CN108445401A - On-line Estimation method, electronic device and the storage medium of battery charge state SOC - Google Patents
On-line Estimation method, electronic device and the storage medium of battery charge state SOC Download PDFInfo
- Publication number
- CN108445401A CN108445401A CN201810134609.6A CN201810134609A CN108445401A CN 108445401 A CN108445401 A CN 108445401A CN 201810134609 A CN201810134609 A CN 201810134609A CN 108445401 A CN108445401 A CN 108445401A
- Authority
- CN
- China
- Prior art keywords
- battery
- soc
- equation
- charge state
- battery charge
- 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.)
- Pending
Links
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]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Secondary Cells (AREA)
Abstract
The present invention relates to a kind of On-line Estimation method, electronic device and the storage medium of battery charge state SOC, this method includes：Obtain the open-circuit voltage U of the battery obtained based on pulse charge-discharge testocvWith battery charge state SOCkQuantitative function relationship Uocv(SOCk), and determine the initial parameter value based on Order RC equivalent-circuit model；Battery model function is established based on the Order RC equivalent-circuit model, according to the battery model function, quantitative function relationship Uocv(SOCk) and battery charge state SOC defined functions obtain battery charge state SOC and polarizing voltage state equation and observational equation；The state equation and observational equation of the corresponding inside battery parameter of the initial parameter value are obtained based on the battery model function；Carry out battery charge state SOC On-line Estimations.The estimation on line precision height of the battery charge state SOC of the present invention, workload are small.
Description
Technical field
The present invention relates to battery technology field more particularly to a kind of On-line Estimation method of battery charge state SOC, electronics
Device and storage medium.
Background technology
Currently, battery management system (BMS) be electric vehicle and other need to use a certain number of accumulators productions
Product in the process of running realize battery the important tool of management and control.Due to use there is some difference the property of accumulator,
Therefore the BMS functions of different vendor's production are not quite identical, but its basic function should include：Characteristic parameter in the process of running
Monitoring, energy hole management, real-time status analysis, fault pre-alarming and safeguard protection, the display of information and storage etc..
Wherein, the analysis of accumulator real-time status includes battery charge state (State of Charge, SOC) estimation, is good for
Health state (SOH) estimates that power rating (SOP) is estimated.The safeguard protection of accumulator is to be directed to its real-time running state, in conjunction with
Control strategy, the generation for taking certain measure to prevent power battery overcurrent, cross discharge charge, overheat condition, and sent out in the above situation
Necessary safeguard measure is taken to ensure product and personal safety when raw.
For the BMS applied to electric vehicle, to ensure that the battery performance in electric vehicle is good, extend accumulator
Group service life, it is also desirable to understand the battery charge state SOC of accumulator accurately and in time.Only for battery charge state SOC
It can estimate, cannot measure, accurate estimation needs practical algorithm.Practical SOC estimation method includes：1) current integration method, should
It is owned by France to will appear deviation accumulation in open loop operation estimation result, cause error gradually to increase；2) ampere-hour integral and open-circuit voltage phase
In conjunction with combined method, by the alignment of relationships ampere-hour method of open-circuit voltage and SOC occur accumulated error, but when accumulator stand
Time is shorter to be will appear open-circuit voltage and tables look-up inaccurate phenomenon；3) Kalman filtering method can estimate SOC in real time, and precision is higher,
But traditional Kalman filtering method needs do a large amount of experiment and obtain battery model parameter.In view of the drawbacks of the prior art, one is provided
The battery charge state SOC estimation on line methods that kind precision is high, workload is small, which become, problem to be solved.
Invention content
The purpose of the present invention is to provide a kind of On-line Estimation method, electronic device and the storages of battery charge state SOC
Medium, it is desirable to provide a kind of battery charge state SOC estimation on line methods that precision is high, workload is small.
To achieve the above object, the present invention provides a kind of On-line Estimation method of battery charge state SOC, and this method includes
Following steps：
S1 obtains the open-circuit voltage U of the battery obtained based on pulse charge-discharge testocvWith battery charge state SOCk's
Quantitative function relationship Uocv(SOCk), and determine the initial parameter value based on Order RC equivalent-circuit model, the initial parameter packet
Include ohmic internal resistance R0, the first polarization resistance RP1, the second polarization resistance RP2, the first polarization capacity Cp1And the second polarization capacity Cp2；
S2 establishes battery model function based on the Order RC equivalent-circuit model, according to the battery model function, quantitative letter
Number relationship Uocv(SOCk) and battery charge state SOC defined functions obtain battery charge state SOC and polarizing voltage state
Equation and observational equation；
S3, based on the battery model function obtain the corresponding inside battery parameter of the initial parameter value state equation and
Observational equation；
S4, using the battery charge state SOC and the state equation and observational equation of polarizing voltage as binary channels without mark
The first layer of Kalman filtering algorithm DUKF, and using the state equation of the inside battery parameter and observational equation as the bilateral
The second layer of road Unscented kalman filtering algorithm DUKF carries out battery charge state SOC On-line Estimations.
Preferably, this method further includes：
Battery charge state SOC On-line Estimation result datas are obtained, by the battery charge state SOC On-line Estimation results
Data substitute into battery model function, to calculate the emulation terminal voltage of battery, will emulate the battery-end electricity of terminal voltage and actual measurement
Pressure is compared, to verify the accuracy of initial parameter value.
Preferably, the battery model function includes：
Wherein, in Order RC equivalent-circuit model, ULFor terminal voltage, UocvFor open-circuit voltage, I is load current, UP1、
UP2For the corresponding polarizing voltage in two circuits,For the derivation function to polarizing voltage.
Preferably, the battery charge state SOC and the state equation and observational equation of polarizing voltage include：
Wherein, [Up1,k,Up2,k,SOCk]TFor state equation, [UL,k] it is observational equation, η is the efficiency for charge-discharge of battery,
CcellFor the active volume of battery, ikFor load current, k is the time, and Δ t is the time interval of experimental data, wkFor systematic procedure
Noise jamming, vkFor systematic survey noise jamming.
Preferably, the state equation and observational equation of the inside battery parameter include：
θk=θk-1+γk-1
Wherein, θk=θk-1+γk-1For state equation,UL,k=Uocv
(SOCk-1)-UP1,K-1-UP2,K-1-ik-1R0,k-1For observational equation, γkIndicate that small disturbance, k are the time, Δ t is experimental data
Time interval, ikFor load current.
To achieve the above object, the present invention also provides a kind of electronic device, the electronic device include memory and with institute
The processor of memory connection is stated, the processing system that can be run on the processor, the place are stored in the memory
Reason system realizes following steps when being executed by the processor：
Obtain the open-circuit voltage U of the battery obtained based on pulse charge-discharge testocvWith battery charge state SOCkQuantify
Functional relation Uocv(SOCk), and determining the initial parameter value based on Order RC equivalent-circuit model, the initial parameter includes Europe
Nurse internal resistance R0, the first polarization resistance RP1, the second polarization resistance RP2, the first polarization capacity Cp1And the second polarization capacity Cp2；
Battery model function is established based on the Order RC equivalent-circuit model, according to battery model function, the quantitative function
Relationship Uocv(SOCk) and battery charge state SOC defined functions obtain the state side of battery charge state SOC and polarizing voltage
Journey and observational equation；
State equation and the sight of the corresponding inside battery parameter of the initial parameter value are obtained based on the battery model function
Survey equation；
Using the state equation and observational equation of the battery charge state SOC and polarizing voltage as binary channels without mark karr
The first layer of graceful filtering algorithm DUKF, and using the state equation of the inside battery parameter and observational equation as the binary channels without
The second layer of mark Kalman filtering algorithm DUKF carries out battery charge state SOC On-line Estimations.
Preferably, the battery model function includes：
Wherein, in Order RC equivalent-circuit model, ULFor terminal voltage, UocvFor open-circuit voltage, I is load current, UP1、
UP2For the corresponding polarizing voltage in two circuits,For the derivation function to polarizing voltage.
Preferably, the battery charge state SOC and the state equation and observational equation of polarizing voltage include：
Wherein, [Up1,k,Up2,k,SOCk]TFor state equation, [UL,k] it is observational equation, η is the efficiency for charge-discharge of battery,
CcellFor the active volume of battery, ikFor load current, k is the time, and Δ t is the time interval of experimental data, wkFor systematic procedure
Noise jamming, vkFor systematic survey noise jamming.
Preferably, the state equation and observational equation of the inside battery parameter include：
θk=θk-1+γk-1
Wherein, θk=θk-1+γk-1For state equation,UL,k=Uocv
(SOCk-1)-UP1,K-1-UP2,K-1-ik-1R0,k-1For observational equation, γkIndicate that small disturbance, k are the time, Δ t is experimental data
Time interval, ikFor load current.
The present invention also provides a kind of computer readable storage medium, processing is stored on the computer readable storage medium
The step of system, the processing system realizes the On-line Estimation method of above-mentioned battery charge state SOC when being executed by processor.
The present invention is directed to the deficiency of battery charge state SOC estimation method in the prior art, especially for legacy card
The defect of Kalman Filtering method, the present invention is based on the battery charge state SOC of binary channels Unscented kalman filtering algorithm DUKF is online
Method of estimation does not need to the functional relation for accurately providing fuel cell modelling parameter (i.e. inside battery parameter) and SOC, it is only necessary to
Know the rough initial value of fuel cell modelling parameter can (i.e. initial parameter value), a large amount of experiment need not be done and obtain fuel cell modelling
Parameter, it is simple and quick, the estimation on line of battery charge state SOC is not only effectively completed, also realizes and inside battery is joined
Several estimation on line has many advantages, such as that precision is high, workload is small.
Description of the drawings
Fig. 1 is the schematic diagram of the hardware structure of electronic device preferred embodiment of the present invention；
Fig. 2 is the flow diagram of one embodiment of On-line Estimation method of battery charge state SOC of the present invention；
Fig. 3 is the circuit diagram for the Order RC equivalent-circuit model that the present invention establishes；
Fig. 4 is the curve graph that the Order RC equivalent-circuit model based on Fig. 3 carries out the voltage-to-current that HPPC is tested；
Fig. 5 is the curve graph of voltage shown in Fig. 4.
Specific implementation mode
In order to make the purpose , technical scheme and advantage of the present invention be clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that described herein, specific examples are only used to explain the present invention, not
For limiting the present invention.Based on the embodiments of the present invention, those of ordinary skill in the art are not before making creative work
The every other embodiment obtained is put, shall fall within the protection scope of the present invention.
It should be noted that the description for being related to " first ", " second " etc. in the present invention is used for description purposes only, and cannot
It is interpreted as indicating or implying its relative importance or implicitly indicates the quantity of indicated technical characteristic.Define as a result, " the
One ", the feature of " second " can explicitly or implicitly include at least one of the features.In addition, the skill between each embodiment
Art scheme can be combined with each other, but must can be implemented as basis with those of ordinary skill in the art, when technical solution
Will be understood that the combination of this technical solution is not present in conjunction with there is conflicting or cannot achieve when, also not the present invention claims
Protection domain within.
As shown in fig.1, being the schematic diagram of the hardware structure of electronic device preferred embodiment of the present invention, the electronic device
1 be it is a kind of can be according to the instruction for being previously set or storing, the automatic equipment for carrying out numerical computations and/or information processing.Institute
State electronic device 1 can be computer, can also be single network server, multiple network servers composition server group or
The cloud that a large amount of hosts or network server is made of of the person based on cloud computing, wherein cloud computing are one kind of Distributed Calculation,
A super virtual computer being made of the computer collection of a group loose couplings.
In the present embodiment, electronic device 1 may include, but be not limited only to, and can be in communication with each other connection by system bus
Memory 11, processor 12, network interface 13, memory 11 are stored with the processing system that can be run on the processor 12.It needs
, it is noted that Fig. 1 illustrates only the electronic device 1 with component 11-13, it should be understood that being not required for implementing all
The component shown, the implementation that can be substituted is more or less component.
Wherein, memory 11 includes memory and the readable storage medium storing program for executing of at least one type.Inside save as the fortune of electronic device 1
Row provides caching；Readable storage medium storing program for executing can be if flash memory, hard disk, multimedia card, card-type memory are (for example, SD or DX memories
Deng), random access storage device (RAM), static random-access memory (SRAM), read-only memory (ROM), electric erasable can compile
Journey read-only memory (EEPROM), programmable read only memory (PROM), magnetic storage, disk, CD etc. it is non-volatile
Storage medium.In some embodiments, readable storage medium storing program for executing can be the internal storage unit of electronic device 1, such as the electronics
The hard disk of device 1；In further embodiments, which can also be that the external storage of electronic device 1 is set
Plug-in type hard disk that is standby, such as being equipped on electronic device 1, intelligent memory card (Smart Media Card, SMC), secure digital
(Secure Digital, SD) blocks, flash card (Flash Card) etc..In the present embodiment, the readable storage medium storing program for executing of memory 11
The operating system and types of applications software of electronic device 1, such as the place in one embodiment of the invention are installed on commonly used in storage
The program code etc. of reason system.It has exported or will export each in addition, memory 11 can be also used for temporarily storing
Class data.
The processor 12 can be in some embodiments central processing unit (Central Processing Unit,
CPU), controller, microcontroller, microprocessor or other data processing chips.The processor 12 is commonly used in the control electricity
The overall operation of sub-device 1, such as execute and carry out data interaction with other equipment or communicate relevant control and processing etc..This
In embodiment, the processor 12 is used to run the program code stored in the memory 11 or processing data, such as transports
Row processing system etc..
The network interface 13 may include radio network interface or wired network interface, which is commonly used in
Communication connection is established between the electronic device 1 and other electronic equipments.In the present embodiment, network interface 13 is mainly used for will be electric
Sub-device 1 is connected with one or more equipment, established between electronic device 1 and one or more equipment data transmission channel and
Communication connection, to obtain relevant data.
The processing system is stored in memory 11, including it is at least one be stored in it is computer-readable in memory 11
Instruction, at least one computer-readable instruction can be executed by processor device 12, the method to realize each embodiment of the application；With
And the function that at least one computer-readable instruction is realized according to its each section is different, can be divided into different logic moulds
Block.
In one embodiment, battery charge state of the present invention can be realized when above-mentioned processing system is executed by the processor 12
The step of On-line Estimation method of SOC, as shown in Fig. 2, including：
Step S1 obtains the open-circuit voltage U of the battery obtained based on pulse charge-discharge testocvWith battery charge state
SOCkQuantitative function relationship Uocv(SOCk), and determine the initial parameter value based on Order RC equivalent-circuit model, it is described initial
Parameter includes ohmic internal resistance R0, polarization resistance Rp1、Rp2And polarization capacity Cp1、Cp2；
First, determine that charging system and discharge system, this example are the ternary of 35Ah with rated capacity according to battery types
Material lithium ion battery is experimental subjects, and certainly, other batteries can also.Battery will be in room temperature shape before standard charging
State should be stored at room temperature depending on different state of temperature regulations is different when battery is in other state of temperatures before standard charging
Processing method：2~4h of quiescence in high temperature, 8~16h of stand at low temperature.Two aspects of acquisition methods Main Basiss of charge and discharge system, one
It is to be provided by the instructions book of battery producer, second is that carrying out charge and discharge system according to the relevant regulations of power accumulator new national standard
Determination.
The ternary material battery that the present embodiment uses battery charge state SOC and open-circuit voltage U in charge and discharge processocv
Between there are certain mapping relations.Specific experiment scheme is：Experiment ternary material battery is fully charged, and stand for a long time
Current discharge afterwards, it is the electricity to obtain battery both end voltage at this time after standing 60min when battery charge state SOC declines 10%
Battery open circuit voltage U under the state-of-charge SOC points of pondocvValue, then carries out the test of next battery charge state SOC points again.It puts
Electric process test after growth time is stood can analogy discharge process, carry out charging process UocvThe measurement of-SOC.According to testing
U under to different battery charge state SOC pointsocvValue after, U can be carried out in MATLAB cftool curve fittingocv-SOC
Curve matching, you can obtain the quantitative function relationship U of the twoocv(SOCk) wherein, k is the time.Specifically, interpolation may be used
The modes such as method or polynomial fitting method carry out curve fitting, and specific approximating method is determined by the degree of fitting height of curve, degree of fitting
More high then precision is higher.
Order RC equivalent-circuit model is as shown in figure 3, include two RC links, i.e. two in Order RC equivalent-circuit model
A polarization capacity (Cp1、Cp2) and polarization resistance (RP1、RP2) composition two circuits, the polarizing voltage in two circuits is respectively
UP1、UP2.To obtain initial parameter value, needs to carry out characteristic test to Order RC equivalent-circuit model, use under normal circumstances
Test method is to carry out mixed pulses power characteristic test (HPPC).One time HPPC experiments detailed process is as shown in Figure 4：1, with
35A current discharge 10s, such as t in Fig. 40To t1It is shown；2,40s is shelved, such as t in Fig. 41To t2It is shown；3, it is charged with 35A electric currents
10s, such as t in Fig. 42To t3It is shown；4,40s is shelved, such as t in Fig. 43To t4It is shown.According to different battery charge state SOC points
Pulse charge and discharge cycles test the identification that can carry out initial parameter value：
Ohmic internal resistance R0Identification：Ohmic internal resistance R0The ohmic internal resistance of ohmic internal resistance and electric discharge including charging, can be to fill
The ohmic internal resistance of electricity or the ohmic internal resistance of electric discharge are as ohmic internal resistance R0.Voltage change curve is as shown in figure 5, before battery discharge
The long period is stood, in t0Current load moment at moment, voltage change UA-UB=Δ UAB；In t1Moment electric current unloads wink
Between, voltage change UD-UC=Δ UDC, pulse current I, therefore the calculation formula of discharge process ohmic internal resistance is：Same method can find out the ohmic internal resistance in charging direction.
The identification of polarization parameter：Polarization parameter includes polarization resistance Rp, polarization capacity Cp, as shown in figure 3, polarization resistance Rp
Including the first polarization resistance RP1, the second polarization resistance RP2, polarization capacity CpIncluding the first polarization capacity Cp1And second polarization capacity
Cp2.To recognize polarization resistance Rp, need first to timeconstantτ=RP*CPIt is recognized.In conjunction with Fig. 3 and Fig. 5, DE is analyzed first
Section, if using D as starting point, the moment that pulsed discharge terminates, polarization capacity Cp1And Cp2The initial voltage at both ends is respectively U1(0) and U2
(0), the zero input response of two RC links can be write as respectively at this time：WithThen battery terminal voltage
Shown in mathematical relationship such as formula (1)：
Enable U1(0)=b1, U2(0)=b2, then above formula can be reduced to formula (2)：
According to formula (2), after pulsed discharge 40s's is stood to battery using the tool boxes cftool in Matlab
Experimental data carries out double exponential function fit, can obtain b1、b2And timeconstantτ1And τ2。
For BC sections, if using B as starting point, pulse constant current discharge current is I, and the zero state response of two RC links can at this time
It is expressed as：WithTherefore the voltage minute when C points in Fig. 4 on two polarization capacities
It is not：WithSince from C points to D points, polarizing voltage can't
It changes, polarizing voltage at this time is rewritable as shown in formula (3)：
The b obtained1、b2Value and τ1、τ2Value substitute into formula (3), you can obtain RP1、RP2, and then can be calculated
Cp1、Cp2Value.This offline parameter identification method can only obtain preset parameter, cannot reflect the dynamic of inside battery parameter
Variation, but may be used to determine the initial parameter value of battery.
Step S2 establishes battery model function based on the Order RC equivalent-circuit model, according to the battery model function, determines
Flow function relationship Uocv(SOCk) and battery charge state SOC defined functions obtain battery charge state SOC and polarizing voltage
State equation and observational equation；
Wherein, battery model function includes：The function of voltage established based on Kirchhoff's second law and current law and
The corresponding derivation function of two polarizing voltages based on Order RC equivalent-circuit model, in formulaWithIt refers to two
The derivation of a polarizing voltage：
Wherein, in Order RC equivalent-circuit model, ULFor terminal voltage, UocvFor open-circuit voltage, I is load current, UP1、
UP2The corresponding polarizing voltage in respectively two circuits.
Since battery model is generally discrete model under the conditions of practical application, thus need by battery model function carry out from
Dispersion：
IkFor the load current of k moment batteries, it is assumed that charging direction is just；UL(k) be the k moment battery terminal voltage；Δ
T is the sampling period；UP(k+1) it is the polarizing voltage in k+1 moment RC links.According to the discretization equation in formula (5), it is based on
Matlab-Function modules and each initial parameter value in Simulink can establish the simulation model of battery, the mould
Type needs to know current value and initial parameter value to find out voltage value.
Battery charge state SOC defined functions：Wherein, CcellFor battery can
With capacity, determining battery (with LiNixCoyMn1-x-yO2For battery) active volume CcellWhen, with above-mentioned standard charging
Battery is charged to full power state by regulation；With the discharge off condition of C/2 current discharges to battery, the total electric discharge of record discharge process
Capacity Q01；Stand 1h；It repeats above-mentioned three step and calculates discharge capacity Q02, Q03, then the arithmetic mean of instantaneous value of discharge capacity is three times
Ccell.If Q01, Q02, Q03 and CcellDeviation be respectively less than 2%, then CcellFor the active volume of the single battery.If
Q01, Q02, Q03 and CcellDeviation have the case where not less than 2%, then need to repeat active volume test process, until
Continuously discharge capacity three times meets the condition of active volume confirmation.
According to battery model function, quantitative function relationship Uocv(SOCk) and battery charge state SOC defined functions can be with
Obtain the state equation of battery charge state SOC and polarizing voltage in the first layer of binary channels Unscented kalman filtering algorithm DUKF
And observational equation：
Wherein, [Up1,k,Up2,k,SOCk]TFor state equation, [UL,k] it is observational equation, η indicates the charge and discharge effect of battery
Rate, CcellFor the active volume of battery, ikFor load current, k is the time, and Δ t refers to the time interval of experimental data, wkRefer to
Systematic procedure noise jamming, vkRefer to systematic survey noise jamming.
Step S3 obtains the state side of the corresponding inside battery parameter of the initial parameter value based on the battery model function
Journey and observational equation；
Wherein, the corresponding inside battery parameter R of selection initial parameter value0、Rp1、Cp1、Rp2、Cp2For the state variable of system,
The observational variable of battery is made of three parts：In the estimation result of the first layer of binary channels Unscented kalman filtering algorithm DUKF,
Battery polarization voltage is used as the observed quantity of polarization parameter, and battery terminal voltage is still used as battery ohmic internal resistance R0Observed quantity.
Since the shock wave of inside battery parameter is smaller, relative to battery charge state SOC and its dependent variable, in entire battery
Change in life span more slowly, therefore the state equation of inside battery parameter and observational equation are according to the battery model function
It obtains, as shown in formula (7)：
Wherein, θk=θk-1+γk-1For state equation,UL,k=Uocv
(SOCk-1)-UP1,K-1-UP2,K-1-ik-1R0,k-1For observational equation, k is time, γkIndicate small disturbance (it is slow due to changing, because
This adds small disturbance γk-1), Δ t is the time interval of experimental data, ikFor load current.
Step S4, using the state equation and observational equation of the battery charge state SOC and polarizing voltage as binary channels
The first layer of Unscented kalman filtering algorithm DUKF, and using the state equation of the inside battery parameter and observational equation as should
The second layer of binary channels Unscented kalman filtering algorithm DUKF carries out battery charge state SOC On-line Estimations.
In the present embodiment, the algorithm flow of binary channels Unscented kalman filtering algorithm DUKF includes formula (8) to formula (14), Xk
For the state variable of system, ZkIt is observational variable：
In the battery charge state SOC of above-mentioned formula (6) and the state equation and observational equation of polarizing voltage, X is enabledk=
[Up1,k,Up2,k,SOCk]T,zk=UL,k, in the state equation and observational equation of the inside battery parameter of above-mentioned formula (7), enableThen according to binary channels Unscented kalman filtering algorithm DUKF
Algorithm flow formula (8) to formula (14) carry out battery charge state SOC On-line Estimations, including on-line identification inside battery parameter with
And battery charge state SOC.
Specifically, binary channels Unscented kalman filtering algorithm DUKF on-line identification inside battery parameters and battery lotus are based on
Electricity condition SOC includes：Two layers of UKF interaction carries out in binary channels Unscented kalman filtering algorithm DUKF, and wherein first layer UKF's is defeated
Go out and carry out delay recursion after variable estimates, then input quantity as second layer UKF again, the output variable of second layer UKF
Delay recursion is carried out after estimating, then input quantity as first layer UKF again.First layer UKF is by the battery charge of battery
The state variable of state SOC and polarizing voltage as system, second layer UKF is using inside battery parameter as state variable.
Above process progress loop iteration can be obtained to the battery charge state SOC and inside battery parameter of real-time estimation,
Battery model function and battery charge state are verified by the dynamic operation condition in relevant manual testing (such as USABC) later
The dynamic response capability of SOC estimations, and the estimation precision of battery charge state SOC can be calculated, such as by reference to QC/T 897-
2011 calculate the estimation precision of battery charge state SOC.
The present embodiment is directed to the deficiency of battery charge state SOC estimation method in the prior art, especially for tradition
The defect of Kalman filtering method, battery charge state SOC of the present embodiment based on binary channels Unscented kalman filtering algorithm DUKF
On-line Estimation method does not need to the functional relation for accurately providing fuel cell modelling parameter (i.e. inside battery parameter) and SOC, only
Need to know the rough initial value of fuel cell modelling parameter can (i.e. initial parameter value), a large amount of experiment need not be done and obtain battery
Modeling parameters, it is simple and quick, the estimation on line of battery charge state SOC is not only effectively completed, is also realized in battery
The estimation on line of portion's parameter has many advantages, such as that precision is high, workload is small.
In a preferred embodiment, on the basis of the above embodiments, this method further includes：Obtain battery charge state
SOC On-line Estimation result datas substitute into the battery charge state SOC On-line Estimation result datas in battery model function,
To calculate the emulation terminal voltage of battery, the battery terminal voltage for emulating terminal voltage and actual measurement is compared, to verify initial ginseng
The accuracy of numerical value.
In the present embodiment, the inside battery parameter in battery charge state SOC On-line Estimation result datas is substituted into battery
In pattern function, the accuracy of inside battery parameter identification is verified.Specifically, by the inside battery parameter in estimated result data
The emulation terminal voltage for calculating battery in formula (4) is substituted into, emulation terminal voltage is compared with measured battery terminal voltage, to test
Demonstrate,prove the accuracy of initial parameter value.
The present invention also provides a kind of computer readable storage medium, processing is stored on the computer readable storage medium
The step of system, the processing system realizes the On-line Estimation method of above-mentioned battery charge state SOC when being executed by processor.
The embodiments of the present invention are for illustration only, can not represent the quality of embodiment.
Through the above description of the embodiments, those skilled in the art can be understood that above-described embodiment side
Method can add the mode of required general hardware platform to realize by software, naturally it is also possible to by hardware, but in many cases
The former is more preferably embodiment.Based on this understanding, technical scheme of the present invention substantially in other words does the prior art
Going out the part of contribution can be expressed in the form of software products, which is stored in a storage medium
In (such as ROM/RAM, magnetic disc, CD), including some instructions are used so that a station terminal equipment (can be mobile phone, computer, clothes
Be engaged in device, air conditioner or the network equipment etc.) execute method described in each embodiment of the present invention.
It these are only the preferred embodiment of the present invention, be not intended to limit the scope of the invention, it is every to utilize this hair
Equivalent structure or equivalent flow shift made by bright specification and accompanying drawing content is applied directly or indirectly in other relevant skills
Art field, is included within the scope of the present invention.
Claims (10)
1. a kind of On-line Estimation method of battery charge state SOC, which is characterized in that this approach includes the following steps：
S1 obtains the open-circuit voltage U of the battery obtained based on pulse charge-discharge testocvWith battery charge state SOCkQuantitative letter
Number relationship Uocv(SOCk), and determining the initial parameter value based on Order RC equivalent-circuit model, the initial parameter includes ohm
Internal resistance R0, the first polarization resistance RP1, the second polarization resistance RP2, the first polarization capacity Cp1And the second polarization capacity Cp2；
S2 establishes battery model function based on the Order RC equivalent-circuit model, is closed according to the battery model function, quantitative function
It is Uocv(SOCk) and battery charge state SOC defined functions obtain battery charge state SOC and polarizing voltage state equation
And observational equation；
S3 obtains state equation and the observation of the corresponding inside battery parameter of the initial parameter value based on the battery model function
Equation；
S4, using the state equation and observational equation of the battery charge state SOC and polarizing voltage as binary channels without mark karr
The first layer of graceful filtering algorithm DUKF, and using the state equation of the inside battery parameter and observational equation as the binary channels without
The second layer of mark Kalman filtering algorithm DUKF carries out battery charge state SOC On-line Estimations.
2. the On-line Estimation method of battery charge state SOC according to claim 1, which is characterized in that this method is also wrapped
It includes：
Battery charge state SOC On-line Estimation result datas are obtained, by the battery charge state SOC On-line Estimation result datas
Substitute into battery model function, to calculate the emulation terminal voltage of battery, will emulate the battery terminal voltage of terminal voltage and actual measurement into
Row comparison, to verify the accuracy of initial parameter value.
3. the On-line Estimation method of battery charge state SOC according to claim 1 or 2, which is characterized in that the battery
Pattern function includes：
Wherein, in Order RC equivalent-circuit model, ULFor terminal voltage, UocvFor open-circuit voltage, I is load current, UP1、UP2For
The corresponding polarizing voltage in two circuits,For the derivation function to polarizing voltage.
4. the On-line Estimation method of battery charge state SOC according to claim 3, which is characterized in that the battery lotus
The state equation and observational equation of electricity condition SOC and polarizing voltage include：
Wherein, [Up1,k,Up2,k,SOCk]TFor state equation, [UL,k] it is observational equation, η is the efficiency for charge-discharge of battery, CcellFor
The active volume of battery, ikFor load current, k is the time, and Δ t is the time interval of experimental data, wkIt is dry for systematic procedure noise
It disturbs, vkFor systematic survey noise jamming.
5. the On-line Estimation method of battery charge state SOC according to claim 3, which is characterized in that in the battery
The state equation and observational equation of portion's parameter include：
θk=θk-1+γk-1
Wherein, θk=θk-1+γk-1For state equation,UL,k=Uocv(SOCk-1)-
UP1,K-1-UP2,K-1-ik-1R0,k-1For observational equation, γkIndicate that small disturbance, k are the time, Δ t is between the time of experimental data
Every ikFor load current.
6. a kind of electronic device, which is characterized in that the electronic device includes memory and the processing that is connect with the memory
Device is stored with the processing system that can be run on the processor in the memory, and the processing system is by the processor
Following steps are realized when execution：
Obtain the open-circuit voltage U of the battery obtained based on pulse charge-discharge testocvWith battery charge state SOCkQuantitative function
Relationship Uocv(SOCk), and determining the initial parameter value based on Order RC equivalent-circuit model, the initial parameter includes in ohm
Hinder R0, the first polarization resistance RP1, the second polarization resistance RP2, the first polarization capacity Cp1And the second polarization capacity Cp2；
Battery model function is established based on the Order RC equivalent-circuit model, according to the battery model function, quantitative function relationship
Uocv(SOCk) and battery charge state SOC defined functions obtain battery charge state SOC and polarizing voltage state equation and
Observational equation；
State equation and the observation side of the corresponding inside battery parameter of the initial parameter value are obtained based on the battery model function
Journey；
It is filtered using the state equation and observational equation of the battery charge state SOC and polarizing voltage as binary channels Unscented kalman
The first layer of wave algorithm DUKF, and using the state equation of the inside battery parameter and observational equation as the binary channels without mark card
The second layer of Kalman Filtering algorithm DUKF carries out battery charge state SOC On-line Estimations.
7. electronic device according to claim 6, which is characterized in that the battery model function includes：
Wherein, in Order RC equivalent-circuit model, ULFor terminal voltage, UocvFor open-circuit voltage, I is load current, UP1、UP2For
The corresponding polarizing voltage in two circuits,For the derivation function to polarizing voltage.
8. electronic device according to claim 7, which is characterized in that the battery charge state SOC and polarizing voltage
State equation and observational equation include：
Wherein, [Up1,k,Up2,k,SOCk]TFor state equation, [UL,k] it is observational equation, η is the efficiency for charge-discharge of battery, CcellFor
The active volume of battery, ikFor load current, k is the time, and Δ t is the time interval of experimental data, wkIt is dry for systematic procedure noise
It disturbs, vkFor systematic survey noise jamming.
9. electronic device according to claim 7, which is characterized in that the state equation of the inside battery parameter and observation
Equation includes：
θk=θk-1+γk-1
Wherein, θk=θk-1+γk-1For state equation,UL,k=Uocv(SOCk-1)-
UP1,K-1-UP2,K-1-ik-1R0,k-1For observational equation, γkIndicate that small disturbance, k are the time, Δ t is between the time of experimental data
Every ikFor load current.
10. a kind of computer readable storage medium, which is characterized in that be stored with processing system on the computer readable storage medium
System realizes the battery charge state SOC as described in any one of claim 1 to 5 when the processing system is executed by processor
On-line Estimation method the step of.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810134609.6A CN108445401A (en) | 2018-02-09 | 2018-02-09 | On-line Estimation method, electronic device and the storage medium of battery charge state SOC |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810134609.6A CN108445401A (en) | 2018-02-09 | 2018-02-09 | On-line Estimation method, electronic device and the storage medium of battery charge state SOC |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108445401A true CN108445401A (en) | 2018-08-24 |
Family
ID=63192185
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810134609.6A Pending CN108445401A (en) | 2018-02-09 | 2018-02-09 | On-line Estimation method, electronic device and the storage medium of battery charge state SOC |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108445401A (en) |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109061508A (en) * | 2018-09-11 | 2018-12-21 | 上海电力学院 | A kind of estimation method of electric automobile lithium battery SOH |
| CN109188293A (en) * | 2018-11-08 | 2019-01-11 | 武汉理工大学 | Based on new breath EKF lithium ion battery SOC estimation method of the covariance with fading factor |
| CN109388074A (en) * | 2018-09-20 | 2019-02-26 | 深圳大学 | A kind of battery pack emulation mode and system, BMS calibration system |
| CN109633473A (en) * | 2019-01-23 | 2019-04-16 | 刘平 | A kind of distributed battery group state-of-charge algorithm for estimating |
| CN109782177A (en) * | 2018-12-29 | 2019-05-21 | 北京新能源汽车股份有限公司 | A kind of acquisition methods of battery capacity, device and automobile |
| CN109799459A (en) * | 2019-01-25 | 2019-05-24 | 欣旺达电子股份有限公司 | Test method, test device and the storage medium of lithium ion power battery core power |
| CN110412472A (en) * | 2019-09-02 | 2019-11-05 | 西北工业大学 | A kind of battery charge state estimation method based on the filtering of normal state gamma |
| CN110470990A (en) * | 2019-08-08 | 2019-11-19 | 浙江飞碟汽车制造有限公司 | A kind of power battery model and its parameter identification method |
| CN110850299A (en) * | 2019-11-26 | 2020-02-28 | 无锡物联网创新中心有限公司 | SR-UKF-based lithium ion power battery state estimation method |
| CN112098873A (en) * | 2020-08-17 | 2020-12-18 | 四川大学 | Lithium battery health state estimation method based on charging voltage curve geometric characteristics |
| CN112098845A (en) * | 2020-08-17 | 2020-12-18 | 四川大学 | Lithium battery state estimation method for distributed energy storage system |
| CN112731161A (en) * | 2021-02-08 | 2021-04-30 | 中南大学 | Nonlinear data feature extraction and classification prediction method based on small amount of data mixed insertion |
| CN112883531A (en) * | 2019-11-29 | 2021-06-01 | 比亚迪股份有限公司 | Lithium ion battery data processing method, computer device and storage medium |
| CN113138340A (en) * | 2020-01-17 | 2021-07-20 | 华为技术有限公司 | Method for establishing battery equivalent circuit model and method and device for estimating state of health |
| CN113147630A (en) * | 2021-04-30 | 2021-07-23 | 江铃汽车股份有限公司 | Mode control method, system, readable storage medium and vehicle |
| CN113466722A (en) * | 2020-03-31 | 2021-10-01 | 比亚迪股份有限公司 | Method and device for determining measurement accuracy of battery state of charge and electronic equipment |
| CN114609526A (en) * | 2022-05-11 | 2022-06-10 | 长城汽车股份有限公司 | Battery SOC estimation method and device, electronic equipment and vehicle |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030112177A1 (en) * | 2001-12-19 | 2003-06-19 | Hiroyuki Toda | Carrier-phase-based relative positioning device |
| CN104267261A (en) * | 2014-10-29 | 2015-01-07 | 哈尔滨工业大学 | On-line secondary battery simplified impedance spectroscopy model parameter estimating method based on fractional order united Kalman filtering |
| EP2963434A1 (en) * | 2014-06-30 | 2016-01-06 | Foundation Of Soongsil University-Industry Cooperation | Battery state estimation method and system using dual extended kalman filter, and recording medium for performing the method |
| CN106019164A (en) * | 2016-07-07 | 2016-10-12 | 武汉理工大学 | Lithium battery SOC estimation algorithm based on dual adaptive unscented Kalman filter |
| CN106126783A (en) * | 2016-06-16 | 2016-11-16 | 同济大学 | A kind of lithium ion battery becomes time scale model parameter estimation method |
| CN106249173A (en) * | 2016-10-10 | 2016-12-21 | 哈尔滨理工大学 | A kind of battery health degree SOH evaluation method |
| CN106602952A (en) * | 2016-06-29 | 2017-04-26 | 河南工程学院 | Flux linkage full-rank identification method for permanent magnet of PMSM |
| CN106814329A (en) * | 2016-12-30 | 2017-06-09 | 深圳市麦澜创新科技有限公司 | A kind of battery SOC On-line Estimation method based on double Kalman filtering algorithms |
| CN107589379A (en) * | 2017-08-30 | 2018-01-16 | 电子科技大学 | A kind of On-line Estimation lithium battery SOC and the method for impedance |
-
2018
- 2018-02-09 CN CN201810134609.6A patent/CN108445401A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030112177A1 (en) * | 2001-12-19 | 2003-06-19 | Hiroyuki Toda | Carrier-phase-based relative positioning device |
| EP2963434A1 (en) * | 2014-06-30 | 2016-01-06 | Foundation Of Soongsil University-Industry Cooperation | Battery state estimation method and system using dual extended kalman filter, and recording medium for performing the method |
| CN104267261A (en) * | 2014-10-29 | 2015-01-07 | 哈尔滨工业大学 | On-line secondary battery simplified impedance spectroscopy model parameter estimating method based on fractional order united Kalman filtering |
| CN106126783A (en) * | 2016-06-16 | 2016-11-16 | 同济大学 | A kind of lithium ion battery becomes time scale model parameter estimation method |
| CN106602952A (en) * | 2016-06-29 | 2017-04-26 | 河南工程学院 | Flux linkage full-rank identification method for permanent magnet of PMSM |
| CN106019164A (en) * | 2016-07-07 | 2016-10-12 | 武汉理工大学 | Lithium battery SOC estimation algorithm based on dual adaptive unscented Kalman filter |
| CN106249173A (en) * | 2016-10-10 | 2016-12-21 | 哈尔滨理工大学 | A kind of battery health degree SOH evaluation method |
| CN106814329A (en) * | 2016-12-30 | 2017-06-09 | 深圳市麦澜创新科技有限公司 | A kind of battery SOC On-line Estimation method based on double Kalman filtering algorithms |
| CN107589379A (en) * | 2017-08-30 | 2018-01-16 | 电子科技大学 | A kind of On-line Estimation lithium battery SOC and the method for impedance |
Non-Patent Citations (1)
| Title |
|---|
| 覃健: "基于DUFK的动力电池内阻与SoC估算", 《通信电源技术》 * |
Cited By (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109061508A (en) * | 2018-09-11 | 2018-12-21 | 上海电力学院 | A kind of estimation method of electric automobile lithium battery SOH |
| CN109388074A (en) * | 2018-09-20 | 2019-02-26 | 深圳大学 | A kind of battery pack emulation mode and system, BMS calibration system |
| CN109188293A (en) * | 2018-11-08 | 2019-01-11 | 武汉理工大学 | Based on new breath EKF lithium ion battery SOC estimation method of the covariance with fading factor |
| CN109782177A (en) * | 2018-12-29 | 2019-05-21 | 北京新能源汽车股份有限公司 | A kind of acquisition methods of battery capacity, device and automobile |
| CN109782177B (en) * | 2018-12-29 | 2021-04-20 | 北京新能源汽车股份有限公司 | Method and device for acquiring electric quantity of battery and automobile |
| CN109633473A (en) * | 2019-01-23 | 2019-04-16 | 刘平 | A kind of distributed battery group state-of-charge algorithm for estimating |
| CN109799459B (en) * | 2019-01-25 | 2021-02-12 | 欣旺达电子股份有限公司 | Method for testing power of lithium ion power battery cell, testing device and storage medium |
| CN109799459A (en) * | 2019-01-25 | 2019-05-24 | 欣旺达电子股份有限公司 | Test method, test device and the storage medium of lithium ion power battery core power |
| CN110470990A (en) * | 2019-08-08 | 2019-11-19 | 浙江飞碟汽车制造有限公司 | A kind of power battery model and its parameter identification method |
| CN110412472A (en) * | 2019-09-02 | 2019-11-05 | 西北工业大学 | A kind of battery charge state estimation method based on the filtering of normal state gamma |
| CN110850299A (en) * | 2019-11-26 | 2020-02-28 | 无锡物联网创新中心有限公司 | SR-UKF-based lithium ion power battery state estimation method |
| CN112883531A (en) * | 2019-11-29 | 2021-06-01 | 比亚迪股份有限公司 | Lithium ion battery data processing method, computer device and storage medium |
| CN112883531B (en) * | 2019-11-29 | 2022-10-18 | 比亚迪股份有限公司 | Lithium ion battery data processing method, computer device and storage medium |
| CN113138340A (en) * | 2020-01-17 | 2021-07-20 | 华为技术有限公司 | Method for establishing battery equivalent circuit model and method and device for estimating state of health |
| CN113466722A (en) * | 2020-03-31 | 2021-10-01 | 比亚迪股份有限公司 | Method and device for determining measurement accuracy of battery state of charge and electronic equipment |
| CN112098845A (en) * | 2020-08-17 | 2020-12-18 | 四川大学 | Lithium battery state estimation method for distributed energy storage system |
| CN112098873B (en) * | 2020-08-17 | 2021-06-01 | 四川大学 | Lithium battery health state estimation method based on charging voltage curve geometric characteristics |
| CN112098873A (en) * | 2020-08-17 | 2020-12-18 | 四川大学 | Lithium battery health state estimation method based on charging voltage curve geometric characteristics |
| CN112731161A (en) * | 2021-02-08 | 2021-04-30 | 中南大学 | Nonlinear data feature extraction and classification prediction method based on small amount of data mixed insertion |
| CN113147630A (en) * | 2021-04-30 | 2021-07-23 | 江铃汽车股份有限公司 | Mode control method, system, readable storage medium and vehicle |
| CN113147630B (en) * | 2021-04-30 | 2023-04-25 | 江铃汽车股份有限公司 | Mode control method, system, readable storage medium and vehicle |
| CN114609526A (en) * | 2022-05-11 | 2022-06-10 | 长城汽车股份有限公司 | Battery SOC estimation method and device, electronic equipment and vehicle |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108445401A (en) | On-line Estimation method, electronic device and the storage medium of battery charge state SOC | |
| CN108196200B (en) | Combined simulation evaluation method for health and state of charge of lithium battery | |
| Ahmed et al. | Model-based parameter identification of healthy and aged li-ion batteries for electric vehicle applications | |
| Ding et al. | An improved Thevenin model of lithium-ion battery with high accuracy for electric vehicles | |
| US10175302B2 (en) | Power system and state of charge estimation | |
| CN106569136B (en) | A kind of cell health state On-line Estimation method and system | |
| CN103632018B (en) | A kind of fuel cell modelling method based on Simscape platform | |
| CN114050633A (en) | Dynamic management and control method and device for lithium battery energy storage system and electronic equipment | |
| CN107576919A (en) | Power battery charged state estimating system and method based on ARMAX models | |
| CN106451592B (en) | The control method of battery charging and discharging, the control equipment of battery charging and discharging and electric vehicle | |
| US20220179003A1 (en) | Characterisation of lithium plating in rechargeable batteries | |
| CN107656210A (en) | A kind of method for estimating battery electric quantity state | |
| CN105223487B (en) | A kind of multimode decoupling method of estimation of lithium ion battery | |
| Wang et al. | An improved coulomb counting method based on dual open‐circuit voltage and real‐time evaluation of battery dischargeable capacity considering temperature and battery aging | |
| CN107167743A (en) | Charge state estimation method and device based on electric vehicle | |
| CN104833921A (en) | Battery pack charge state calculating method and calculating device | |
| CN109839599A (en) | Lithium ion battery SOC estimation method based on second order EKF algorithm | |
| CN115542186B (en) | Method, device, equipment and medium for evaluating state and consistency of energy storage battery | |
| CN108829911A (en) | A kind of open-circuit voltage and SOC functional relation optimization method | |
| Cai et al. | Electro-thermal model for lithium-ion battery simulations | |
| CN110210147A (en) | The simulator and emulation mode of estimating state of health of battery | |
| Gayathri et al. | Battery condition prognostic system using IoT in smart microgrids | |
| Liu et al. | A framework for battery temperature estimation based on fractional electro-thermal coupling model | |
| CN113296010A (en) | Battery health state online evaluation method based on differential voltage analysis | |
| CN106646260A (en) | SOC estimation method for BMS system based on genetic neural network |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180824 |