CN109870651A - A kind of electric automobile power battery system SOC and SOH joint estimation on line method - Google Patents
A kind of electric automobile power battery system SOC and SOH joint estimation on line method Download PDFInfo
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Abstract
The invention belongs to electric automobile power battery management domains, are related to a kind of electric automobile power battery system SOC and SOH joint estimation on line method.This method mainly comprises the steps that first, acquires experimental data, establishes battery model and pick out model parameter initial value, obtain the coefficient initial value A of space equation0、B0、C0、D0；Second, two spreading kalman (EKF) filtering rings are respectively to battery pack SOC and internal resistance R0It is estimated, the two estimation result can be corrected mutually；Third, by the estimation result SOC and R of step 20It is input to the model parameter R for exporting and picking out in BCRLS algorithm0、R1、C1, and update space equation coefficient Ak、Bk、Ck、DkCarry out SOC and the SOH estimation of subsequent time；This method has merged double card Kalman Filtering and BCRLS algorithm, solve the problems, such as that algorithm caused by uncertain noise no longer has unbiasedness, improve the precision of battery pack model, double Kalman filtering algorithms efficiently solve the influence that online SOC value estimates battery SOH, improve the estimation precision and robustness of SOH.
Description
Technical field
The invention belongs to batteries of electric automobile management domains, are related to a kind of battery system SOC and SOH joint estimation on line side
Method.
Background technique
In recent years, the development of electric car is especially burning hot, three electric systems one of of the electrokinetic cell system as electric car,
The performance of power battery directly affects the performance of vehicle.The state-of-charge (SOC) of battery is reflection power battery residual capacity
With acting ability an important indicator, and cell health state (SOH) reflection be remaining battery cycle life, power battery
The real-time accurate estimation of system SOC and SOH have weight for improving battery safety in utilization, battery life, electric car performance
The theory significance and application value wanted.
Internal state parameter of the SOC and SOH as power battery, can not directly measure, can only be by cell voltage, electricity
The measurement of the parameters such as stream, internal resistance calculates indirectly.Common SOC estimation method has current integration method, open circuit voltage method, nerve net
Network method, Kalman filtering method etc., common SOH evaluation method are defined method, discharge test method, resistance Commutation Law, AC impedance
Analytic approach, Kalman filtering method, support vector machines method etc..Kalman filtering method is all answered in terms of SOC estimation and SOH estimation
With, but be all usually estimation research individually to be carried out to SOC and SOH, and Kalman filtering algorithm is a kind of state based on model
Optimal estimation method, the algorithm are higher to the required precision of battery model, and the inaccuracy of battery model will lead to Kalman filtering
Diverging is to lose filter action.
Summary of the invention
The purpose of the present invention is in view of the above shortcomings of the prior art, provide a kind of electric automobile power battery system
SOC and SOH combines estimation on line method, to establish the battery model of degree of precision, solves electrokinetic cell system SOC and SOH connection
Close estimation on line problem.
The purpose of the present invention can be achieved through the following technical solutions: a kind of electric automobile power battery system SOC and SOH
Joint estimation on line method, for the real-time online estimation of electric automobile power battery system SOC and SOH, comprising steps of
S1: to electric automobile power battery group carry out fast charging and discharging experiment, calibration battery open circuit voltage (OCV) with
Relational expression between SOC, battery pack Dai Weining model (Thevenin model) initial parameter value are different by carrying out to battery pack
Pulse under SOC value, which is tested, to be obtained, and at the beginning of obtaining the matching factor of batteries of electric automobile group model corresponding state space equation
Value matrix A0、B0、C0、D0；
S2: end voltage U, the electric current I of electric automobile power battery group are obtained；
S3: according to batteries of electric automobile group Thevenin equivalent-circuit model column write circuit fundamental equation, and according to pulse
Response invariant method to battery circuit equation carry out sliding-model control, and will be discrete after equation be rewritten into least squares formalism,
Then the pass between OCV and SOC obtained with deviation compensation recursive least squares algorithm (BCRLS algorithm) and in conjunction with previous step
It is formula, with the voltage, electric current, temperature data of the battery pack of previous step acquisition to electric automobile power battery group model parameter
R0、R1、C1On-line identification is carried out, the A in system state equation is then updatedk、Bk、Ck、Dk；
S4:SOC estimation block, which is considered as constant value for electric automobile power battery group ohmic internal resistance, with extension
The SOC of Kalman filtering (EKF) algorithm estimation k moment electric automobile power battery groupkValue, by the SOC at k momentkValue input OCV
In SOC relational expression module, the open-circuit voltage values U at k moment is obtainedoc(SOCk), and by the open-circuit voltage values U at k momentoc(SOCk)
With k moment battery pack polarizing voltage value U1,kIt is input to the calculating that model observation is carried out in the SOH estimation block of next step；
S5:SOH estimation block estimates k moment electric automobile power battery group with Extended Kalman filter (EKF) algorithm
Ohmic internal resistance value R0,k, then according to relational expressionThe estimation of SOH is carried out, wherein REolFor battery pack
Internal resistance value when life termination, RNewIndicate internal resistance value when battery pack is not used, R0,kIndicate the battery pack at the k moment of estimation
Ohmic internal resistance value, by the ohmic internal resistance value R at k moment0,kIt is input in the SOC estimation block of previous step, carries out model observation
Calculating.
The beneficial effects of the present invention are:
1. being recognized with deviation compensation recursive least-squares (BCRLS) algorithm to power battery model parameter, effectively
It solves data multiplication and the moment is saturated, no with big measurement noise, and conventional the least square theory identification because there are data
Identification model caused by certainty noise no longer has the problem of unbiasedness, ensure that the high-precision characteristic of battery model, keeps away
Exempt from influence of the model parameter variation to algorithm estimation precision, improves the robustness of algorithm.
2. double expanded Kalman filtration algorithms that the present invention uses, can be simultaneously to the SOC value and SOH of electrokinetic cell system
Value is estimated, efficiently solves the problems, such as battery model time-varying characteristics and the influence that online SOC value estimates power battery SOH,
The estimation precision of power battery SOH is improved, and double Kalman filtering algorithms are parallel computations, operational efficiency is higher.
Detailed description of the invention
Fig. 1: electrokinetic cell system SOC and SOH combine estimation on line algorithm flow chart
Fig. 2: power battery pack Dai Weining (Thevenin) equivalent-circuit model figure
Specific embodiment
In the following, being further described in conjunction with attached drawing to a specific embodiment of the invention.
The present invention provides a kind of electric automobile power battery system SOC and SOH to combine estimation on line method, this method stream
Journey figure is below described in detail each implementation steps as shown in Figure 1, including step S1-S5:
S1: to electric automobile power battery group carry out fast charging and discharging experiment, calibration battery open circuit voltage (OCV) with
Relational expression between SOC, battery pack Dai Weining model (Thevenin model) initial parameter value are different by carrying out to battery pack
Pulse under SOC value, which is tested, to be obtained, and at the beginning of obtaining the matching factor of batteries of electric automobile group model corresponding state space equation
Value matrix A0、B0、C0、D0；
S3: the equivalent-circuit model of electric automobile power battery group is as shown in Fig. 2, wherein Uoc(SOC) what is indicated is battery
The open-circuit voltage of group, R0Indicate ohmic internal resistance, R1Indicate polarization resistance, C1Indicate that polarization capacity, I are load current (input quantity),
U is end voltage (output quantity), column write circuit equation are as follows:
U=Uoc(SOC)+IR0+U1
Enable y=U-Uoc(SOC), then ssystem transfer function isBy impulse response not political reformCan obtain it is discrete after system difference equation are as follows:
yk=k1yk-1+k2I(k)+k3I(k-1)
It is converted into least squares formalism are as follows: yk=h (k)TθLS+ e (k), wherein h (k)T=[y (k-1) I (k) I (k-1)]
For sample set, θLS=[k1 k2 k3]TFor parameter to be identified, e (k) is to measure noise；It is calculated with deviation compensation recursive least-squares
Method carries out model parameter on-line identification, and algorithm key step is as follows:
S301: parameter initialization
Wherein,For deviation compensation recursive least squares algorithm identification initial value,For the calculation of common least square
The initial value of method identification, J (0) are cost function initial value, and P (0) is covariance initial value, and ε is lesser real vector (by user
Oneself setting), δ is lesser positive number (being set by user oneself)；
S302: forecasting system output and evaluated error calculate
WhereinIt is exported for forecasting system,For system estimation error；
S303: gain matrix calculates
K (k)=P (k-1) h (k) [1+h (k)TP(k-1)h(k)]-1
S304: least-squares parameter estimation
S305: error cost function calculates
S306: Noise Variance Estimation
Wherein Λ is weighting matrix, can be set by user oneself；
S307: covariance matrix and parameter error compensation are updated
P (k)=[I0-K(k)h(k)T]P(k-1)
Output bias compensates the parametric results of recursive least squares algorithm identificationThen according to following formula:
Find out batteries of electric automobile group model parameter R0、R1、C1；
S4: Extended Kalman filter (EKF) algorithm estimates electric automobile power battery group SOC module, needs to know battery
The state space equation of group, then by SOC and battery pack polarizing voltage U1,kAs state variable, battery pack ohmic internal resistance R0,kWhen
Make constant, detailed process is as follows: setting system state variables as Xk=[SOCk U1,k]T, electric automobile power battery group state sky
Between equation be
Wherein WkFor systematic procedure noise, QkFor process-noise variance intensity battle array, VkFor system measurements noise, RkTo measure
Noise variance intensity battle array, τ=R1C1For time constant, Δ t is the systematic sampling time, and η is battery set charge/discharge efficiency, CnFor electricity
Pond group rated capacity, ikFor load current, contrast standard EKF algorithm state equation can then be obtained:
Then k moment SOC estimation block EKF algorithm specifically executes following steps:
S401: computing system state one-step prediction valueWhereinBecome for the state at system k-1 moment
Amount, original state variable set to obtain by user；
S402: computing system one-step prediction mean square error battle array Pk,k-1=Ak-1Pk-1Ak-1 T+Qk-1, wherein Ak-1 TFor Ak-1Turn
Set matrix, computing system filtering gain battle array Kk=Pk,k-1Ck T(CkPk,k-1Ck T+Rk)-1；
S403: battery pack ohmic internal resistance initial value R in obtaining step S50,k-1Computing system model output valueComputing system state estimationWherein ZkFor system actual measured value, calculate
System state estimation mean squared error matrix Pk=(I-KkCk)Pk,k-1(I-KkCk)T+KkRkKk T, output system state estimation amountInto
The calculating of the model output at k moment in the status predication and step 5 at row k+1 moment；
S5: Extended Kalman filter (EKF) algorithm estimates electric automobile power battery group ohmic internal resistance R0,kModule, by Europe
Nurse internal resistance is set as state variable, and specific implementation process is as follows:
Electric automobile power battery group state space equation are as follows:
Wherein γk-1For systematic procedure noise, Qk' it is process-noise variance intensity battle array, βkFor system measurements noise, Rk' be
Measuring noise square difference intensity battle array, contrast standard EKF algorithm state space equation are known:Then k moment EKF
Algorithm estimates electric automobile power battery group ohmic internal resistance R0,kIt is specific to execute following steps:
S501: computing system state one-step prediction valueWhereinFor the system k-1 moment
State variable, original state variable set to obtain by user；
S502: computing system one-step prediction mean square error battle arrayWhereinForTransposed matrix, computing system filtering gain battle array
S503: the SOC at the k moment that EKF algorithm is estimated in obtaining step S4kValue substitutes into the relationship between OCV and SOC
Formula obtains current SOCkOpen-circuit voltage values U' under valueoc(SOCk), then carry out the calculating of model output valueComputing system state estimationWherein Z'kFor system
Actual measured value, computing system state estimation mean squared error matrixOutput
System mode estimatorCarry out the calculating of the model output at k moment in the status predication and step 4 of subsequent time k+1.
Specific embodiment of the present invention is presented above, but the present invention is not limited to described embodiment.
All within the spirits and principles of the present invention, in such a way that those skilled in the art are readily apparent that, carry out any modification, etc.
With replacement, improvement etc., should all be included in the protection scope of the present invention.
Claims (5)
1. a kind of electric automobile power battery system SOC and SOH combines estimation on line method, it to be used for electric automobile power battery system
The real-time online estimation of system SOC and SOH, comprising steps of
S1: to electric automobile power battery group carry out fast charging and discharging experiment, calibration battery open circuit voltage (OCV) and SOC it
Between relational expression, battery pack Dai Weining model (Thevenin model) initial parameter value, which passes through, carries out different SOC values to battery pack
Under pulse test and obtain, and obtain the matching factor matrix of initial value of batteries of electric automobile group model corresponding state space equation
A0、B0、C0、D0；
S2: end voltage U, the electric current I of electric automobile power battery group are obtained；
S3: according to batteries of electric automobile group Thevenin equivalent-circuit model column write circuit fundamental equation, and according to impulse response
Not political reform to battery circuit equation carry out sliding-model control, and will be discrete after equation be rewritten into least squares formalism, then
The relationship between OCV and SOC obtained with deviation compensation recursive least squares algorithm (BCRLS algorithm) and combination previous step
Formula, with the voltage, electric current, temperature data of the battery pack of previous step acquisition to electric automobile power battery group model parameter R0、
R1、C1On-line identification is carried out, the A in system state equation is then updatedk、Bk、Ck、Dk；
Electric automobile power battery group ohmic internal resistance is considered as constant value by S4:SOC estimation block, the module, with extension karr
The SOC of graceful filtering (EKF) algorithm estimation k moment electric automobile power battery groupkValue, by the SOC at k momentkValue input OCV and SOC
In relational expression module, the open-circuit voltage values U at k moment is obtainedoc(SOCk), and by the open-circuit voltage values U at k momentoc(SOCk) and when k
Carve battery pack polarizing voltage value U1,kIt is input to the calculating that model observation is carried out in the SOH estimation block of next step；
S5:SOH estimation block, with the Europe of Extended Kalman filter (EKF) algorithm estimation k moment electric automobile power battery group
Nurse internal resistance value R0,k, then according to relational expressionThe estimation of SOH is carried out, wherein REolFor battery life
Internal resistance value when termination, RNewIndicate internal resistance value when battery pack is not used, R0,kIndicate ohm of the battery pack at the k moment of estimation
Internal resistance value, by the ohmic internal resistance value R at k moment0,kIt is input in the SOC estimation block of previous step, carries out the meter of model observation
It calculates.
2. a kind of electric automobile power battery system SOC according to claim 1 and SOH combines estimation on line method,
It is characterized in that, in the step S1, passes through the pass between fast charging and discharging experimental calibration battery open circuit voltage (OCV) and SOC
It is formula, the relational expression being fitted by 1stOpt software are as follows:
Uoc(SOC)=p1+p2*SOC^0.5+p3*SOC+p4*SOC^1.5+p5*SOC^2+p6*SOC^2.5+p7*S OC^3+p8*
SOC^3.5+p9*SOC^4+p10*SOC^4.5
Wherein p1=34.922, p2=-58.731, p3=46.274, p4=-20.329, p5=5.508, p6=-0.956, p7
=0.107, p8=-0.007, p9=0.0002, p10=-4.851.
3. a kind of electric automobile power battery system SOC according to claim 1 and SOH combines estimation on line method,
It is characterized in that, in the step S3, the deviation compensation recursive least-squares Identification of parameter proposed be can effectively solve: because of electricity
Electrical automobile power battery pack real time data doubles and the moment is with big measurement noise, and conventional the least square theory identification is because going out
Identification model caused by existing data saturation, uncertain noise no longer has the problem of unbiasedness, the algorithm can preferably with
Track system dynamic operational behaviour.
4. a kind of electric automobile power battery system SOC according to claim 1 and SOH combines estimation on line method,
It is characterized in that, in the step S4, sets system state variables as Xk=[SOCk U1,k]T, electric automobile power battery group state
Space equation is
Wherein WkFor systematic procedure noise, QkFor process-noise variance intensity battle array, VkFor system measurements noise, RkTo measure noise side
Poor intensity battle array, τ=R1C1For time constant, Δ t is the systematic sampling time, and η is battery set charge/discharge efficiency, CnFor battery pack volume
Constant volume, ikFor load current, contrast standard EKF algorithm state equation can then be obtained:
Then k moment SOC estimation block EKF algorithm specifically executes following steps:
Step S4.1: computing system state one-step prediction valueWhereinBecome for the state at system k-1 moment
Amount, original state variable set to obtain by user；
Step S4.2: computing system one-step prediction mean square error battle array Pk,k-1=Ak-1Pk-1Ak-1 T+Qk-1, wherein Ak-1 TFor Ak-1Turn
Set matrix, computing system filtering gain battle array Kk=Pk,k-1Ck T(CkPk,k-1Ck T+Rk)-1；
Step S4.3: battery pack ohmic internal resistance initial value R in obtaining step S50,k-1Computing system model output valueComputing system state estimationWherein ZkFor system actual measured value, calculate
System state estimation mean squared error matrix Pk=(I-KkCk)Pk,k-1(I-KkCk)T+KkRkKk T, output system state estimation amountInto
The calculating of the model output at k moment in the status predication and step 5 at row k+1 moment.
5. a kind of electric automobile power battery system SOC according to claim 1 and SOH combines estimation on line method,
Be characterized in that, in the step S5, set system state variables asThen electric automobile power battery group state space
Equation is
Wherein γk-1For systematic procedure noise, Qk' it is process-noise variance intensity battle array, βkFor system measurements noise, Rk' it is to measure
Noise variance intensity battle array, contrast standard EKF algorithm state space equation are known:Then k moment EKF algorithm
Estimate electric automobile power battery group ohmic internal resistance R0,kIt is specific to execute following steps:
Step S5.1: computing system state one-step prediction valueWhereinFor the system k-1 moment
State variable, original state variable set to obtain by user；
Step S5.2: computing system one-step prediction mean square error battle arrayWhereinForTransposed matrix, computing system filtering gain battle array
Step S5.3: the SOC at the k moment that EKF algorithm is estimated in obtaining step S4kValue substitutes into the relationship between OCV and SOC
Formula obtains current SOCkOpen-circuit voltage values U' under valueoc(SOCk), then carry out the calculating of model output valueComputing system state estimationWherein Z'kFor system
Actual measured value, computing system state estimation mean squared error matrixOutput
System mode estimatorCarry out the calculating of the model output at k moment in the status predication and step 4 of subsequent time k+1.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| CN110261779A (en) * | 2019-06-25 | 2019-09-20 | 西安石油大学 | A kind of ternary lithium battery charge state cooperates with estimation method with health status online |
| CN110308405A (en) * | 2019-07-26 | 2019-10-08 | 四川嘉垭汽车科技有限公司 | Charge states of lithium ion battery and health status estimation method at a temperature of varying environment |
| CN110837049A (en) * | 2019-11-26 | 2020-02-25 | 无锡物联网创新中心有限公司 | Lithium ion power battery state estimation method based on UKF algorithm |
| CN110850299A (en) * | 2019-11-26 | 2020-02-28 | 无锡物联网创新中心有限公司 | SR-UKF-based lithium ion power battery state estimation method |
| CN110888058A (en) * | 2019-12-02 | 2020-03-17 | 西安科技大学 | Algorithm based on power battery SOC and SOH joint estimation |
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| CN111337832A (en) * | 2019-12-30 | 2020-06-26 | 南京航空航天大学 | Power battery multidimensional fusion SOC and SOH online joint estimation method |
| CN111668865A (en) * | 2020-07-21 | 2020-09-15 | 广东电网有限责任公司电力科学研究院 | Hierarchical control method and related device for echelon utilization energy storage system |
| CN112147515A (en) * | 2020-09-29 | 2020-12-29 | 厦门金龙联合汽车工业有限公司 | New energy vehicle battery system power and internal resistance online estimation method |
| CN112213653A (en) * | 2019-10-30 | 2021-01-12 | 蜂巢能源科技有限公司 | Battery cell state of charge estimation method of power battery and battery management system |
| CN112557925A (en) * | 2020-11-11 | 2021-03-26 | 国联汽车动力电池研究院有限责任公司 | Lithium ion battery SOC estimation method and device |
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| CN113466710A (en) * | 2021-08-17 | 2021-10-01 | 南京工程学院 | SOC and SOH collaborative estimation method for energy storage battery in receiving-end power grid containing new energy |
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| CN113567861A (en) * | 2021-07-06 | 2021-10-29 | 上海电机学院 | Estimation method for health state of power battery |
| CN115201687A (en) * | 2022-07-13 | 2022-10-18 | 西南交通大学 | Battery model parameter and SoC joint estimation method based on online broadband impedance |
| CN115656839A (en) * | 2022-12-21 | 2023-01-31 | 四川帝威能源技术有限公司 | Battery state parameter collaborative estimation method based on BP-DEKF algorithm |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106054080A (en) * | 2016-06-06 | 2016-10-26 | 电子科技大学 | Joint estimation method for State of Charge (SOC) and State of Health (SOH) of power battery |
| KR20160128000A (en) * | 2015-04-28 | 2016-11-07 | 영남대학교 산학협력단 | Apparatus and method of battery diagnostic |
| CN107037374A (en) * | 2017-05-04 | 2017-08-11 | 广东电网有限责任公司电力科学研究院 | A kind of SOC and SOH combined estimation methods of lithium ion battery |
| 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 |
| CN108508366A (en) * | 2017-02-28 | 2018-09-07 | 霍尼韦尔国际公司 | The online determination of the model parameter of lead-acid battery and the calculating of SOC and SOH |
| CN108508371A (en) * | 2018-04-09 | 2018-09-07 | 重庆大学 | A kind of power battery SOC/SOH/SOP combined estimation methods based on equivalent-circuit model |
-
2019
- 2019-01-22 CN CN201910059323.0A patent/CN109870651A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20160128000A (en) * | 2015-04-28 | 2016-11-07 | 영남대학교 산학협력단 | Apparatus and method of battery diagnostic |
| CN106054080A (en) * | 2016-06-06 | 2016-10-26 | 电子科技大学 | Joint estimation method for State of Charge (SOC) and State of Health (SOH) of power battery |
| CN108508366A (en) * | 2017-02-28 | 2018-09-07 | 霍尼韦尔国际公司 | The online determination of the model parameter of lead-acid battery and the calculating of SOC and SOH |
| CN107037374A (en) * | 2017-05-04 | 2017-08-11 | 广东电网有限责任公司电力科学研究院 | A kind of SOC and SOH combined estimation methods of lithium ion battery |
| 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 |
| CN108508371A (en) * | 2018-04-09 | 2018-09-07 | 重庆大学 | A kind of power battery SOC/SOH/SOP combined estimation methods based on equivalent-circuit model |
Non-Patent Citations (2)
| Title |
|---|
| 于海波等: "基于混合卡尔曼滤波和H滤波的动力电池SOC估计", 《电力电子技术》 * |
| 张民: "基于EKF的电动汽车锂离子电池SOC、SOH估算", 《中国优秀硕士学位论文全文数据库 工程科技II辑》 * |
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| CN110161423A (en) * | 2019-06-26 | 2019-08-23 | 重庆大学 | A kind of dynamic lithium battery state joint estimation method based on various dimensions coupling model |
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| CN110308405B (en) * | 2019-07-26 | 2021-05-04 | 四川嘉垭汽车科技有限公司 | Method for estimating state of charge and state of health of lithium ion battery at different ambient temperatures |
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| CN110837049A (en) * | 2019-11-26 | 2020-02-25 | 无锡物联网创新中心有限公司 | Lithium ion power battery state estimation method based on UKF algorithm |
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| CN111668865B (en) * | 2020-07-21 | 2021-09-03 | 广东电网有限责任公司电力科学研究院 | Hierarchical control method and related device for echelon utilization energy storage system |
| CN112147515A (en) * | 2020-09-29 | 2020-12-29 | 厦门金龙联合汽车工业有限公司 | New energy vehicle battery system power and internal resistance online estimation method |
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| CN112557925A (en) * | 2020-11-11 | 2021-03-26 | 国联汽车动力电池研究院有限责任公司 | Lithium ion battery SOC estimation method and device |
| CN112557925B (en) * | 2020-11-11 | 2023-05-05 | 国联汽车动力电池研究院有限责任公司 | Lithium ion battery SOC estimation method and device |
| CN112710955A (en) * | 2020-12-16 | 2021-04-27 | 上海理工大学 | Algorithm for improving battery capacity estimation precision |
| CN113093014A (en) * | 2021-03-31 | 2021-07-09 | 山东建筑大学 | Online collaborative estimation method and system for SOH and SOC based on impedance parameters |
| CN113093014B (en) * | 2021-03-31 | 2022-05-27 | 山东建筑大学 | Online collaborative estimation method and system for SOH and SOC based on impedance parameters |
| CN113176505A (en) * | 2021-04-30 | 2021-07-27 | 重庆长安新能源汽车科技有限公司 | On-line estimation method and device for state of charge and state of health of vehicle-mounted power battery and storage medium |
| CN113504481A (en) * | 2021-06-07 | 2021-10-15 | 华南理工大学 | Multi-state joint estimation system and method for power battery of electric vehicle |
| CN113567861A (en) * | 2021-07-06 | 2021-10-29 | 上海电机学院 | Estimation method for health state of power battery |
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