CN108732509A - A kind of On-line Estimation method of the charge states of lithium ion battery of space-oriented application - Google Patents
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Abstract
A kind of On-line Estimation method of the charge states of lithium ion battery of space-oriented application, be related to lithium ion battery health management arts, in order to solve the problems, such as that estimated accuracy is low existing for existing lithium ion battery SOC methods of estimation, stability is poor and to the completeness of training dataset it is more demanding.Establish battery equivalent circuit model；Identification of Model Parameters is carried out, and considers the variation relation between model parameter and SOC, establishes model parameter interpolation table；Model parameter meets when applying current excitation signal to circuit model voltage responsive error in allowed limits；Establish the state space equation of state-of-charge estimating system；Using the state space equation of foundation, the On-line Estimation of current time battery charge state SOC is carried out based on UPF algorithms；According to the state-of-charge SOC estimation at current time, model parameter is updated in conjunction with the model parameter interpolation table of foundation, and by newer model parameter in the estimation of subsequent time state-of-charge.Suitable for On-line Estimation battery charge state.
Description
Technical Field
The invention relates to the field of lithium ion battery health management, in particular to a lithium ion battery charge state online estimation method based on an unscented particle filter algorithm.
Background
The lithium ion battery has the advantages of high single output voltage, long cycle life, low self-discharge rate, high energy density, no environmental pollution and the like, is widely applied to the fields of consumer electronics, electric automobiles, communication energy storage base stations and the like, and is gradually expanded to the military fields of aviation, aerospace, navigation and the like. Particularly in the aspect of space satellite application, the lithium ion battery can greatly reduce the weight and the volume of load, has great advantages compared with the traditional nickel-hydrogen battery and nickel-chromium battery, and becomes a third-generation satellite energy storage battery. As the only energy source for the operation of the satellite in the earth shadow period, the safe and reliable operation of the lithium ion battery is the premise for ensuring the on-orbit operation of the space vehicles such as the satellite. Therefore, lithium ion battery management for space-oriented applications has become a hot spot of research.
The State of Charge (SOC) estimation is one of the core contents of the health management of the lithium ion battery, the online real-time estimation can predict the running time of the system, and a reasonable battery charging and discharging strategy is formulated, which has important significance for guaranteeing the safe running of the system. For space application, SOC estimation can provide important reference for energy distribution and optimal management of a lithium ion battery or a spacecraft power subsystem. However, the strong non-linear characteristic of the lithium ion battery itself brings great challenges to the accurate estimation of the SOC, and therefore, developing an SOC estimation method capable of adapting to a complex battery system is a technical difficulty of current battery management.
At present, methods for estimating the SOC of a lithium ion battery mainly include an ampere-hour integration method, an open-circuit voltage method, a Kalman filter method, an Extended Kalman Filtering (EKF), a neural network, and the like. The ampere-hour integration method is simple to realize and is also an estimation method adopted in most occasions with low precision requirements at present, but accumulated errors exist in the long-term estimation process, and the initial SOC cannot be determined; the open-circuit voltage method needs long-time battery standing, is mostly used for testing application under laboratory conditions, and cannot estimate SOC in real time on line; the Kalman filtering algorithm is only suitable for a linear system, and the filtering precision of the nonlinear system, namely a battery, is not high and even is divergent; the extended Kalman filtering algorithm has certain adaptability to a nonlinear system, but still has certain defects to a strong nonlinear system, namely a battery, and has the problems of reduced stability and even divergence; the neural network method can realize online estimation of the SOC, but a large number of test data sets are required to be used as supports, namely the requirement on the completeness of a training data set is high. Meanwhile, a Particle Filter series algorithm, such as an Unscented Particle Filter (UPF) algorithm, has good adaptability to a nonlinear non-Gaussian complex system, and has a wide application prospect in the fields of battery state estimation and life prediction.
Disclosure of Invention
The invention aims to solve the problems of low estimation precision, poor stability and higher requirement on the completeness of a training data set in the conventional lithium ion battery SOC estimation method, so that the on-line estimation method for the lithium ion battery SOC is applied to space.
The invention relates to an online estimation method of the state of charge of a lithium ion battery for space application, which comprises the following steps:
step one, establishing a battery equivalent circuit model;
secondly, identifying model parameters, and establishing a model parameter interpolation table by considering the change relation between the model parameters and the SOC;
step three, based on the model parameters identified in the step two, applying a current excitation signal to the circuit model, judging whether the voltage response error is in an allowable range, if so, performing the step four, otherwise, returning to the step two;
establishing a state space equation of the state of charge estimation system;
step five, adopting the state space equation established in the step four, and carrying out online estimation on the SOC of the battery at the current moment based on a UPF algorithm;
step six: and updating the model parameters by combining the model parameter interpolation table established in the step two according to the SOC estimated value of the current moment, and using the updated model parameters in the estimation of the SOC of the next moment.
Preferably, the battery equivalent circuit model in the step one is a first-order RC circuit model, and the model parameter includes ohmic internal resistance R0Polarization resistance RpAnd a polarization capacitor CpAnd electromotive force E of power supplym(ii) a The polarized capacitor is connected with the polarized resistor in parallel to form a parallel RC branch, one end of the branch is connected with the load, the other end of the branch is connected with the high potential end of the power supply after being connected with the ohmic resistor in series, and the low potential end of the power supply is connected with the load.
Preferably, the state space equation established in the fourth step considers the change of the coulomb efficiency parameter of the battery in the charging and discharging process, and performs real-time filtering update by taking the coulomb efficiency as one dimension of the state quantity, and the expression of the state space equation is as follows:
xk＝Ak-1xk-1+Bk-1Uk-1+wk-1
yk＝Ckxk-DkUk+f(SOCk)+vk
wherein x iskIs the system state quantity at time k, Uk-1Is the system control quantity at the time k-1, wk-1Systematic process noise at time k-1, Ak-1And Bk-1Respectively corresponding state transition transformation matrixes of the system state quantity and the system control quantity at the moment of k-1; y iskSystem observations at time k, Em＝f(SOCk)，EmThe value is obtained by looking up according to the model parameter interpolation table, SOCkSOC at time k, vkMeasurement noise at time k, CkA transformation matrix between the observed quantities and the state quantities corresponding to the system state quantities at time k, DkAnd the transformation matrix is a transformation matrix between the state quantity and the observed quantity corresponding to the system control quantity at the time k.
It is preferable that the first and second liquid crystal layers are formed of,
yk＝[Ut,k]，Ck＝[0 -1 0]，Dk＝[R0]，vk＝[vk]
wherein, Up,kTerminal voltage, T, of a parallel RC branch at time ksFor the sampling interval, τ ═ Rp*CpIs a time constant, CNis the maximum capacity of the battery, ηkCoulomb efficiency, U, at time k of the batteryt,kTerminal voltage of load at time k, IL,kLoad side current at time k, wk-1,1、wk-1,2And wk-1,3respectively represent state quantities η, UpProcess noise corresponding to SOC.
According to the lithium ion battery state of charge online estimation method for space application, the influence of SOC change on model parameters is considered in the established battery equivalent model, and the online estimation of SOC is realized based on a UPF algorithm. The method can be suitable for SOC online estimation under various battery working conditions and various uncertain environments, and has the advantages of high estimation precision and good stability.
Drawings
FIG. 1 is a flow chart of an online estimation method of the state of charge of a lithium ion battery for space-oriented application according to the present invention;
FIG. 2 is a schematic diagram of a battery equivalent circuit model;
FIG. 3 is a HPPC condition current diagram for model parameter identification;
FIG. 4 is an HPPC operating condition voltage map for model parameter identification;
FIG. 5 is a graph showing the variation relationship between the identified model parameters and SOC;
(a) is R0A curve of variation with SOC, wherein (b) is RpA curve of variation with SOC, (C) is CpA curve relating to the change in SOC, wherein (d) is EmA change relation curve between the SOC and the SOC;
FIG. 6 is a diagram showing the result of estimating the working condition of the CCD under the condition of uncertain initial value of SOC;
(a) is SOC estimated value curve, (b) is estimation error curve;
FIG. 7 is a diagram illustrating the result of estimating the working condition of the HPPC under the condition of uncertain initial value of SOC;
(a) is SOC estimated value curve, (b) is estimation error curve;
FIG. 8 is a diagram showing the result of estimating the working condition of the CCD in the presence of noise in current measurement;
(a) is SOC estimated value curve, (b) is estimation error curve;
FIG. 9 is a graph of HPPC condition estimation results in the presence of current measurement noise;
(a) is SOC estimated value curve, (b) is estimation error curve;
FIG. 10 is a comparison of the estimated results of two different methods under CCD conditions;
(a) is SOC estimated value curve, (b) is estimation error curve;
FIG. 11 is a comparison of the estimated results of two different methods under HPPC conditions;
(a) is an SOC estimation value curve, and (b) is an estimation error curve.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The invention is further described with reference to the following drawings and specific examples, which are not intended to be limiting.
An online estimation method for the state of charge of a lithium ion battery applied to space comprises the following steps:
step one, establishing a battery equivalent circuit model; the equivalent circuit model includes parameters with ohmic internal resistance R0Polarization resistance RpPolarization capacitance CpAnd electromotive force Em；
Step two, building a model parameter identification environment under matlab/simulink software, identifying model parameters, and establishing a model parameter interpolation table by considering the variation relation between the model parameters and the SOC;
the HPPC working condition current is used as the excitation for identifying the environment, and the HPPC working condition voltage is used as the response output for identifying the environment.
Step three, circuit model evaluation, namely, based on the circuit model parameters identified in the step two, applying a current excitation signal to the circuit model, judging whether the voltage response error is in an allowable range, if so, performing the step four, otherwise, returning to the step two;
the same current excitation signal is applied to the model, the response voltage of the model is compared with a reference voltage (real voltage), and the voltage response error does not exceed 22 mV. Therefore, the established circuit model and the parameter identification result thereof have good representation capability on the battery and can be applied to subsequent SOC estimation.
Step four, the change of coulomb efficiency parameters of the battery in the charging and discharging process is considered in the established state space equation, and real-time filtering updating is carried out by taking the coulomb efficiency as one dimension of the state quantity, and the expression of the state space equation is as follows:
xk＝Ak-1xk-1+Bk-1Uk-1+wk-1(1)
yk＝Ckxk-DkUk+f(SOCk)+vk(2)
wherein x iskIs the system state quantity at time k, Uk-1Is the system control quantity at the time k-1, wk-1Systematic process noise at time k-1, Ak-1And Bk-1Respectively corresponding state transition transformation matrixes of the system state quantity and the system control quantity at the moment of k-1; y iskSystem observations at time k, Em＝f(SOCk)，EmThe value is obtained by looking up according to the model parameter interpolation table, SOCkSOC at time k, vkMeasurement noise at time k, CkA transformation matrix between the observed quantities and the state quantities corresponding to the system state quantities at time k, DkAnd the transformation matrix is a transformation matrix between the state quantity and the observed quantity corresponding to the system control quantity at the time k.
The change of the coulomb efficiency parameter of the battery in the charging and discharging process is considered, and the coulomb efficiency is used as one dimension of the state quantity to carry out real-time filtering updating, so that the estimation performance of the SOC is effectively improved.
The detailed derivation process is as follows:
if the ends of the parallel RC branches are assumedVoltage is UpThe load terminal voltage and current are respectively UtAnd ILThen, the first-order RC battery model includes the following circuit equations:
Ut＝Em-Up-ILR0(3)
the equation is discretized as:
Ut,k＝Em,k-IL,kR0-Up,k(5)
wherein, TsFor the sampling interval, τ ═ Rp*CpTime and constants.
Meanwhile, the discrete recursion equation of the SOC obtained by the ampere-hour integration method is as follows:
wherein, CNthe rated capacity of the battery is usually taken as the maximum capacity of the battery, and η is the coulomb efficiency of the battery.
selecting battery coulombic efficiency eta, state of charge SOC and polarization voltage UpAs the state quantity of the system, the load voltage U can be detected on linetFor the observed quantity of the system, the corresponding state space equation is:
wherein, wk-1,1、wk-1,2And wk-1,3respectively represent state quantities η, UpAnd process noise of the SOC; v. ofkTo measure noise.
If the following steps are recorded:
yk＝[Ut,k]，Ck＝[0 -1 0]，Dk＝[R0]，vk＝[vk]
the above state space equation can be expressed as:
xk＝Ak-1xk-1+Bk-1Uk-1+wk-1(10)
yk＝Ckxk-DkUk+f(SOCk)+vk(11)
wherein E ism＝f(SOCk)，EmThe values can be found from the established model parameter interpolation table.
And step five, adopting the state space equation of the step four, and carrying out online estimation on the SOC of the current battery based on a UPF algorithm:
step five, first: initialization
The particle number N of the UPF algorithm, the initial value of the state quantity, the noise variance and the like are set, and the particle distribution and the covariance matrix thereof are initialized.
Distribution p (x) based on initial time (zero time)0) Randomly generating N particles { x0 (i)+And the corresponding covariance matrix { P }0 (i)+1,2, N, and giving an initial model parameter R according to the initial SOC0，Rp，CpAnd Em；
Step five two: calculating the sigma point distribution of each particle
Wherein,in order to generate the sigma point matrix,is an amplification matrix of the system state quantity and noise,an augmented covariance matrix of state quantities and noise,l is the dimension of the state quantity and λ is a constant. w and Q are the state noise matrix and variance, respectively, and v and R are the measurement noise matrix and variance.
Step five and step three: time updating
Wherein,the value is updated for one step of the system state quantity,is an estimated value of the state quantity at the moment k-1,for state quantity process noise at time k-1, Ak-1And Bk-1For state transition transformation matrices, Uk-1A system control quantity;in order to predict the value of the state quantity in one step,andis a weight constant;one-step prediction value for covariance;updating the value of the observed quantity by one step, CkAnd DkF (-) represents SOC and electromotive force E as transformation matrix between state quantity and observed quantitymFunctional relationship between;and predicting the one-step prediction value of the observed quantity.
Step five and four: measurement update
Wherein,measuring a variance matrix;is the covariance matrix between the state quantities and the measurements; kkIs the Kalman filter gain;is the estimated value of the system state at the moment k;an updated covariance matrix.
Step five: weight calculation and normalization
Wherein q isiThe weight value of each particle is used as the weight value,is a normalized particle weight value, ykIs the system observed quantity at time k.
Step five and step six: particle resampling
Wherein,andthe original particles and their covariance matrix,andthe resulting particles and their covariance matrix are resampled,is a random number between 0 and 1.
Step five and seven: SOC estimation
Therein, SOCkAs a result of the SOC estimation at time k,for each of the estimated values of SOC for each particle,is the second dimension value of the state quantity.
Sixthly, obtaining the estimation result SOC of the k time according to the step five and sevenkUpdating model parameter R by combining with model parameter interpolation table0，Rp，CpAnd EmAnd used in the SOC estimation at the next time;
and repeating the fifth step and the second step to the sixth step, and estimating the SOC of the battery until the estimation cycle is finished.
And (3) experimental verification:
the method is adopted to carry out SOC estimation experiments under two different battery working conditions (CCD and HPPC) and two uncertain conditions (SOC initial values are uncertain and current measurement noise exists), and is compared with an EKF method.
(1) Uncertainty of initial value of SOC
FIG. 6 is the estimation result under CCD condition, and FIG. 7 is the estimation result under HPPC condition; according to the experimental results, the
The detailed estimated performance by the time the initial SOC0 is 0.85 is shown in table 1.
Table 1 estimation result when initial SOC0 is 0.85
Experiments show that under the condition that the initial value of the SOC is uncertain, the maximum estimation errors under the two working conditions are within 5%, the average estimation error is less than 2%, and the convergence (error is less than 1%) response time is not more than 120s, namely the adopted estimation method still has good estimation performance and convergence under the condition that the initial value of the SOC is uncertain.
(2) In the presence of current measurement noise
FIG. 8 is the estimation result under CCD condition, and FIG. 9 is the estimation result under HPPC condition; detailed performance of SOC estimation in the presence of current noise according to the experimental results is shown in table 2.
TABLE 2 SOC estimation results under Current noise
It can be obtained that when the current detection has certain noise interference, the maximum estimation error of the two working conditions does not exceed 5%, and the average error is within 2%, namely, the current detection still has good estimation performance.
(3) Comparison of the methods
FIG. 10 is a comparison graph of the estimated results of two different methods under the CCD condition, FIG. 11 is a comparison graph of the estimated results of two different methods under the HPPC condition, and the detailed comparison of the estimated performances is shown in tables 3 and 4.
TABLE 3 comparison of CCD behavior estimation
| Method of producing a composite material | Maximum error | Mean error | Root mean square error |
| EKF | 0.0497 | 0.0194 | 0.0225 |
| UPF | 0.0203 | 0.0082 | 0.0098 |
TABLE 4 HPPC Condition estimation Performance comparison
| Method of producing a composite material | Maximum error | Mean error | Root mean square error |
| EKF | 0.0648 | 0.0150 | 0.0207 |
| UPF | 0.0131 | 0.0030 | 0.0038 |
According to the experimental result, under two different working conditions, the UPF algorithm is superior to the EKF algorithm in three evaluation indexes of maximum error, average error and root mean square error. In addition, the UPF algorithm can keep high estimation precision and good stability in the whole SOC change process, and the EKF algorithm has larger deviation in the estimation result at the end stage of battery discharge and even has a 'divergence' trend, namely the stability of the algorithm is obviously reduced. Therefore, the SOC online estimation method has good estimation accuracy and stability.
Claims (4)
1. A lithium ion battery charge state online estimation method for space application is characterized by comprising the following steps:
step one, establishing a battery equivalent circuit model;
secondly, identifying model parameters, and establishing a model parameter interpolation table by considering the change relation between the model parameters and the SOC;
step three, based on the model parameters identified in the step two, applying a current excitation signal to the circuit model, judging whether the voltage response error is in an allowable range, if so, performing the step four, otherwise, returning to the step two;
establishing a state space equation of the state of charge estimation system;
step five, adopting the state space equation established in the step four, and carrying out online estimation on the SOC at the current moment based on a UPF algorithm;
step six: and updating the model parameters by combining the model parameter interpolation table established in the step two according to the SOC estimated value of the current moment, and using the updated model parameters in the estimation of the SOC of the next moment.
2. The method according to claim 1, wherein the battery equivalent circuit model in the step one is a first-order RC circuit model, and the model parameter includes ohmic internal resistance R0Polarization resistance RpAnd a polarization capacitor CpAnd electromotive force E of power supplym(ii) a The polarized capacitor is connected with the polarized resistor in parallel to form a parallel RC branch, one end of the branch is connected with the load, the other end of the branch is connected with the high potential end of the power supply after being connected with the ohmic resistor in series, and the low potential end of the power supply is connected with the load.
3. The online estimation method for the state of charge of the lithium ion battery applied to the space according to claim 1, characterized in that the state space equation established in the step four considers the change of coulombic efficiency parameters of the battery in the charging and discharging processes, and performs real-time filtering update by taking the coulombic efficiency as one dimension of the state quantity, and the expression of the state space equation is as follows:
xk＝Ak-1xk-1+Bk-1Uk-1+wk-1
yk＝Ckxk-DkUk+f(SOCk)+vk
wherein x iskIs the system state quantity at time k, Uk-1Is the system control quantity at the time k-1, wk-1At time k-1Systematic process noise, Ak-1And Bk-1Respectively corresponding state transition transformation matrixes of the system state quantity and the system control quantity at the moment of k-1; y iskSystem observations at time k, Em＝f(SOCk)，EmThe value is obtained by looking up according to the model parameter interpolation table, SOCkSOC at time k, vkMeasurement noise at time k, CkA transformation matrix between the observed quantities and the state quantities corresponding to the system state quantities at time k, DkAnd the transformation matrix is a transformation matrix between the state quantity and the observed quantity corresponding to the system control quantity at the time k.
4. The method for on-line estimation of the state of charge of the lithium ion battery for space-oriented application according to claim 3,
yk＝[Ut,k]，Ck＝[0 -1 0]，Dk＝[R0]，vk＝[vk]
wherein, Up,kTerminal voltage, T, of a parallel RC branch at time ksFor the sampling interval, τ ═ Rp*CpIs a time constant, CNis the maximum capacity of the battery, ηkCoulomb efficiency, U, at time k of the batteryt,kTerminal voltage of load at time k, IL,kLoad current at time k, wk-1,1、wk-1,2And wk-1,3respectively represent state quantities η, UpProcess noise corresponding to SOC.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109324294A (en) * | 2018-11-22 | 2019-02-12 | 联想(北京)有限公司 | Data processing method and its device |
| CN110133525A (en) * | 2019-05-13 | 2019-08-16 | 哈尔滨工业大学 | A kind of health state of lithium ion battery estimation method applied to battery management system |
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| CN110927595A (en) * | 2019-12-17 | 2020-03-27 | 北京空间飞行器总体设计部 | Ampere-hour meter electric quantity calculation method of spacecraft storage battery |
| CN112379275A (en) * | 2020-11-23 | 2021-02-19 | 中国电子科技集团公司第十八研究所 | Multi-parameter corrected power battery SOC estimation method and estimation system |
| CN113391212A (en) * | 2021-06-23 | 2021-09-14 | 山东大学 | Lithium ion battery equivalent circuit parameter online identification method and system |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103439668A (en) * | 2013-09-05 | 2013-12-11 | 桂林电子科技大学 | Charge state evaluation method and system of power lithium ion battery |
| CN103675683A (en) * | 2012-09-02 | 2014-03-26 | 东莞市振华新能源科技有限公司 | Lithium battery state of charge (SOC) estimation method |
| CN105572596A (en) * | 2016-01-20 | 2016-05-11 | 上海交通大学 | Lithium battery SOC estimation method and system |
| CN105842633A (en) * | 2016-05-30 | 2016-08-10 | 广西大学 | Method for estimating SOC (State of Charge) of lithium ion battery based on gray extended Kalman filtering algorithm |
| CN106093793A (en) * | 2016-07-28 | 2016-11-09 | 河南许继仪表有限公司 | A kind of SOC estimation method based on battery discharge multiplying power and device |
| CN106291375A (en) * | 2016-07-28 | 2017-01-04 | 河南许继仪表有限公司 | A kind of SOC estimation method based on cell degradation and device |
| CN106443473A (en) * | 2016-10-09 | 2017-02-22 | 西南科技大学 | SOC estimation method for power lithium ion battery group |
| CN107219466A (en) * | 2017-06-12 | 2017-09-29 | 福建工程学院 | A kind of lithium battery SOC estimation method for mixing EKF |
| CN107390127A (en) * | 2017-07-11 | 2017-11-24 | 欣旺达电动汽车电池有限公司 | A kind of SOC estimation method |
| CN107402353A (en) * | 2017-06-30 | 2017-11-28 | 中国电力科学研究院 | A kind of state-of-charge to lithium ion battery is filtered the method and system of estimation |
| CN107576915A (en) * | 2017-08-31 | 2018-01-12 | 北京新能源汽车股份有限公司 | Battery capacity estimation method and device |
| CN107831448A (en) * | 2017-11-07 | 2018-03-23 | 国网江苏省电力公司盐城供电公司 | A kind of state-of-charge method of estimation of parallel connection type battery system |
| CN108008320A (en) * | 2017-12-28 | 2018-05-08 | 上海交通大学 | A kind of charge states of lithium ion battery and the adaptive combined method of estimation of model parameter |
-
2018
- 2018-06-06 CN CN201810576541.7A patent/CN108732509A/en active Pending
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103675683A (en) * | 2012-09-02 | 2014-03-26 | 东莞市振华新能源科技有限公司 | Lithium battery state of charge (SOC) estimation method |
| CN103439668A (en) * | 2013-09-05 | 2013-12-11 | 桂林电子科技大学 | Charge state evaluation method and system of power lithium ion battery |
| CN105572596A (en) * | 2016-01-20 | 2016-05-11 | 上海交通大学 | Lithium battery SOC estimation method and system |
| CN105842633A (en) * | 2016-05-30 | 2016-08-10 | 广西大学 | Method for estimating SOC (State of Charge) of lithium ion battery based on gray extended Kalman filtering algorithm |
| CN106093793A (en) * | 2016-07-28 | 2016-11-09 | 河南许继仪表有限公司 | A kind of SOC estimation method based on battery discharge multiplying power and device |
| CN106291375A (en) * | 2016-07-28 | 2017-01-04 | 河南许继仪表有限公司 | A kind of SOC estimation method based on cell degradation and device |
| CN106443473A (en) * | 2016-10-09 | 2017-02-22 | 西南科技大学 | SOC estimation method for power lithium ion battery group |
| CN107219466A (en) * | 2017-06-12 | 2017-09-29 | 福建工程学院 | A kind of lithium battery SOC estimation method for mixing EKF |
| CN107402353A (en) * | 2017-06-30 | 2017-11-28 | 中国电力科学研究院 | A kind of state-of-charge to lithium ion battery is filtered the method and system of estimation |
| CN107390127A (en) * | 2017-07-11 | 2017-11-24 | 欣旺达电动汽车电池有限公司 | A kind of SOC estimation method |
| CN107576915A (en) * | 2017-08-31 | 2018-01-12 | 北京新能源汽车股份有限公司 | Battery capacity estimation method and device |
| CN107831448A (en) * | 2017-11-07 | 2018-03-23 | 国网江苏省电力公司盐城供电公司 | A kind of state-of-charge method of estimation of parallel connection type battery system |
| CN108008320A (en) * | 2017-12-28 | 2018-05-08 | 上海交通大学 | A kind of charge states of lithium ion battery and the adaptive combined method of estimation of model parameter |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109324294A (en) * | 2018-11-22 | 2019-02-12 | 联想(北京)有限公司 | Data processing method and its device |
| CN110133525A (en) * | 2019-05-13 | 2019-08-16 | 哈尔滨工业大学 | A kind of health state of lithium ion battery estimation method applied to battery management system |
| CN110135527A (en) * | 2019-06-12 | 2019-08-16 | 哈尔滨工业大学 | A kind of dynamical unmanned plane charge states of lithium ion battery estimating system and method |
| CN110308395A (en) * | 2019-06-28 | 2019-10-08 | 安徽贵博新能科技有限公司 | A kind of power rating evaluation method based on multi-constraint condition battery pack |
| CN110927595A (en) * | 2019-12-17 | 2020-03-27 | 北京空间飞行器总体设计部 | Ampere-hour meter electric quantity calculation method of spacecraft storage battery |
| CN112379275A (en) * | 2020-11-23 | 2021-02-19 | 中国电子科技集团公司第十八研究所 | Multi-parameter corrected power battery SOC estimation method and estimation system |
| CN113391212A (en) * | 2021-06-23 | 2021-09-14 | 山东大学 | Lithium ion battery equivalent circuit parameter online identification method and system |
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