the temperature of battery.Above-mentioned ginseng
Number has relevance with cell degradation, is easy to measure, the real time monitoring by BMS systems (battery management system) to mesuring battary
It can obtain.
Characteristic variable includes：Deng pressure drop ampere-hour.Include Deng pressure drop ampere-hour：Deng pressure drop electric discharge ampere-hour and/or pressure drop is waited to charge
Ampere-hour.Characterized battery Deng pressure drop electric discharge ampere-hour in each charging-discharging cycle according to waiting corresponding ampere-hour number after pressure drop electric discharge,
Also will each voltage drops to electric discharge ampere-hour corresponding to another low-voltage from a high voltage in discharge cycle.Deng pressure drop
The ampere-hour that charges is characterized battery in each charging-discharging cycle according to waiting corresponding ampere-hour number after pressure drop charging.For cycle charge discharge
For the lithium ion battery of electricity, referring to Fig. 2, in figure the pressure drop such as L1 characterizations charging ampere-hour with cycle-index variation rule curve,
L2 characterization etc. pressure drop electric discharge ampere-hour with cycle-index variation rule curve, L3 characterize available capacity with cycle-index change advise
Restrain curve.In discharge cycle, cell voltage drops to another low voltage required time from a high voltage, with
Being continuously increased for discharge and recharge number and reduction trend is presented, i.e., with the actual capacity of lithium ion battery there are certain correlation,
And it is easy to by directly monitoring structure.Therefore, in the present embodiment selection etc. pressure drop electric discharge ampere-hour and/or wait pressure drop charging ampere-hour make
Variable is characterized, wherein again to wait pressure drop to discharge ampere-hour as optimal selection, the maximum available of battery is calculated with it.Need
It is bright, before the characteristic variable for calculating mesuring battary, discharge and recharge data prediction can be carried out, is gone by the way of digital filtering
Except abnormal data, to ensure the accuracy calculated.
Step 102, input the characteristic variable of mesuring battary to correlation model, is held with obtaining the maximum of mesuring battary and can use
Amount.
Wherein, the quantitative relationship of correlation model characteristic feature variable and maximum available.The input parameter of correlation model
Variable is characterized, output parameter is maximum available.
In the present embodiment, before step 102, method of estimation further includes the step of establishing correlation model.
The step of establishing correlation model, specifically includes：
Step 100-1, the life time decay data of the acquisition multiple batteries identical with mesuring battary model.
Step 100-2, build the characteristic variable of battery according to life time decay data and calculate capacity data sequence.
Wherein, life time decay data include following parameter：In charge and discharge process, voltage, electric current and the temperature of battery.
Before step 100-2, further include：Based on digital filtering to life time decay data smoothing denoising.To screen out exception
Value and problematic data, improve the accuracy of correlation model.
Step 100-3, based on least square fitting capacity data sequence and characteristic variable, to build correlation model.
Step 100-3 is specifically included：One order polynomial plan is carried out to characteristic variable and battery capacity using least square method
Close or quadratic polynomial is fitted.
Obtained polynomial fitting is respectively：
Fitting polynomial formulas：
CAP=1.0245C+65.7621；
CAP is available capacity, and C is characterized variable (or health factor)；
Error mean Ex=0, error to standard deviation σ=0.8216.
Fitting of a polynomial effect curve figure, referring to the L4 in Fig. 3.Wherein, the former data in Fig. 3 namely pressure drop is waited
The numerical value for ampere-hour of discharging.
Quadratic polynomial fitting formula：
CAP=0.0129C2+0.5955C+69.0271；
CAP is available capacity, and C is characterized variable；
Error mean Ex=0, error to standard deviation σ=0.7954.
The fitting effect curve map of quadratic polynomial, referring to the L5 in Fig. 3.
In the present embodiment, multiple initial characteristics variables may be selected before establishing correlation model, for example, waiting pressure drop electric discharge peace
When, etc. ampere-hour discharge voltage it is poor, etc. pressure drop charging ampere-hour and wait ampere-hour charging voltage poor；Characterization poor etc. ampere-hour discharge voltage is each
In charging-discharging cycle by battery according to etc. voltage difference corresponding after ampere-hour electric discharge；The each charge and discharge of characterization poor etc. ampere-hour charging voltage
In the electric cycle by battery according to etc. voltage difference corresponding after ampere-hour charging.Analyze the capacity data of features described above variable and battery
Whether the correlation between sequence, judgement have linear relationship therebetween, can specifically pass through Pearson relevant function method meters
The related coefficient between capacity data sequence and characteristic variable is calculated, to verify its correlation.Because wait pressure drop charging ampere-hour health because
The Pearson correlation coefficient r=0.9694 of son；As can be seen that it is stronger to wait pressure drop charging ampere-hour health factor and capacity to have
Correlation, thus using etc. pressure drop charging ampere-hour as characterize maximum available characteristic variable.Obtained correlation model can be accurate
The quantitative relationship of true characteristic feature variable and maximum available.
Step 103, the residual capacity for obtaining mesuring battary, and electricity to be measured is estimated according to residual capacity and maximum available
The state-of-charge in pond.
In step 103, the step of residual capacity for obtaining mesuring battary, specifically includes：
Step 103-1, the capacity initial value of mesuring battary is calculated according to SOC-OCV curves.
Step 103-2, the volume change value of mesuring battary is calculated based on current integration method.
Step 103-3, residual capacity is calculated according to capacity initial value and volume change value.
Specifically, residual capacity Q ' is calculated according to equation below in step 103-3：
Q '=(Q0+Q1)；
And then state-of-charge SOC can be calculated according to equation below in step 103：
SOC=(Q0+Q1)/Qmax；
Wherein, Q0For capacity initial value, Q1For volume change value, QmaxFor maximum available.
Illustrate the standard that battery charge state SOC is calculated using the method for estimation of the present embodiment below by way of an instantiation
True property：
Selection battery dispatches from the factory a battery of the capacity for 100AH as mesuring battary, after running a period of time, according to survey
The fitting formula obtained in the voltage obtained, current data and above-mentioned steps 100-3 is measured, estimating the current of mesuring battary can
It is dispatch from the factory the 90% of capacity, i.e. Q with maximum capacitymax=90AH.Capacity initial value Q is calculated according to SOC-OCV curves0=40AH, profit
Volume change value Q is calculated with current integration method1=20AH, the state-of-charge SOC=(Q of mesuring battary0+Q1)/Qmax=(40+
20)/90=66.7%.
And if conventionally calculate the SOC of battery, since it is without considering influence of the cycle life to capacity, then SOC
=(Q0+Q1)/QFactory-said value=(40+20)/100=60.0%.As it can be seen that conventional method SOC meetings output in cell degradation is bigger
Evaluated error, and error is directly proportional with cell degradation degree.
In the present embodiment, the current maximum available of estimation on line battery is realized by correlation model, it is basic herein
On realize battery Life cycle SOC calculate, solve the problems, such as with battery capacity decay SOC estimation errors become larger.And
Algorithm is simple and practical, and complexity is low, suitable for embedded BMS systems.
Embodiment 2
As shown in figure 4, the estimating system of the battery charge state of the present embodiment includes：Data acquisition module 1, computing module
2 and model building module 3.
Data acquisition module 1 is used for the discharge and recharge data for obtaining mesuring battary, and calculates electricity to be measured according to discharge and recharge data
The characteristic variable in pond.Wherein, discharge and recharge data include following parameter：In charge and discharge process, voltage, electric current and the temperature of battery.
Characteristic variable includes：Deng pressure drop electric discharge ampere-hour and/or wait pressure drop charging ampere-hour.It is each Deng pressure drop electric discharge ampere-hour characterization
By battery according to ampere-hour number corresponding after grade pressure drop electric discharge in charging-discharging cycle.Each discharge and recharge is characterized Deng pressure drop charging ampere-hour
By battery according to ampere-hour number corresponding after grade pressure drop charging in cycle.In the present embodiment selection etc. pressure drop electric discharge ampere-hour and/or
Ampere-hour is charged as characteristic variable Deng pressure drop, wherein again to wait pressure drop to discharge ampere-hour as optimal selection, battery is calculated most with it
Big active volume.
Model building module 3 is used to establish correlation module.Correlation model characteristic feature variable and maximum available are determined
Magnitude relation, the input parameter of correlation model are characterized variable, and output parameter is maximum available.
Model building module 3 specifically includes：Data capture unit 31, computing unit 32 and model construction unit 33.Data
Acquiring unit is used for the life time decay data for obtaining the multiple batteries identical with the model of mesuring battary.Wherein, discharge and recharge data
Include following parameter with life time decay data：In charge and discharge process, voltage, electric current and the temperature of battery.Computing unit is used for root
The characteristic variable of battery is built according to life time decay data and calculates capacity data sequence.Model construction unit is used to be based on a most young waiter in a wineshop or an inn
Multiplication is fitted capacity data sequence and characteristic variable, to build correlation model.Specifically, model construction unit utilizes least square
Method carries out a fitting of a polynomial to characteristic variable and battery capacity or quadratic polynomial is fitted, to build correlation model.
In the present embodiment, model building module further includes：Data processing unit 34.Data processing unit is used for based on numeral
Filter method is to life time decay data smoothing denoising.So as to which computing unit is built according to by the life time decay data of smoothing denoising
The characteristic variable of battery simultaneously calculates capacity data sequence, improves the accuracy of correlation model.
Correlation model can be stored in BMS systems after establishing, for the current nuclear power state of computing module estimation battery
When call at any time, to calculate the current maximum available of battery.
Computing module 2 is used to input the characteristic variable of mesuring battary to correlation model, to obtain the maximum of mesuring battary
Active volume Qmax, and the residual capacity of mesuring battary is obtained, mesuring battary is estimated according to residual capacity and maximum available
State-of-charge.
Specifically, computing module calculates the capacity initial value Q of mesuring battary according to SOC-OCV curves0, based on current integration method
Calculate the volume change value Q of mesuring battary1, and residual capacity is calculated according to capacity initial value and volume change value, and then calculate lotus
Electricity condition SOC, specific formula are as follows：
SOC=(Q0+Q1)/Qmax。
In the present embodiment, the current maximum available of estimation on line battery is realized by correlation model, it is basic herein
On realize battery Life cycle SOC calculate, solve the problems, such as with battery capacity decay SOC estimation errors become larger.And
Algorithm is simple and practical, and complexity is low, suitable for embedded BMS systems.
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 of the present invention and essence, 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 (12)
1. a kind of method of estimation of battery charge state, it is characterised in that the method for estimation comprises the following steps：
S1, obtain the discharge and recharge data of mesuring battary, and the feature for calculating according to the discharge and recharge data mesuring battary becomes
Amount；
S2, the characteristic variable of the mesuring battary inputted to correlation model, held with obtaining the maximum of the mesuring battary and can use
Amount；
The quantitative relationship of the correlation model characteristic feature variable and maximum available；
S3, obtain the residual capacity of the mesuring battary, and according to the residual capacity and maximum available estimation
The state-of-charge of mesuring battary.
2. the method for estimation of battery charge state as claimed in claim 1, it is characterised in that step S2Before, the estimation side
Method further includes the step of establishing correlation model；
Described the step of establishing correlation model, specifically include：
S11, obtain the life time decay data of multiple batteries identical with the model of the mesuring battary；
S12, according to the life time decay data build battery characteristic variable and calculate capacity data sequence；
S13, based on capacity data sequence and characteristic variable described in least square fitting, to build correlation model.
3. the method for estimation of battery charge state as claimed in claim 2, it is characterised in that step S12Before, further include：
Based on digital filtering to the life time decay data smoothing denoising.
4. the method for estimation of the battery charge state as described in any one in claim 1-3, it is characterised in that the feature
Variable includes：Deng pressure drop electric discharge ampere-hour and/or wait pressure drop charging ampere-hour；
It is described to wait pressure drop electric discharge ampere-hour to characterize battery in each charging-discharging cycle according to waiting corresponding ampere-hour after pressure drop electric discharge
Number；
It is described to wait pressure drop charging ampere-hour to characterize battery in each charging-discharging cycle according to waiting corresponding ampere-hour after pressure drop charging
Number.
5. the method for estimation of battery charge state as claimed in claim 1, it is characterised in that step S3In, obtain described to be measured
The step of residual capacity of battery, specifically includes：
The capacity initial value of the mesuring battary is calculated according to SOC-OCV curves；
The volume change value of the mesuring battary is calculated based on current integration method；
The residual capacity is calculated according to the capacity initial value and the volume change value.
6. the method for estimation of battery charge state as claimed in claim 2, it is characterised in that discharge and recharge data and described
Life time decay data include following parameter：
In charge and discharge process, voltage, electric current and the temperature of battery.
7. a kind of estimating system of battery charge state, it is characterised in that the estimating system includes：
Data acquisition module, for obtaining the discharge and recharge data of mesuring battary, and is treated according to calculating the discharge and recharge data
Survey the characteristic variable of battery；
Computing module, for inputting the characteristic variable of the mesuring battary to correlation model, to obtain the mesuring battary
Maximum available；
The quantitative relationship of the correlation model characteristic feature variable and maximum available；
The computing module is additionally operable to obtain the residual capacity of the mesuring battary, and according to the residual capacity and the maximum
Active volume estimates the state-of-charge of the mesuring battary.
8. the estimating system of battery charge state as claimed in claim 7, it is characterised in that the estimating system further includes：
Model building module；
The model building module, specifically includes：
Data capture unit, for obtaining the life time decay data of the multiple batteries identical with the model of the mesuring battary；
Computing unit, for building the characteristic variable of battery according to the life time decay data and calculating capacity data sequence；
Model construction unit, for based on capacity data sequence and characteristic variable described in least square fitting, to build correlation
Model.
9. the estimating system of battery charge state as claimed in claim 8, it is characterised in that the model building module also wraps
Include：
Data processing unit, for based on digital filtering to the life time decay data smoothing denoising.
10. the estimating system of the battery charge state as described in any one in claim 7-9, it is characterised in that the spy
Sign variable includes：Deng pressure drop electric discharge ampere-hour and/or wait pressure drop charging ampere-hour；
It is described to wait pressure drop electric discharge ampere-hour to characterize battery in each charging-discharging cycle according to waiting corresponding ampere-hour after pressure drop electric discharge
Number；
It is described to wait pressure drop charging ampere-hour to characterize battery in each charging-discharging cycle according to waiting corresponding ampere-hour after pressure drop charging
Number.
11. the estimating system of battery charge state as claimed in claim 7, it is characterised in that the computing module is specifically used
In the capacity initial value that the mesuring battary is calculated according to SOC-OCV curves, the mesuring battary is calculated based on current integration method
Volume change value, and the residual capacity is calculated according to the capacity initial value and the volume change value.
12. the estimating system of battery charge state as claimed in claim 8, it is characterised in that the discharge and recharge data and institute
Stating life time decay data includes following parameter：
In charge and discharge process, voltage, electric current and the temperature of battery.
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| CN110058177A (en) * | 2019-05-06 | 2019-07-26 | 奇瑞新能源汽车技术有限公司 | A kind of power battery electricity SOC modification method |
| CN110361664A (en) * | 2019-08-23 | 2019-10-22 | 清华四川能源互联网研究院 | Battery detection method, battery monitoring device and charging equipment |
| CN111025168A (en) * | 2019-11-29 | 2020-04-17 | 淮南师范学院 | Battery health state monitoring device and battery state of charge intelligent estimation method |
| CN115219906A (en) * | 2022-06-27 | 2022-10-21 | 武汉理工大学 | Multi-model fusion battery state of charge prediction method and system based on GA-PSO optimization |
| EP4198533A4 (en) * | 2020-08-13 | 2024-03-27 | Terawatt Technology K.K. | Information processing device, information processing method, and program |
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