CN105717460B - A kind of power battery SOC methods of estimation and system based on nonlinear observer - Google Patents
A kind of power battery SOC methods of estimation and system based on nonlinear observer Download PDFInfo
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Abstract
The invention discloses a kind of power battery SOC methods of estimation based on nonlinear observer, including：Power battery carries out intermittent electric discharge and stands experiment, draws SOC and OCV expression formulas；Establish battery equivalent circuit model；Determine each parameter value of model；Build the separate manufacturing firms model of battery；Design self-adaptation nonlinear observer；Determine optimization object function；Solve optimal value of the parameter；Calculate the estimate of battery SOC.A kind of power battery SOC estimating systems based on optimal self-adaptation nonlinear observer, including power battery SOC OCV expression formula determining modules；Equivalent-circuit model establishes module；Parameter determination module；Power battery SOC estimates separate manufacturing firms model determining module；Self-adaptation nonlinear Design of Observer module；Parameter optimization object function determining module；Optimized parameter solves module；Power battery SOC estimation modules.Which raises SOC estimated accuracies, reduce algorithm calculation amount, are conducive to hardware realization, applied to electric automobile power battery management system field.
Description
Technical field
The present invention relates to electric automobile power battery management system field, more particularly to one kind based on optimal adaptive non-
The power battery SOC methods of estimation and system of Systems with Linear Observation device.
Background technology
Power battery is one of core component of electric vehicle, directly influences distance travelled, acceleration and the climbing of vehicle
The performance indicators such as ability.Battery management system (Battery Management System, BMS) is responsible for carrying out state to battery
The management and control of monitoring, electric quantity balancing, heat management, energy distribution etc., to extending battery, improving battery peace
Quan Xing, reduction battery Life cycle use cost etc. are of great significance.State-of-charge (State of Charge, SOC) is
Reflect an important indicator of battery dump energy and ability of doing work, be battery charging and discharging control, health status monitoring, energy point
Match somebody with somebody and the important evidence of the functions such as electric quantity balancing.However, shadow of the battery SOC by factors such as temperature, electric current, cycle-indexes
It rings, there is apparent uncertain and very strong nonlinear characteristic, therefore accurately estimation is considered as battery management system to SOC online
System is studied and one of the core in design process and Technology Difficulties.
At present, disclosed power battery SOC methods of estimation mainly include both at home and abroad：Internal resistance method, current integration method (also referred to as storehouse
Logical sequence measurement Law), open circuit voltage method, neural network, Kalman filtering method and observer method etc..Wherein, internal resistance method is according to battery
Functional relation between internal resistance and SOC calculates battery SOC by detecting the internal resistance of cell, however it is online, accurately measure battery
Internal resistance has difficulties, and limits application of this method in Practical Project.Although current integration method principle is simple, is easily achieved,
But the initial error and the cumulative errors because of caused by current measurement inaccuracy that SOC can not be eliminated.Open circuit voltage method according to
Open-circuit voltage (Open-Circuit Voltage, OCV) and the correspondence of SOC calculate battery SOC, it is necessary to which battery is abundant
OCV could be measured after standing, therefore is not suitable for the On-line Estimation of SOC.Neural network, it is necessary to substantial amounts of training sample, by
In the sample data that can not possibly obtain covering all actual conditions in practical applications, thus its precision will be subject to it is certain
It influences, and this method is computationally intensive is difficult to realize within hardware.Kalman filtering method and observer method, can correct well
The initial error of battery SOC, and with good anti-noise ability, but calculation amount is relatively large, hardware realization is difficult.It is so existing
In practical applications, all to some extent there are certain inconvenience and defect, therefore have must for some power battery SOC methods of estimation
It is further to be improved.
The content of the invention
In order to solve the above-mentioned technical problem, the object of the present invention is to provide a kind of precision is high, adaptable, calculation amount is small
The power battery SOC methods of estimation and system based on nonlinear observer.
The technical solution adopted in the present invention is：A kind of power battery SOC methods of estimation based on nonlinear observer, bag
Include step：
Intermittent electric discharge-standing experiment is carried out to power battery, fitting draws SOC and OCV
Between function expression；
Establish the equivalent-circuit model of power battery；
Determine each parameter value of power battery equivalent-circuit model；
Establish the separate manufacturing firms model of the power battery for SOC estimations；
Self-adaptation nonlinear observer designed for battery SOC estimation；
It determines for the object function of self-adaptation nonlinear observer parameter optimization；
Solve the most optimized parameter value of self-adaptation nonlinear observer；
The estimate of battery SOC is calculated using optimal self-adaptation nonlinear observer.
As the improvement of the technical solution, the battery model for battery parameter identification is equivalent using n ranks RC
Circuit model, wherein n >=1.
As the improvement of the technical solution, the battery model parameter is real by carrying out pulsed discharge-standing to battery
It tests, is obtained according to corresponding voltage response curves using exponentially fitted method off-line identification.
As the improvement of the technical solution, the SOC methods of estimation employ optimal self-adaptation nonlinear observer.
As the improvement of the technical solution, the value of the gain matrix of the self-adaptation nonlinear observer is formulated
For：
Wherein, aij(i, j=1,2 ..., n) it is first for the i-th row j row of the coefficient matrix in battery system state-space model
Element；
ki=αi(0.1+|ey|/(|ey|+0.1) (i=1,2 ..., n+1), αiFor proportionality coefficient (be more than zero real number),
eyFor systematic observation error.
As the improvement of the technical solution, the proportionality coefficient of the gain matrix of the optimal self-adaptation nonlinear observer is adopted
Optimized design has been carried out with swarm intelligence searching algorithm.
As the improvement of the technical solution, the gain matrix proportionality coefficient for optimal self-adaptation nonlinear observer
The swarm intelligence searching algorithm of optimized design is using particle swarm optimization algorithm.
Further, the optimized design of the gain matrix proportionality coefficient of the optimal self-adaptation nonlinear observer be with
The minimum target of absolute average error under measurement condition between battery SOC estimate and reference value, wherein：
Object function：minZ(α1,α2,α3)=mean (abs (SOCr-SOCe))
Wherein, mean () is is averaged function, and abs () is the function that takes absolute value, SOCrFor the reference value of SOC
(can usually be obtained by current integration method), SOCeFor the estimate of SOC.
The present invention also provides a kind of power battery SOC estimating systems based on optimal self-adaptation nonlinear observer, including：
Power battery SOC-OCV relational expression determining modules, it is real for carrying out intermittent electric discharge-standing to power battery
It tests, fitting draws the function expression between SOC and OCV；Electrokinetic cell system equivalent-circuit model establishes module, for establishing
The equivalent-circuit model of power battery；
Electrokinetic cell system equivalent circuit model parameter determining module, for determining each of power battery equivalent-circuit model
Parameter value；
Power battery SOC estimates separate manufacturing firms model determining module, for establishing the power battery for SOC estimations
Separate manufacturing firms model；
The self-adaptation nonlinear Design of Observer module of power battery SOC estimations, for being designed for battery SOC estimation
Self-adaptation nonlinear observer；
Self-adaptation nonlinear observer parameter optimization object function determining module, for determining to see for self-adaptation nonlinear
Survey the object function of device parameter optimization；
The particle swarm optimization algorithm module of self-adaptation nonlinear observer the most optimized parameter, it is adaptive non-for solving
The most optimized parameter value of Systems with Linear Observation device；
Power battery SOC estimation modules based on optimal nonlinear observer, for being seen using optimal self-adaptation nonlinear
Survey the estimate that device calculates battery SOC.
The beneficial effects of the invention are as follows：The present invention uses the power battery SOC based on optimal self-adaptation nonlinear observer
Method of estimation and system, wherein nonlinear observer, compared to traditional Kalman filtering, sliding mode observer and neutral net etc.
Method, precision is quite but computation complexity is lower, is easier to hardware realization, and self-adaptation nonlinear observer is compared with traditional
Constant-gain nonlinear observer, the value of gain matrix can be adaptively adjusted according to systematic observation error, contribute to
Improve estimated accuracy of the algorithm for estimating to noise and adaptability and SOC；Optimal self-adaptation nonlinear observer, adaptive
It answers and optimization processing has been done to the proportionality coefficient of gain matrix on the basis of nonlinear observer, SOC can be further improved and estimated
Count precision.Therefore, outstanding advantages of the invention are to improve SOC estimated accuracies, while reduce algorithm calculation amount, are conducive to firmly
Part is realized.
Description of the drawings
The specific embodiment of the present invention is described further below in conjunction with the accompanying drawings：
Fig. 1 is the flow chart of the battery SOC method of estimation of one embodiment of the invention；
Fig. 2 is the battery Order RC equivalent-circuit model schematic diagram of one embodiment of the invention；
Fig. 3 is the voltage response curves of the cell pulse discharge of one embodiment of the invention；
Fig. 4 passes through particle swarm optimization algorithm self-adaptation nonlinear observer gain matrix for one embodiment of the invention
The flow chart of proportionality coefficient；
Fig. 5 passes through particle swarm optimization algorithm self-adaptation nonlinear observer gain matrix for one embodiment of the invention
The convergence curve of global optimum during proportionality coefficient；
Fig. 6 is the measurement condition current-responsive curve for being used to verify battery SOC estimated accuracy of one embodiment of the invention；
Fig. 7 is the measurement condition voltage response curves for being used to verify battery SOC estimated accuracy of one embodiment of the invention；
Fig. 8 is the SOC test result schematic diagrames of the battery SOC method of estimation of one embodiment of the invention；
Fig. 9 is the SOC error testing result schematic diagrams of the battery SOC method of estimation of one embodiment of the invention；
Figure 10 is the functional block diagram of the battery SOC estimating system of one embodiment of the invention.
Specific embodiment
It should be noted that in the case where there is no conflict, the feature in embodiment and embodiment in the application can phase
Mutually combination.
In order to improve the Accuracy and high efficiency of Prospect of EVS Powered with Batteries SOC estimations, the embodiment of the present invention provides one
Power battery SOC method of estimation and system of the kind based on optimal self-adaptation nonlinear observer.
A kind of power battery SOC methods of estimation based on nonlinear observer, including step：
Intermittent electric discharge-standing experiment is carried out to power battery, fitting draws the function expression between SOC and OCV；
Establish the equivalent-circuit model of power battery；
Determine each parameter value of power battery equivalent-circuit model；
Establish the separate manufacturing firms model of the power battery for SOC estimations；
Self-adaptation nonlinear observer designed for battery SOC estimation；
It determines for the object function of self-adaptation nonlinear observer parameter optimization；
Solve the most optimized parameter value of self-adaptation nonlinear observer；
The estimate of battery SOC is calculated using optimal self-adaptation nonlinear observer.
As the improvement of the technical solution, the battery model for battery parameter identification is equivalent using n ranks RC
Circuit model, wherein n >=1.
As the improvement of the technical solution, the battery model parameter is real by carrying out pulsed discharge-standing to battery
It tests, is obtained according to corresponding voltage response curves using exponentially fitted method off-line identification.
As the improvement of the technical solution, the SOC methods of estimation employ optimal self-adaptation nonlinear observer.
As the improvement of the technical solution, the value of the gain matrix of the self-adaptation nonlinear observer is formulated
For：
Wherein, aij(i, j=1,2 ..., n) it is first for the i-th row j row of the coefficient matrix in battery system state-space model
Element；
ki=αi(0.1+|ey|/(|ey|+0.1) (i=1,2 ..., n+1), αiFor proportionality coefficient (be more than zero real number),
eyFor systematic observation error.
As the improvement of the technical solution, the proportionality coefficient of the gain matrix of the optimal self-adaptation nonlinear observer is adopted
Optimized design has been carried out with swarm intelligence searching algorithm.
As the improvement of the technical solution, the gain matrix proportionality coefficient for optimal self-adaptation nonlinear observer
The swarm intelligence searching algorithm of optimized design is using particle swarm optimization algorithm.
Further, the optimized design of the gain matrix proportionality coefficient of the optimal self-adaptation nonlinear observer be with
The minimum target of absolute average error under measurement condition between battery SOC estimate and reference value, wherein：
Object function：minZ(α1,α2,α3)=mean (abs (SOCr-SOCe))
Wherein, mean () is is averaged function, and abs () is the function that takes absolute value, SOCrFor the reference value of SOC
(can usually be obtained by current integration method), SOCeFor the estimate of SOC.
A kind of power battery SOC methods of estimation based on optimal self-adaptation nonlinear observer, as shown in Figure 1, including with
Lower step：
S101, intermittent electric discharge-standing experiment is carried out to power battery at ambient temperature, is intended according to obtained experimental data
The relational expression for closing out SOC-OCV (is denoted as OCV=focv(SOC)) different SOC values pair and in conduct SOC estimation procedures are asked for
The benchmark for the OCV values answered.
As one embodiment of the present of invention, use the SOC-OCV relational expressions that 5 rank multinomials are fitted for：
OCV=focv(SOC)
=12.5801 × SOC5-35.3081×SOC4+36.3924×SOC3-16.7012×SOC2+4.0110×SOC+
3.2030
S102, the equivalent-circuit model for establishing power battery.
Common battery model includes：Electrochemical model, neural network model and equivalent-circuit model etc., wherein, it is equivalent
Circuit model is more suited to battery parameter identification and SOC estimations.As one embodiment of the present of invention, Fig. 2 gives battery
Second order equivalent-circuit model, is apparent from by Fig. 2：
Wherein, ItRepresent battery-end electric current, RoRepresent ohmic internal resistance, Rp1And Cp1Activation polarization internal resistance and activation are represented respectively
Polarization capacity, Rp2And Cp2Concentration polarization internal resistance and concentration polarization capacitance, τ are represented respectively1=Rp1Cp1, τ2=Rp2Cp2。
S103, each parameter value for determining power battery equivalent-circuit model.
As one embodiment of the present of invention, battery progress pulse is put using 2C constant current values at SOC=70%
Electricity-standing is tested, and the voltage responsive during record is distinguished according to gained voltage response curves (attached drawing 3) using exponentially fitted method
Know each parameter value for battery equivalent circuit model.The principle and application method of exponential function fitting are summarized as follows：
(1) obviously, it is as follows to be represented by exponential function for the AB sections in voltage response curves shown in Fig. 3：
(2) contrast equation (1) and (2) can obtain：
(3) in addition, battery model parameter RoIt can be obtained by following equation：
S104, establish for the separate manufacturing firms model of the SOC power batteries estimated.
As one embodiment of the present of invention, battery SOC is established according to battery Order RC equivalent-circuit model shown in Fig. 2
The separate manufacturing firms model of estimation is as follows：
State equation：X (k+1)=Ax (k)+Bu (k+1)
Output equation：Y (k)=h (x (k))+Du (k)
Wherein, u=It, x=[x1 x2 x3]T=[Vcp1 Vcp2 SOC]T, y=Vt,
H (x)=Voc-Vcp1-Vcp2=focv(SOC)-Vcp1-Vcp2, D=Ro。
Wherein, ItRepresent battery-end electric current, RoRepresent ohmic internal resistance, Rp1And Cp1Activation polarization internal resistance and activation are represented respectively
Polarization capacity, Rp2And Cp2Concentration polarization internal resistance and concentration polarization capacitance, V are represented respectivelycp1Represent polarization capacity Cp1Terminal voltage,
Vcp2Represent polarization capacity Cp2Terminal voltage, VocRepresent open-circuit voltage, fOCVThe non-linear letter of () between battery OCV and SOC
Number relation, QnFor battery rated capacity, TsFor the sampling period of electric current and voltage.
S105, the self-adaptation nonlinear observer designed for battery SOC estimation.
According to the battery system separate manufacturing firms model that step S104 is established, estimating for state variable and output variable is calculated
Evaluation difference is as follows：
State estimation：
Output estimation value：
Wherein,WithThe estimate of state variable and output variable is represented respectively,Represent nonlinear function matrix h's
First derivative.
Wherein, the value of the gain matrix K of nonlinear observer must is fulfilled for the following conditions：
ATK-1+K-1A=-Q
Wherein, matrix Q must meet condition：(1) with the same orders of A；(2) all characteristic values are more than zero, it can thus be appreciated that matrix K and K-1
It is positive definite matrix.As one embodiment of the present of invention, system state space order of equation number (has 3 states to become for 3
Amount), therefore the value of K has following form：
Wherein, aijFor the i-th row j column elements of A, ki=αi(0.1+|ey|/(|ey|+0.1) (i=1,2,3), αiTo be more than
Zero real number, need to be according to practical application value, eyRepresent output error.
S106, determine for the object function of self-adaptation nonlinear observer parameter optimization.
The object definition of self-adaptation nonlinear observer parameter optimization is：Ask for making the absolute of SOC estimation and reference value
α during mean error minimum1, α2And α3Value, then object function be represented by：
minZ(α1,α2,α3)=mean (abs (SOCr-SOCe))
Wherein, mean () is is averaged function, and abs () is the function that takes absolute value, SOCrFor the reference value of SOC
(can usually be obtained by current integration method), SOCeFor the estimate of SOC.
S107, the most optimized parameter value for solving self-adaptation nonlinear observer.
As one embodiment of the present of invention, Fig. 4 is given using particle group optimizing (Particle Swarm
Optimization, PSO) Algorithm for Solving α1, α2And α3Optimal value process, Fig. 5 gives global optimum, and (i.e. SOC estimates
Absolute average error between evaluation and reference value) convergence process.
S108, the estimate that battery SOC is calculated using optimal self-adaptation nonlinear observer.
As one embodiment of the present of invention, Fig. 8 Fig. 9 gives the battery SOC estimation knot under operating mode shown in Fig. 6 Fig. 7
Fruit.Wherein, the parameter (α of optimal self-adaptation nonlinear observer1=20.0, α2=5.9 and α3=1.3) value is to pass through step
What rapid S106 was obtained, the parameter (α of self-adaptation nonlinear observer1=10.0, α2=5.0 and α3=1.0) value is artificially to set
's.
The correlation technique of the present invention is better understood from and grasped for ease of those skilled in the art, now by particle group optimizing
(PSO) basic principle of algorithm is described below：
PSO algorithms have many advantages, such as that small calculation amount, fast convergence rate, global optimizing ability are strong.In PSO algorithms, each
The potential solution of optimization problem be considered as D dimension search space on one be referred to as " particle " point, all particles all there are one
The adaptive value that is determined by object function and determine the speed of its heading and distance.The position of postulated particle is expressed as xi
=(xi1,xi2,…,xiD)T, speed is expressed as vi=(vi1,vi2,…,viD)T, then the update rule of speed and position is as follows：
Speed updates：
Location updating：
Wherein, i ∈ [1, m], m are the number of particle；D ∈ [1, D], D are the dimension of solution vector；K is iterations；WithThe respectively d of the position of particle i, speed and individual optimal value in kth time iteration ties up component；To be complete
Ds of the office optimal value g in kth time iteration ties up component；c1And c2For Studying factors, be respectively used to adjust to individual optimal value and
The maximum step-length of global optimum flight, usually takes c1=c2=2.0；W is inertia weight, general value range for [0.4,
0.9]；r1And r2For the random number between [0,1].In order to improve the search performance of algorithm, the inertia power of linear decrease can be used
Weight：
Wherein, wmaxAnd wminThe respectively upper and lower bound of inertia weight, can distinguish value for 0.9 and 0.4, k be current
Iterations, kmaxFor maximum iteration.
Corresponding to above-mentioned embodiment of the method, the embodiment of the present invention also provides a kind of based on the observation of optimal self-adaptation nonlinear
The power battery SOC estimating systems of device, as shown in Figure 10, power battery SOC estimating systems can include：
Power battery SOC-OCV relational expressions determining module 201, for determining that basis is worked as in battery SOC estimation procedure
Functional relation expression formula when preceding SOC value calculates current open circuit voltage (OCV) value between required SOC and OCV.
Electrokinetic cell system equivalent-circuit model establishes module 202, for establishing the electricity for off-line identification battery parameter
Cell system equivalent-circuit model, the selection including equivalent-circuit model species, the derivation of voltage responsive function.
Electrokinetic cell system equivalent circuit model parameter determining module 203, for off-line identification battery equivalent circuit model
Parameter value, the selection of acquisition, voltage response curves fitting function including cell pulse discharge-standing voltage response curves,
Derivation, the choosing of battery parameter discrimination method of relational expression between battery parameter and voltage response curves fitting function coefficient
Take and battery parameter identification etc..
Power battery SOC estimation separate manufacturing firms models determining module 204, for establishing for battery SOC estimation
Separate manufacturing firms equation, derivation, the shape of selection, state equation and output equation including system state variables and output variable
Discretization of state equation and output equation etc..
The self-adaptation nonlinear Design of Observer module 205 of power battery SOC estimations, estimates for establishing power battery SOC
The mathematical model of the self-adaptation nonlinear observer of meter, and derive the expression formula of self-adaptation nonlinear observer gain matrix.
Self-adaptation nonlinear observer parameter optimization object function determining module 206 is seen for establishing self-adaptation nonlinear
Survey the object function of device parameter optimization.
The particle swarm optimization algorithm module 207 of self-adaptation nonlinear observer the most optimized parameter, for using particle
Colony optimization algorithm solves the most optimized parameter of self-adaptation nonlinear observer, including particle swarm optimization algorithm parameter initialization, grain
The updating of sub- flying speed and position, the calculating of current fitness value, the update of particle individual optimum position, particle group are optimal
Update of position etc..
Power battery SOC estimation modules 208 based on optimal nonlinear observer, it is designed optimal adaptive for passing through
Nonlinear observer is answered to calculate the estimate of battery SOC, the meter of real-time acquisition, observation error including cell voltage and electric current
Update of calculation, state variable and output variable etc..
The above are implementing to be illustrated to the preferable of the present invention, but the invention is not limited to the implementation
Example, those skilled in the art can also make a variety of equivalent variations on the premise of without prejudice to spirit of the invention or replace
It changes, these equivalent deformations or replacement are all contained in the application claim limited range.
Claims (8)
1. a kind of power battery SOC methods of estimation based on nonlinear observer, which is characterized in that including step：
Intermittent electric discharge-standing experiment is carried out to power battery, fitting draws the function expression between SOC and OCV；
Establish the equivalent-circuit model of power battery；
Determine each parameter value of power battery equivalent-circuit model；
Establish the separate manufacturing firms model of the power battery for SOC estimations；
Self-adaptation nonlinear observer designed for battery SOC estimation；Wherein
The value of the gain matrix of the self-adaptation nonlinear observer is formulated as：
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Wherein, aij(i, j=1,2 ..., n) it is first for the i-th row j row of the coefficient matrix in battery system separate manufacturing firms model
Element；
ki=αi(0.1+|ey|/(|ey|+0.1) (i=1,2 ..., n+1), αiTo be more than zero proportionality coefficient, eyFor systematic perspective
Survey error；
It determines for the object function of self-adaptation nonlinear observer parameter optimization；
Solve the most optimized parameter value of self-adaptation nonlinear observer；
The estimate of battery SOC is calculated using optimal self-adaptation nonlinear observer.
2. the power battery SOC methods of estimation according to claim 1 based on nonlinear observer, it is characterised in that：Institute
The battery equivalent circuit model for battery parameter identification is stated using n rank RC equivalent-circuit models, wherein n >=1.
3. the power battery SOC methods of estimation according to claim 2 based on nonlinear observer, it is characterised in that：Institute
Stating battery equivalent circuit model parameter is tested by carrying out pulsed discharge-standing to battery, bent according to corresponding voltage responsive
Line is obtained using exponentially fitted method off-line identification.
4. the power battery SOC methods of estimation according to claim 3 based on nonlinear observer, it is characterised in that：Institute
It states SOC methods of estimation and employs optimal self-adaptation nonlinear observer.
5. the power battery SOC methods of estimation according to claim 4 based on nonlinear observer, it is characterised in that：Institute
The proportionality coefficient for stating the gain matrix of optimal self-adaptation nonlinear observer is optimized using swarm intelligence searching algorithm
Design.
6. the power battery SOC methods of estimation according to claim 5 based on nonlinear observer, it is characterised in that：Institute
The swarm intelligence searching algorithm for stating the gain matrix proportionality coefficient optimized design for optimal self-adaptation nonlinear observer is adopted
It is particle swarm optimization algorithm.
7. the power battery SOC methods of estimation according to claim 6 based on nonlinear observer, it is characterised in that：Institute
The optimized design for stating the gain matrix proportionality coefficient of optimal self-adaptation nonlinear observer is estimated with battery SOC under measurement condition
The minimum target of absolute average error between evaluation and reference value, wherein：
Object function：minZ(α1,α2,α3)=mean (abs (SOCr-SOCe))
Wherein, mean () is is averaged function, and abs () is the function that takes absolute value, SOCrFor the reference value of SOC, SOCeFor
The estimate of SOC.
8. a kind of power battery SOC estimating systems based on optimal self-adaptation nonlinear observer, which is characterized in that including：
Power battery SOC-OCV relational expression determining modules are tested for carrying out intermittent electric discharge-standing to power battery,
Fitting draws the function expression between SOC and OCV；
Electrokinetic cell system equivalent-circuit model establishes module, for establishing the equivalent-circuit model of power battery；
Electrokinetic cell system equivalent circuit model parameter determining module, for determining each parameter of power battery equivalent-circuit model
Value；
Power battery SOC estimate separate manufacturing firms model determining module, for establish for SOC estimation power battery from
Dissipate state-space model；
The self-adaptation nonlinear Design of Observer module of power battery SOC estimations, for being designed for the adaptive of battery SOC estimation
Answer nonlinear observer；Wherein
The value of the gain matrix of the self-adaptation nonlinear observer is formulated as：
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Wherein, aij(i, j=1,2 ..., n) it is first for the i-th row j row of the coefficient matrix in battery system separate manufacturing firms model
Element；
ki=αi(0.1+|ey|/(|ey|+0.1) (i=1,2 ..., n+1), αiTo be more than zero proportionality coefficient, eyFor systematic perspective
Survey error；
Self-adaptation nonlinear observer parameter optimization object function determining module, for determining for self-adaptation nonlinear observer
The object function of parameter optimization；
The particle swarm optimization algorithm module of self-adaptation nonlinear observer the most optimized parameter, for solving self-adaptation nonlinear
The most optimized parameter value of observer；
Power battery SOC estimation modules based on optimal self-adaptation nonlinear observer, for using optimal self-adaptation nonlinear
Observer calculates the estimate of battery SOC.
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| CN106324523B (en) * | 2016-09-26 | 2019-02-19 | 合肥工业大学 | Lithium battery SOC estimation method based on discrete-time variable structure observer |
| CN106338695A (en) * | 2016-10-09 | 2017-01-18 | 深圳市沃特玛电池有限公司 | Battery model parameter identification method based on particle swarm algorithm |
| CN106707178B (en) * | 2016-12-01 | 2020-05-19 | 深圳市麦澜创新科技有限公司 | Method for estimating SOC of battery by multi-gain observer based on classifier decision |
| CN106597308B (en) * | 2016-12-16 | 2018-12-25 | 西南交通大学 | A kind of power battery method for estimating remaining capacity |
| CN108241128B (en) * | 2018-01-09 | 2019-10-01 | 西南交通大学 | A kind of proton exchange film fuel battery system method for estimating state |
| CN108791269B (en) * | 2018-06-27 | 2019-12-31 | 福州大学 | PHEV distributed control method applicable to power battery exchange modularization |
| CN109239614A (en) * | 2018-11-12 | 2019-01-18 | 合肥工业大学 | The lithium battery SOC estimation method of drift current value in a kind of consideration sensor |
| CN109782182B (en) * | 2019-01-14 | 2021-08-03 | 深圳大学 | Online estimation method and device for energy state of series battery pack |
| CN112428878A (en) * | 2019-08-26 | 2021-03-02 | 上海汽车集团股份有限公司 | Software refreshing control method and device and Internet of vehicles equipment |
| CN110907835B (en) * | 2019-12-10 | 2020-12-11 | 北京理工大学 | Battery model parameter identification and SOC estimation method with noise immunity characteristic |
| US11541775B2 (en) * | 2020-02-04 | 2023-01-03 | Ford Global Technologies, Llc | Battery state of charge estimation system for a hybrid/electric vehicle |
| CN112685917B (en) * | 2021-01-27 | 2023-04-18 | 重庆大学 | Battery equalization modeling system and method based on nonlinear efficiency model |
| CN113109712A (en) * | 2021-04-15 | 2021-07-13 | 上海交通大学宁波人工智能研究院 | Nonlinear observer based on two-branch equivalent circuit and SOC estimation method |
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