CN109239604A - A kind of Unscented kalman filtering on-vehicle battery SOC estimation method based on state-detection mechanism - Google Patents
A kind of Unscented kalman filtering on-vehicle battery SOC estimation method based on state-detection mechanism Download PDFInfo
- Publication number
- CN109239604A CN109239604A CN201811245486.XA CN201811245486A CN109239604A CN 109239604 A CN109239604 A CN 109239604A CN 201811245486 A CN201811245486 A CN 201811245486A CN 109239604 A CN109239604 A CN 109239604A
- Authority
- CN
- China
- Prior art keywords
- state
- battery
- model
- kalman filtering
- unscented kalman
- 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
Landscapes
- Tests Of Electric Status Of Batteries (AREA)
- Secondary Cells (AREA)
Abstract
The Unscented kalman filtering on-vehicle battery SOC estimation method based on state-detection mechanism that the invention discloses a kind of, belong to vehicle mounted dynamic battery technical field, include the following steps: to establish battery equivalent model, using composite model method combination ampere-hour method, determines the model equation of on-vehicle battery；Model parameter debates knowledge, and least square method of recursion recognizes the relevant parameter of battery model battery observation model equation, and system input quantity is Persistent Excitation, and debating knowledge the number of iterations restrains the final result of parameter and tend towards stability；The SOC of on-vehicle battery is estimated precisely in real time, the Unscented kalman filtering algorithm using the state-detection mechanism based on residual information characteristic estimates battery SOC.For the present invention based on observing residual information, the detection of state-detection condition is abnormal, and the associated covariance battle array of adaptive fading factor adaptive correction Unscented kalman filtering is used under abnormal conditions, reaches and solves the problems, such as that tracking convergence rate is slow and robustness is weak.
Description
Technical field
The present invention relates to a kind of on-vehicle battery SOC estimation methods, more particularly to a kind of nothing based on state-detection mechanism
Mark Kalman filtering on-vehicle battery SOC estimation method, belongs to vehicle mounted dynamic battery technical field.
Background technique
Currently, the problem not good enough for vehicle mounted dynamic battery SOC estimated accuracy, Unscented kalman filtering algorithm are directed to battery
Nonlinear characteristic improve SOC using the Posterior Mean and variance of the non-property system mode of the above precision approximate Gaussian of second order and estimate
Precision and better astringency are counted, is a kind of common approach of current battery SOC estimation, but the algorithm model mistake occurs in battery
Difference, state initial value design error or when state mutation situation, be easy to cause the robustness of algorithm to be deteriorated, estimated accuracy reduction and with
The problem of track reduced capability.
In view of the above problems, numerous improved methods are produced, wherein more typically having:
1, it predicts and updates battery model parameter in real time using neural network, reduce model time-varying error to Unscented kalman
The influence of estimation effect is filtered, but this method relies on amount of training data and method constitutes excessively complexity；
2, by reducing sampled point number to algorithm and using spherical radius, it is ensured that all sampled points are in a unit hyper-sphere
On body, to improve algorithm to the robustness of initial value design, and the numerical stability of algorithm is improved using square root filtering, but with
Track convergence rate is slightly slow；
3, it is newly ceased to noise covariance adaptive correction adjustment in estimation procedure using systematic observation residual error to improve system
Estimation effect, this method improves algorithm keeps track ability, but adaptive process adjustment state noise variance is easy to cause and estimates
It counts waveform and generates buffeting.
Summary of the invention
The main object of the present invention is to provide for a kind of Unscented kalman filtering vehicle mounted electric based on state-detection mechanism
Pond SOC estimation method, based on observing residual information, the detection of state-detection condition is abnormal, using adaptive under abnormal conditions
The associated covariance battle array of decay factor adaptive correction Unscented kalman filtering, reaches that solution tracking convergence rate is slow and robust
The weak problem of ability.
The purpose of the present invention can reach by using following technical solution:
A kind of Unscented kalman filtering on-vehicle battery SOC estimation method based on state-detection mechanism, includes the following steps:
Step 1: establishing battery equivalent model, using composite model method combination ampere-hour method, determine the model of on-vehicle battery
Equation；
Step 2: model parameter debates knowledge, and least square method of recursion recognizes the related ginseng of battery model battery observation model equation
Number, system input quantity are Persistent Excitation, and debating knowledge the number of iterations restrains the final result of parameter and tend towards stability；
Step 3: the SOC of on-vehicle battery being estimated precisely in real time, is examined using the state based on residual information characteristic
The Unscented kalman filtering algorithm of survey mechanism estimates battery SOC.
Further, in step 1, battery equivalent model is established, according to the nonlinear characteristic of battery, merges Shepherd mould
The same entry deletion of pattern function after merging is obtained combined state by type, Unnewehr Universal model and Nernst model
Battery observation model equation can be obtained in conjunction with Ah counting method in model equation.
Further, combined state model equation are as follows:
Wherein: xkFor the SOC value at k moment；
ikThe cell current value obtained for k instance sample；
η is charge efficiency；
C is the specified total capacity of battery；
Δ t is the sampling period；
wkFor the white Gaussian noise for meeting normal distribution, i.e. wk~N (0, Qk), QkFor state-noise variance matrix.
Further, battery observation model equation are as follows:
Wherein: EoFor battery open circuit voltage；
RΩFor the internal resistance of cell；
k1、k2、k3、k4For fitting coefficient；
ykWith the battery terminal voltage obtained for k instance sample；
vkFor the white Gaussian noise for meeting normal distribution, i.e. vk~N (0, Rk), RkFor observation noise variance matrix.
Further, in step 2, model parameter debates knowledge, observes mould using well known least square method of recursion identification battery
It is as follows to obtain the recursive calculative formula for the battery observation model equation of battery observation model equation for the model parameter of type equation:
Wherein, data vector
Parameter vector to be estimated
Least squares formalism yk=HTθ+e(k)；
E (k) is error function.
Further, in step 3, the SOC of on-vehicle battery is estimated precisely in real time, is included the following steps:
Step 31: state and covariance initialization；
Step 32: calculating sampled point, it corresponds to weighting coefficient；
Step 33: state and covariance prediction；
Step 34: measurement prediction；
Step 35: calculating state and measurement prediction cross covariance；
Step 36: computing system observes residual information；
Step 37: abnormal state situation being carried out using the observed quantity abnormal state detection method based on residual information characteristic
Differentiate；
Step 38: calculating Kalman gain；
Step 39: state and covariance update；
Step 310: judge whether time iteration is completed, if it is terminates algorithm, the return step 32 if not completing.
Further, in step 31, state and covariance initialization result are as follows:
Wherein:For the SOC initial estimate of setting, as system initial state value；
P0For state error covariance initial value.
Further, in step 32, sampled point and its corresponding weighting coefficient are calculated, chooses Sigma point as particle point,
Sigma point, sampling policy are calculated using SVD decomposition method are as follows:
Pk-1=Uk-1Sk-1Vk-1 (5)
According to the statistical information of input variable, using single order and weighted corresponding to Sigma point symmetry sampling policy
Coefficient are as follows:
Wherein: α (0≤α≤1) indicates point set to the distance of average point；
λ=α2(n+ ε)-n is scaling factor, and ε is secondary scaling factor.
Further, in step 33, state and covariance prediction are as follows:
In step 34, measurement prediction is as follows:
In step 35, calculates state and measurement prediction cross covariance is as follows:
In step 36, computing system observes residual information:
In step 38, it is as follows to calculate Kalman gain:
In step 39, state and covariance update are as follows:
Further, in step 37, using the observed quantity abnormal state detection method based on residual information characteristic to state
Abnormal conditions are differentiated, detect Rule of judgment by determining with lower inequality:
Wherein vk: for systematic observation residual information；
ζ is detection limit control coefrficient；
Adaptive fading factor according to the design of residual information covariance matching principle updates observation error and mutual error association
Variance matrix, robust strategy design procedure are as follows:
A. residual information covariance matrix is calculated:
B. it calculates the present invention and proposes decay factor value:
C. it is used for decay factor λkRevised measurement updaue are as follows:
The value of adjustable attenuation coefficient μ is less than 1.
Advantageous effects of the invention:
1, the Unscented kalman filtering on-vehicle battery SOC estimation method provided by the invention based on state-detection mechanism,
The abnormal state detection mechanism for newly ceasing characteristic based on residual error is introduced in Unscented kalman filtering, is detected in estimation procedure different
When normal state, the related association side of part is updated using improved adaptive fading factor amendment Unscented kalman filtering algorithm measurement
Poor battle array, so as to effectively control SOC initial value design error and model error to the shadow of final estimation effect in a certain range
It rings.Based on battery composite model, battery parameter is recognized using least square method of recursion, gives method specific implementation step
Suddenly.
2, the Unscented kalman filtering on-vehicle battery SOC estimation method provided by the invention based on state-detection mechanism, benefit
Estimate that there are good robustness and tracking velocity, all kinds of error precisions with SOC of the method disclosed by the invention to battery
Control provides a new method within 5% for the estimation of vehicle mounted dynamic battery SOC.
3, the Unscented kalman filtering on-vehicle battery SOC estimation method provided by the invention based on state-detection mechanism,
In the case where exception is not detected during estimation SOC, is estimated with classical Unscented kalman filtering method, ensure that estimation wave
The precision and flatness of shape compensate for the simple decay factor that introduces and are easy that estimation waveform has been guided to buffet.
4, the Unscented kalman filtering on-vehicle battery SOC estimation method provided by the invention based on state-detection mechanism,
In the case where detecting exception, introducing decay factor method, which guarantees quickly to track, converges to target, effectively overcomes model
And initial value design error, summarize the present invention has the advantages that
(1) it is few that the parameter that the novel state-detection mechanism of characteristic is related to newly is ceased based on residual error, based on automatic adjusument,
Debugging is convenient, and abnormal state detection effect is obvious.
(2) the novel decay factor correction strategy introduced is suitable for the estimation of vehicle mounted dynamic battery SOC, misses to battery model
The robustness of difference is good, strong to initial value design error tracking convergence capabilities.
Detailed description of the invention
Fig. 1 is the Unscented kalman filtering on-vehicle battery SOC estimation method according to the invention based on state-detection mechanism
A preferred embodiment flow chart.
Specific embodiment
To make the more clear and clear technical solution of the present invention of those skilled in the art, below with reference to examples and drawings
The present invention is described in further detail, and embodiments of the present invention are not limited thereto.
As shown in Figure 1, the Unscented kalman filtering on-vehicle battery SOC provided in this embodiment based on state-detection mechanism estimates
Meter method, includes the following steps:
Step 1: establishing battery equivalent model
According to the nonlinear characteristic of battery, merge common Shepherd model, Unnewehr Universal model and
The same entry deletion of pattern function after merging is obtained the composite model of formula (2), composite model is in fitting essence by Nernst model
Advantage is had more on degree, and battery observation model equation can be obtained in conjunction with Ah counting method:
Combined state model equation:
Wherein: xkFor the SOC value at k moment, ikFor the cell current value that k instance sample obtains, η is charge efficiency；C is electricity
The specified total capacity in pond；Δ t is the sampling period；wkFor the white Gaussian noise for meeting normal distribution, i.e. wk~N (0, Qk), QkFor shape
State noise variance matrix；
Battery observation model equation:
Wherein: EoFor battery open circuit voltage；RΩFor the internal resistance of cell；k1、k2、k3、k4For fitting coefficient；ykIt is adopted with for the k moment
The battery terminal voltage that sample obtains；vkFor the white Gaussian noise for meeting normal distribution, i.e. vk~N (0, Rk), RkFor observation noise variance
Battle array；
Step 2: model parameter debates knowledge
Using well known least square method of recursion distinguishing type (2) model parameter, this method is united in unknown observation data probability
There is result good statistical property to take data vector H for the battery observation model equation of formula (2) under meter information state
(k)=[1, ik,xk,1/xk,ln(xk),ln(1-xk)], parameter vector to be estimatedLeast squares formalism yk
=HTθ+e (k), wherein e (k) is error function；
The recursive calculative formula is as follows:
Step 3: using the Unscented kalman filtering algorithm of the state-detection mechanism based on residual information characteristic to battery SOC
Estimated:
(1) state and covariance initialization:
Wherein,For the SOC initial estimate of setting, as system initial state value；P0For at the beginning of state error covariance
Value；
(2) calculating sampled point, it corresponds to weighting coefficient, and selection Sigma point is as particle point, to solve P under abnormal conditionsk-1
Orthotropicity can be lost and not be available the problem of Cholesky is decomposed, therefore, the present invention calculates Sigma using SVD decomposition method
Point；
Sampling policy are as follows:
Pk-1=Uk-1Sk-1Vk-1 (5)
According to the statistical information of input variable, using single order and weighted corresponding to Sigma point symmetry sampling policy
Coefficient are as follows:
Wherein, due to the state variable x of batterykFor single argument, state distribution parameter β is taken 0 best in this case；α(0
≤ α≤1) indicate that point set to the distance of average point, takes α=0.01 that can obtain more satisfactory effect；λ=α2(n+ ε)-n is scale tune
The factor is saved, ε is secondary scaling factor, can usually take ε=0；
(3) state and covariance prediction:
(4) measurement prediction:
(5) state and measurement prediction cross covariance are calculated:
(6) computing system observes residual information:
(7) the present embodiment is proposed using the observed quantity abnormal state detection method based on residual information characteristic to abnormal state
Situation carries out sentencing method for distinguishing, detects Rule of judgment by determining with lower inequality:
Wherein: vkFor systematic observation residual information；ζ is detection limit control coefrficient, is found by many experiments, value is usual
Preferable detection effect is obtained when ζ >=100；
When formula (12) is set up, it is abnormal to show that the system mode for SOC estimation exists, when this occurs, then draws
Enter the present invention and updates observation error and mutual error association according to the adaptive fading factor of residual information covariance matching principle design
Variance matrix, this robust strategy make system have preferable tracking velocity and precision, and robust strategy design procedure is as follows:
A. residual information covariance matrix is calculated:
B. it calculates the present invention and proposes decay factor value:
Adaptive fading factor method is introduced into greatly or even to cause to dissipate using being easy to appear fluctuation in battery SOC estimation
The problem of, the value that present invention design is added to adjustable attenuation coefficient μ, μ is affected to estimation effect, answers in actual estimated
In, μ value is generally much less than 1；
C. it is used for decay factor λkRevised measurement updaue are as follows:
The improved form of decay factor is capable of the weight ratio of active balance combined state model equation prediction and observation information,
And the influence when error occurs in the state initial value of model or setting to filter effect can be controlled in a certain range；
(8) Kalman gain is calculated
(9) state and covariance update
(10) judge whether time iteration is completed, if it is terminate algorithm, if otherwise return step (2).
In the present embodiment, the Unscented kalman filtering on-vehicle battery provided in this embodiment based on state-detection mechanism
SOC estimation method is introduced the abnormal state detection mechanism for newly being ceased characteristic based on residual error in Unscented kalman filtering, estimated
When detecting abnormality in the process, updated using improved adaptive fading factor amendment Unscented kalman filtering algorithm measurement
Partial associated covariance battle array, so as to effectively control SOC initial value design error and model error in a certain range to most
The influence of whole estimation effect.Based on battery composite model, battery parameter, the side of giving are recognized using least square method of recursion
Method implements step.
In the present embodiment, the Unscented kalman filtering on-vehicle battery provided in this embodiment based on state-detection mechanism
SOC estimation method estimates there is good robustness and tracking speed using SOC of the method disclosed by the invention to battery
Degree, all kinds of error precisions control within 5%, provide a new method for the estimation of vehicle mounted dynamic battery SOC.
In the present embodiment, the Unscented kalman filtering on-vehicle battery provided in this embodiment based on state-detection mechanism
SOC estimation method is estimated in the case where exception is not detected during estimating SOC with classical Unscented kalman filtering method
Meter ensure that the precision and flatness of estimation waveform, compensates for the simple decay factor that introduces and is easy that estimation waveform has been guided to buffet.
In the present embodiment, the Unscented kalman filtering on-vehicle battery provided in this embodiment based on state-detection mechanism
SOC estimation method, in the case where detecting exception, introducing decay factor method, which guarantees quickly to track, converges to target,
Effectively overcome model and initial value design error.
In conclusion in the present embodiment, the Unscented kalman filtering provided in this embodiment based on state-detection mechanism
On-vehicle battery SOC estimation method, it is few newly to cease the parameter that the novel state-detection mechanism of characteristic is related to based on residual error, with adaptive
Based on adjusting, debugging is convenient, and abnormal state detection effect is obvious；The novel decay factor correction strategy introduced is suitable for vehicle-mounted dynamic
The estimation of power battery SOC, it is good to the robustness of battery model error, it is strong to initial value design error tracking convergence capabilities.
The above, further embodiment only of the present invention, but scope of protection of the present invention is not limited thereto, and it is any
Within the scope of the present disclosure, according to the technique and scheme of the present invention and its design adds those familiar with the art
With equivalent substitution or change, protection scope of the present invention is belonged to.
Claims (10)
1. a kind of Unscented kalman filtering on-vehicle battery SOC estimation method based on state-detection mechanism, which is characterized in that including
Following steps:
Step 1: establishing battery equivalent model, using composite model method combination ampere-hour method, determine the model equation of on-vehicle battery；
Step 2: model parameter debates knowledge, and least square method of recursion recognizes the relevant parameter of battery model battery observation model equation,
System input quantity is Persistent Excitation, and debating knowledge the number of iterations restrains the final result of parameter and tend towards stability；
Step 3: the SOC of on-vehicle battery being estimated precisely in real time, using the state-detection machine based on residual information characteristic
The Unscented kalman filtering algorithm of system estimates battery SOC.
2. a kind of estimation side Unscented kalman filtering on-vehicle battery SOC based on state-detection mechanism as described in claim 1
Method, which is characterized in that in step 1, establish battery equivalent model, according to the nonlinear characteristic of battery, merge Shepherd model,
The same entry deletion of pattern function after merging is obtained composite-like morphotype by Unnewehr Universal model and Nernst model
Battery observation model equation can be obtained in conjunction with Ah counting method in type equation.
3. a kind of estimation side Unscented kalman filtering on-vehicle battery SOC based on state-detection mechanism as claimed in claim 2
Method, which is characterized in that combined state model equation are as follows:
Wherein: xkFor the SOC value at k moment；
ikThe cell current value obtained for k instance sample；
η is charge efficiency；
C is the specified total capacity of battery；
Δ t is the sampling period；
wkFor the white Gaussian noise for meeting normal distribution, i.e. wk~N (0, Qk), QkFor state-noise variance matrix.
4. a kind of estimation side Unscented kalman filtering on-vehicle battery SOC based on state-detection mechanism as claimed in claim 2
Method, which is characterized in that battery observation model equation are as follows:
Wherein: EoFor battery open circuit voltage；
RΩFor the internal resistance of cell；
k1、k2、k3、k4For fitting coefficient；
ykWith the battery terminal voltage obtained for k instance sample；
vkFor the white Gaussian noise for meeting normal distribution, i.e. vk~N (0, Rk), RkFor observation noise variance matrix.
5. a kind of estimation side Unscented kalman filtering on-vehicle battery SOC based on state-detection mechanism as claimed in claim 4
Method, which is characterized in that in step 2, model parameter debates knowledge, recognizes battery observation model side using well known least square method of recursion
It is as follows to obtain the recursive calculative formula for the battery observation model equation of battery observation model equation for the model parameter of journey:
Wherein, data vector
Parameter vector to be estimated
Least squares formalism yk=HTθ+e(k)；
E (k) is error function.
6. a kind of estimation side Unscented kalman filtering on-vehicle battery SOC based on state-detection mechanism as described in claim 1
Method, which is characterized in that in step 3, the SOC of on-vehicle battery is estimated precisely in real time, is included the following steps:
Step 31: state and covariance initialization；
Step 32: calculating sampled point, it corresponds to weighting coefficient；
Step 33: state and covariance prediction；
Step 34: measurement prediction；
Step 35: calculating state and measurement prediction cross covariance；
Step 36: computing system observes residual information；
Step 37: abnormal state situation being sentenced using the observed quantity abnormal state detection method based on residual information characteristic
Not；
Step 38: calculating Kalman gain；
Step 39: state and covariance update；
Step 310: judge whether time iteration is completed, if it is terminates algorithm, the return step 32 if not completing.
7. a kind of estimation side Unscented kalman filtering on-vehicle battery SOC based on state-detection mechanism as claimed in claim 6
Method, which is characterized in that in step 31, state and covariance initialization result are as follows:
Wherein:For the SOC initial estimate of setting, as system initial state value；
P0For state error covariance initial value.
8. a kind of estimation side Unscented kalman filtering on-vehicle battery SOC based on state-detection mechanism as claimed in claim 6
Method, which is characterized in that in step 32, calculate sampled point and its corresponding weighting coefficient, choose Sigma point as particle point, use
SVD decomposition method calculates Sigma point, sampling policy are as follows:
Pk-1=Uk-1Sk-1Vk-1 (5)
According to the statistical information of input variable, using single order corresponding to Sigma point symmetry sampling policy and weighted coefficient
Are as follows:
Wherein: α (0≤α≤1) indicates point set to the distance of average point；
λ=α2(n+ ε)-n is scaling factor, and ε is secondary scaling factor.
9. a kind of estimation side Unscented kalman filtering on-vehicle battery SOC based on state-detection mechanism as claimed in claim 6
Method, which is characterized in that in step 33, state and covariance prediction are as follows:
In step 34, measurement prediction is as follows:
In step 35, calculates state and measurement prediction cross covariance is as follows:
In step 36, computing system observes residual information:
In step 38, it is as follows to calculate Kalman gain:
In step 39, state and covariance update are as follows:
10. a kind of estimation side Unscented kalman filtering on-vehicle battery SOC based on state-detection mechanism as claimed in claim 6
Method, which is characterized in that in step 37, using the observed quantity abnormal state detection method based on residual information characteristic to abnormal state
Situation is differentiated, detects Rule of judgment by determining with lower inequality:
Wherein vk: for systematic observation residual information；
ζ is detection limit control coefrficient；
Adaptive fading factor according to the design of residual information covariance matching principle updates observation error and mutual error covariance
Battle array, robust strategy design procedure are as follows:
A. residual information covariance matrix is calculated:
B. it calculates the present invention and proposes decay factor value:
C. it is used for decay factorλkRevised measurement updaue are as follows:
Adjustable attenuation coefficientμValue less than 1.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811245486.XA CN109239604A (en) | 2018-10-24 | 2018-10-24 | A kind of Unscented kalman filtering on-vehicle battery SOC estimation method based on state-detection mechanism |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811245486.XA CN109239604A (en) | 2018-10-24 | 2018-10-24 | A kind of Unscented kalman filtering on-vehicle battery SOC estimation method based on state-detection mechanism |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109239604A true CN109239604A (en) | 2019-01-18 |
Family
ID=65081987
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811245486.XA Pending CN109239604A (en) | 2018-10-24 | 2018-10-24 | A kind of Unscented kalman filtering on-vehicle battery SOC estimation method based on state-detection mechanism |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109239604A (en) |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110082690A (en) * | 2019-05-06 | 2019-08-02 | 江南大学 | A kind of estimation method of vehicle-mounted fan battery state of charge |
| CN110562263A (en) * | 2019-09-19 | 2019-12-13 | 中国人民解放军陆军装甲兵学院 | Wheel hub motor driven vehicle speed estimation method based on multi-model fusion |
| CN110633761A (en) * | 2019-10-12 | 2019-12-31 | 东北大学 | Power system false data detection method based on improved Kalman filtering |
| CN111044906A (en) * | 2019-12-10 | 2020-04-21 | 深圳市鹏诚新能源科技有限公司 | Lithium ion battery energy state estimation method based on maximum likelihood criterion |
| CN111474481A (en) * | 2020-04-13 | 2020-07-31 | 深圳埃瑞斯瓦特新能源有限公司 | Battery SOC estimation method and device based on extended Kalman filtering algorithm |
| CN112285570A (en) * | 2020-10-29 | 2021-01-29 | 哈尔滨工业大学（威海） | Electric vehicle fault diagnosis method based on attenuation memory filter |
| CN112858920A (en) * | 2021-02-01 | 2021-05-28 | 山西国润储能科技有限公司 | SOC estimation method of all-vanadium redox flow battery fusion model based on adaptive unscented Kalman filtering |
| CN113219336A (en) * | 2020-02-06 | 2021-08-06 | 丰田自动车株式会社 | Battery deterioration determination system, method, and non-transitory storage medium storing program |
| CN113625177A (en) * | 2021-08-06 | 2021-11-09 | 国网安徽省电力有限公司滁州供电公司 | SOC estimation method based on particle swarm optimization particle filter algorithm |
| CN114371232A (en) * | 2021-12-22 | 2022-04-19 | 天津国科医工科技发展有限公司 | Chromatographic filtering method, device, medium and system based on Kalman filtering algorithm |
| CN114509947A (en) * | 2022-02-18 | 2022-05-17 | 淮阴工学院 | Proton exchange membrane fuel cell fault detection method based on robust Kalman filter |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101625397A (en) * | 2009-08-06 | 2010-01-13 | 杭州电子科技大学 | Mixed rapid estimation method for residual energy of battery |
| CN104714188A (en) * | 2015-03-31 | 2015-06-17 | 桂林电子科技大学 | Method and system for estimating measured noise variance matrix matched battery state of charge (SOC) |
| JP2015224927A (en) * | 2014-05-27 | 2015-12-14 | 学校法人立命館 | Storage residual amount estimation device, method of estimating storage residual amount of storage battery, and computer program |
| CN107153163A (en) * | 2017-06-20 | 2017-09-12 | 福建工程学院 | A kind of lithium battery SOC estimation method based on adaptive UKF |
-
2018
- 2018-10-24 CN CN201811245486.XA patent/CN109239604A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101625397A (en) * | 2009-08-06 | 2010-01-13 | 杭州电子科技大学 | Mixed rapid estimation method for residual energy of battery |
| JP2015224927A (en) * | 2014-05-27 | 2015-12-14 | 学校法人立命館 | Storage residual amount estimation device, method of estimating storage residual amount of storage battery, and computer program |
| CN104714188A (en) * | 2015-03-31 | 2015-06-17 | 桂林电子科技大学 | Method and system for estimating measured noise variance matrix matched battery state of charge (SOC) |
| CN107153163A (en) * | 2017-06-20 | 2017-09-12 | 福建工程学院 | A kind of lithium battery SOC estimation method based on adaptive UKF |
Non-Patent Citations (3)
| Title |
|---|
| 何灵娜: ""基于卡尔曼滤波的动力电池SOC估计算法设计"", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
| 崔乃刚 等: ""基于强跟踪UKF的导航系统故障检测方法"", 《哈尔滨工程大学学报》 * |
| 谭兴龙 等: ""基于SVD的改进抗差UKF算法及在组合导航中的应用"", 《控制与决策》 * |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110082690A (en) * | 2019-05-06 | 2019-08-02 | 江南大学 | A kind of estimation method of vehicle-mounted fan battery state of charge |
| CN110562263A (en) * | 2019-09-19 | 2019-12-13 | 中国人民解放军陆军装甲兵学院 | Wheel hub motor driven vehicle speed estimation method based on multi-model fusion |
| CN110633761A (en) * | 2019-10-12 | 2019-12-31 | 东北大学 | Power system false data detection method based on improved Kalman filtering |
| CN110633761B (en) * | 2019-10-12 | 2022-12-13 | 东北大学 | Power system false data detection method based on improved Kalman filtering |
| CN111044906A (en) * | 2019-12-10 | 2020-04-21 | 深圳市鹏诚新能源科技有限公司 | Lithium ion battery energy state estimation method based on maximum likelihood criterion |
| CN113219336A (en) * | 2020-02-06 | 2021-08-06 | 丰田自动车株式会社 | Battery deterioration determination system, method, and non-transitory storage medium storing program |
| CN111474481A (en) * | 2020-04-13 | 2020-07-31 | 深圳埃瑞斯瓦特新能源有限公司 | Battery SOC estimation method and device based on extended Kalman filtering algorithm |
| CN112285570B (en) * | 2020-10-29 | 2022-05-20 | 哈尔滨工业大学（威海） | Electric vehicle fault diagnosis method based on attenuation memory filter |
| CN112285570A (en) * | 2020-10-29 | 2021-01-29 | 哈尔滨工业大学（威海） | Electric vehicle fault diagnosis method based on attenuation memory filter |
| CN112858920A (en) * | 2021-02-01 | 2021-05-28 | 山西国润储能科技有限公司 | SOC estimation method of all-vanadium redox flow battery fusion model based on adaptive unscented Kalman filtering |
| CN112858920B (en) * | 2021-02-01 | 2022-09-27 | 山西国润储能科技有限公司 | SOC estimation method of all-vanadium redox flow battery fusion model based on adaptive unscented Kalman filtering |
| CN113625177A (en) * | 2021-08-06 | 2021-11-09 | 国网安徽省电力有限公司滁州供电公司 | SOC estimation method based on particle swarm optimization particle filter algorithm |
| CN114371232A (en) * | 2021-12-22 | 2022-04-19 | 天津国科医工科技发展有限公司 | Chromatographic filtering method, device, medium and system based on Kalman filtering algorithm |
| CN114371232B (en) * | 2021-12-22 | 2024-03-22 | 天津国科医工科技发展有限公司 | Chromatographic filtering method, device, medium and system based on Kalman filtering algorithm |
| CN114509947A (en) * | 2022-02-18 | 2022-05-17 | 淮阴工学院 | Proton exchange membrane fuel cell fault detection method based on robust Kalman filter |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109239604A (en) | A kind of Unscented kalman filtering on-vehicle battery SOC estimation method based on state-detection mechanism | |
| CN110596593B (en) | Lithium ion battery SOC estimation method based on intelligent adaptive extended Kalman filtering | |
| CN109459705B (en) | Power battery SOC estimation method of anti-outlier robust unscented Kalman filtering | |
| CN107290688B (en) | A kind of lithium battery SOC estimation method based on adaptive fuzzy Kalman filtering | |
| CN104714188B (en) | Method and system for estimating measured noise variance matrix matched battery state of charge (SOC) | |
| CN109870651A (en) | A kind of electric automobile power battery system SOC and SOH joint estimation on line method | |
| CN108490356A (en) | A kind of lithium battery SOC estimation method for improving EKF algorithms | |
| CN109726501A (en) | RLS lithium battery model parameter on-line identification method based on variable forgetting factor | |
| CN105510829B (en) | A kind of Novel lithium ion power battery SOC methods of estimation | |
| CN105425153B (en) | A kind of method of the state-of-charge for the electrokinetic cell for estimating electric vehicle | |
| CN102289557B (en) | Battery model parameter and residual battery capacity joint asynchronous online estimation method | |
| CN104021289B (en) | Non-Gaussian unsteady-state noise modeling method | |
| CN105929340B (en) | A method of battery SOC is estimated based on ARIMA | |
| CN109633479A (en) | Lithium battery SOC estimation on line method based on built-in capacitor G-card Kalman Filtering | |
| CN111537903B (en) | HCKF-based battery SOC estimation method | |
| CN113075569A (en) | Battery state of charge estimation method and device based on noise adaptive particle filtering | |
| CN106646339A (en) | Online matching and positioning method in wireless position fingerprint indoor positioning | |
| CN112858929A (en) | Battery SOC estimation method based on fuzzy logic and extended Kalman filtering | |
| CN109375111A (en) | A kind of estimation method of battery dump energy based on UHF | |
| Li et al. | State of Health and Charge Estimation Based on Adaptive Boosting integrated with particle swarm optimization/support vector machine (AdaBoost-PSO-SVM) Model for Lithium-ion Batteries | |
| CN113625182A (en) | Method for on-line estimating battery state | |
| CN106443496A (en) | Battery charge state estimation method with improved noise estimator | |
| CN115219918A (en) | Lithium ion battery life prediction method based on capacity decline combined model | |
| CN112415412A (en) | Method and device for estimating SOC value of battery, vehicle and storage medium | |
| CN106599541B (en) | A kind of structure and parameter on-line identification method of dynamic power load model |
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: 20190118 |