CN110008558A - Supercapacitor service life On-line Estimation method - Google Patents

Supercapacitor service life On-line Estimation method Download PDF

Info

Publication number
CN110008558A
CN110008558A CN201910240523.6A CN201910240523A CN110008558A CN 110008558 A CN110008558 A CN 110008558A CN 201910240523 A CN201910240523 A CN 201910240523A CN 110008558 A CN110008558 A CN 110008558A
Authority
CN
China
Prior art keywords
supercapacitor
super capacitor
estimation
equivalent
value
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
Application number
CN201910240523.6A
Other languages
Chinese (zh)
Inventor
王琪
韩晓新
诸一琦
罗印升
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Jiangsu University of Technology
Original Assignee
Jiangsu University of Technology
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Jiangsu University of Technology filed Critical Jiangsu University of Technology
Priority to CN201910240523.6A priority Critical patent/CN110008558A/en
Publication of CN110008558A publication Critical patent/CN110008558A/en
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/30—Circuit design
    • G06F30/36—Circuit design at the analogue level
    • G06F30/367—Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Hardware Design (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Microelectronics & Electronic Packaging (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Electric Double-Layer Capacitors Or The Like (AREA)

Abstract

The invention discloses a kind of supercapacitor service life On-line Estimation methods, comprising: establishes the kinetic model of supercapacitor, includes the equivalent resistance and equivalent capacity of supercapacitor in kinetic model；Ultracapacitor voltage parameter identification rule is established according to kinetic model, and the adaptive approach of supercapacitor equivalent resistance and equivalent capacity On-line Estimation is established according to voltage parameter identification rule, the On-line Estimation for realizing equivalent resistance and equivalent capacity obtains estimation resistance value and estimated capacity value；Based on Li Yapu love Theory of Stability and convergence principle, the stability and convergence of supercapacitor equivalent resistance and equivalent capacity On-line Estimation adaptive approach is analyzed；On-line Estimation is carried out to the service life of supercapacitor according to the estimation resistance value or estimated capacity value, the adaptive approach ensure that the accuracy of service life On-line Estimation, and relatively existing On-line Estimation method, operand are substantially reduced, and have saved a large amount of manpower and material resources.

Description

Online estimation method for service life of super capacitor
Technical Field
The invention relates to the technical field of super capacitors, in particular to an online service life estimation method for a super capacitor.
Background
The super capacitor provides an attractive energy storage substitute for high-performance application equipment due to the advantages of small volume, high power density and the like, can absorb and output higher current in a short time, and can particularly play an advantage in some application scenes, such as a regenerative braking state and an acceleration state of an electric/hybrid electric vehicle. As with other energy storage devices, the performance of a supercapacitor is also highly dependent on its age, which is primarily affected by the number of cycles and temperature. Therefore, timely estimation of supercapacitor life is critical to ensure its high reliability applications and fault prediction.
The methods for estimating the service life of the super capacitor are classified into an off-line method and an on-line method, wherein the off-line method comprises an alternating current signal injection method, an electrochemical impedance spectroscopy method, a time domain characterization method and the like, and although the methods are simple and easy to implement, the service life of the super capacitor can be estimated only in an off-line state by using auxiliary instruments, namely, the normal operation of the super capacitor needs to be interrupted. In addition, in the course of life estimation, since it is difficult to accurately measure the voltage and noise of the supercapacitor, the accuracy of life estimation is often not as expected. The on-line method comprises an extended Kalman filtering method, a neural network method, a fuzzy logic method and the like, wherein the extended Kalman filtering method is used for realizing the life estimation based on the approximate linearization of a nonlinear system near a state working point, and although the life estimation precision is improved, the stable work under all working conditions cannot be ensured; the neural network method and the fuzzy logic method belong to soft computing methods, and although the method can also obtain higher precision, the computation is more complex and takes longer time.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an online estimation method for the service life of a super capacitor, which effectively solves the technical problem that the service life of the super capacitor cannot be efficiently estimated online in the prior art.
In order to achieve the purpose, the invention is realized by the following technical scheme:
an online estimation method for the life of a super capacitor comprises the following steps:
s10, establishing a dynamic model of the super capacitor, wherein the dynamic model comprises the equivalent resistance and the equivalent capacitance of the super capacitor; the supercapacitor is represented by a Stern model, the Stern model is formed by combining a Helmholtz model and a Gouy-Chapman model, and the dynamic description of the supercapacitor is as follows:
wherein, VcIs the supercapacitor voltage, IcIs a supercapacitor current, RsIs the resistance value of the equivalent resistor, CTIs the capacity of the equivalent capacitance; t is t1For the start time of sampling, t2Is the sampling termination time, and t1＜t2；
S20, establishing a supercapacitor voltage parameter identification rule according to the dynamic model, and establishing a self-adaptive method for online estimation of equivalent resistance and equivalent capacitance of the supercapacitor according to the voltage parameter identification rule, so as to realize online estimation of the equivalent resistance and the equivalent capacitance and obtain an estimated resistance value and an estimated capacity value;
s30, analyzing the stability and convergence of the supercapacitor equivalent resistance and equivalent capacitance online estimation adaptive method based on the Lyapunov stability theory and the convergence principle;
and S40, carrying out online estimation on the service life of the super capacitor according to the estimated resistance value or the estimated capacity value.
In the method for estimating the service life of the super capacitor on line, after a dynamic model of the super capacitor is established, the equivalent resistance and the equivalent capacitance are identified and estimated on line based on the voltage parameter of the super capacitor, and then the service life of the super capacitor is estimated on line. Moreover, based on the Lyapunov stability theory and the convergence principle, the stability and the convergence of the online estimation adaptive method for the equivalent resistance and the equivalent capacitance of the super capacitor are analyzed, and the system is ensured to have better stability and convergence.
Drawings
A more complete understanding of the present invention, and the attendant advantages and features thereof, will be more readily understood by reference to the following detailed description when considered in conjunction with the accompanying drawings wherein:
FIG. 1 is a schematic flow chart of an online estimation method for lifetime of a super capacitor according to the present invention;
fig. 2 is a model of a supercapacitor in accordance with the present invention.
Detailed Description
In order to make the contents of the present invention more comprehensible, the present invention is further described below with reference to the accompanying drawings. The invention is of course not limited to this particular embodiment, and general alternatives known to those skilled in the art are also covered by the scope of the invention.
Fig. 1 is a schematic flow chart of the online estimation method for lifetime of a supercapacitor provided in the present invention, and as can be seen from the diagram, the online estimation method for lifetime includes:
s10, establishing a dynamic model of the super capacitor, wherein the dynamic model comprises the equivalent resistance and the equivalent capacitance of the super capacitor;
s20, establishing a supercapacitor voltage parameter identification rule according to the dynamic model, and establishing a self-adaptive method for online estimation of equivalent resistance and equivalent capacitance of the supercapacitor according to the voltage parameter identification rule, so as to realize online estimation of the equivalent resistance and the equivalent capacitance and obtain an estimated resistance value and an estimated capacitance value;
s30, analyzing the stability and convergence of the supercapacitor equivalent resistance and equivalent capacitance online estimation adaptive method based on the Lyapunov stability theory and the convergence principle;
s40, estimating the service life of the super capacitor on line according to the estimated resistance value or the estimated capacity value.
Fig. 2 shows a model of the supercapacitor, represented by a Stern model, and including a Helmholtz model and a Gouy-Chapman model, in which the dynamics of the supercapacitor are described by formula (1):
wherein, VcIs the supercapacitor voltage, IcIs a supercapacitor current, RsIs the resistance value of the equivalent resistor, CTIs the capacity of the equivalent capacitance; t is t1For the start time of sampling, t2Is the sampling termination time, and t1＜t2。
Rewriting the supercapacitor model in formula (1) to a regression model, as in formula (2):
where Ψ ∈ R2Is a regression quantity (vector of known function) containing the supercapacitor voltage VcAnd a supercapacitor current IcThe vector of (a); theta is epsilon to R2As a vector of parameters, including the equivalent capacitance CTVector of (a)1And equivalent resistance RsVector of (a)2As shown in formula (3) and formula (4):
θ2＝Rs(4)
the identification rule of the voltage parameter of the super capacitor is embodied as a voltage estimation error e of the super capacitor, and is expressed as formula (5):
e＝Vc-Vc^ (5)
wherein, VcIs the voltage V of the super capacitorcI.e., the voltage estimate.
The estimated supercapacitor voltage can be represented by equation (6):
wherein,is an estimated capacity value of the equivalent capacitance of the super capacitor,is an estimated resistance value of the equivalent resistance of the supercapacitor,the regulation current of the super capacitor after passing through the PID regulator is shown as a formula (7):
wherein, KpProportional gain, K, of a PID regulatoriIs the integral gain, K, of a PID regulatordE' is the derivative of the voltage estimation error e with respect to time t, which is the derivative gain of the PID regulator.
Substituting formula (7) for formula (6) to give formula (8):
subtracting the formula (8) from the formula (2), and obtaining an adaptive method for online estimation of the equivalent resistance and the equivalent capacitance based on a linear regression principle, wherein the formula is as follows (9):
wherein,Θ*is the error of the parameter vector estimation, and Θ*The method comprises the following steps of (1) determining a parameter vector theta by using a vector equation, wherein theta-theta is an estimated value of the parameter vector theta;
expressing formula (9) in a state space expression form to obtain formula (10):
X'＝AX+BU (10)
wherein,for the state vector, X' is the derivative of the state vector X with respect to time t, U ∈ R2＝ΨTΘ*For the input of the state space, A ∈ R2×2And B ∈ R2As a stabilization matrix, as in formula (11) and formula (12):
wherein,
based on the above, the method comprises the following steps in the process of online estimation of the equivalent resistance and the equivalent capacitance:
s21 setting initial value e of super capacitor voltage estimation error e0Predefining a set of values as an estimated value theta ^ of the parameter vector theta;
s22 identifying rule and equivalent resistance sum of super capacitor according to established voltage parameterAn adaptive method for equivalent capacitance on-line estimation is characterized in that a pole allocation method is used for placing a closed-loop pole at a required position or solving a proportional gain K of a Riccati equation to a PID regulatorpIntegral gain KiAnd a differential gain KdAnd (3) carrying out self-adaptive selection to obtain a group of estimated values of equivalent resistance and equivalent capacitance, wherein the Riccati equation is as shown in a formula (13):
ATP+PA＝-Q (13)
wherein, P is a positive definite symmetric matrix, and Q is a positive definite matrix;
s23, calculating the voltage estimation error e of the super capacitor and judging whether e is more than or equal to e0If yes, go to step S26; otherwise, jumping to step S24;
s24 updating the parameter vector estimation difference value delta theta ^ according to delta theta ^ which is a derivative of the parameter vector estimation value theta ^ with respect to time t;
s25, updating the estimated value theta ^ of the parameter vector according to theta ^ (k) ^ theta (k-1) + delta theta ^ and jumping to the step S22, wherein k represents the sampling time, and theta ^ (k) represents the estimated value of the parameter vector at the k sampling time;
s26 outputs a parameter vector theta to complete the on-line estimation of the equivalent resistance and the equivalent capacitance, and the estimated resistance value and the estimated capacitance value are obtained.
Stability and convergence are important aspects of online estimation of the service life of the supercapacitor, and if the estimation method lacks stability and convergence, no practical significance is achieved. Therefore, the stability and the convergence of the online estimation self-adaption method for the equivalent resistance and the equivalent capacitance of the super capacitor are analyzed based on the Lyapunov stability theory and the convergence principle.
The theorem used during the stability analysis was: for a non-linear system like equation (1) in the present invention, the stability and estimation error of the adaptive estimation method can be guaranteed to asymptotically converge to zero by the estimation rule in equation (6) and the adaptation law in equation (14):
Θ^'＝-ΓΨBTPX (14)
wherein r ═ γ1,γ2]，γ1And gamma2A positive constant gain, i.e., an adaptation rate; p is a positive definite symmetric matrix satisfying the formula (13).
Thus, in the course of the analysis, the lyapunov function V is first defined, as in formula (15):
V＝XTPX+Θ*TΓ-1Θ*(15)
then, carrying out derivation on the Lyapunov function V to obtain a derivative V', as shown in formula (16):
V'＝X'TPX+XTPX'+2Θ*TΓ-1Θ^' (16)
when the sampling frequency of the adaptive method is high enough, the variation of the parameter vector Θ between two samples is negligible, i.e. there is Θ*' Θ ^ and bringing formula (10) into formula (16) gives formula (17):
V'＝[AX+BU]TPX+XTP[AX+BU]+2Θ*TΓ-1Θ^' (17)
let U ═ ΨTΘ*Carry-over into formula (17) to give formula (18):
V'＝XT[ATP+PA]X+2Θ*TΨBTPX+2Θ*TΓ-1Θ^' (18)
further bringing formula (13) into formula (18) to obtain formula (19):
V'＝-XTQX+2Θ*T[ΨBTPX+Γ-1Θ^'](19)
obtaining equation (20) from the adaptation rate in equation (14):
V'＝-XTQX＜0 (20)
since Q is a positive definite matrix, X ═ 0 is a balance point for global asymptotic stabilization, forAny X ≠ 0, with V' < 0. The global asymptotic stability of the system can be realized according to the Lyapunov direct method; in addition, state vector X, ultracapacitor voltage estimation erroreAnd integrationError theta of parameter vector estimation*Are bounded and can converge to a finite value; and the regression amount psi is bounded, and the derivative X ' of the state vector is also bounded according to the formula (10), so the second derivative V ' of the derivative V ' is also bounded, and the equivalent resistance and the equivalent capacitance of the super capacitor meet the stability requirement by the online estimation self-adaptive method.
The rationale used in the convergence analysis process is: if a differentiable function V (t) has a limit at t → ∞ and its derivative V '(t) is uniformly continuous with respect to t, then there is V' (t) → 0 at t → ∞.
According to this lemma, in the present invention, since the lyapunov function V has a limit value at t → ∞, and the derivative V 'continues uniformly with respect to time t, the derivative V' therefore continues uniformly with respect to time tAnd V ═ XTQX < 0 to obtainTherefore, it isAndthereby havingThe adaptive method for online estimation of the equivalent resistance and the equivalent capacitance of the super capacitor meets the convergence requirement.
Lifetime estimation of a supercapacitor may be from an estimated resistance value of the supercapacitorOr estimated capacity values. When the estimated resistance value of the supercapacitor increases, the estimated capacity value decreases, and the life is shortened. Therefore, the supercapacitor estimated capacity value is extracted from equations (21) and (22)And estimating the resistance value
The lifetime indicator L (100%) of the supercapacitor can be represented by formula (23):
wherein R isEOLIs the resistance value, R, of the equivalent resistor at the end of the service life of the super capacitornewIs the resistance value of the equivalent resistor of a brand new super capacitor, and REOL＝2Rnew. In practical applications, the parameter θ is due to the influence of low resistance and low sensor accuracy and measurement noise2Is difficult to estimate. Therefore, the estimated capacity value according to the equivalent capacitance of the super capacitor can be obtainedThe lifetime of the supercapacitor is estimated. Here, the lifetime index may be expressed by formula (24):
wherein, CEOLThe capacity of the equivalent capacitance at the end of the life of the supercapacitor, CnewIs the capacity of the equivalent capacitance of a brand-new super capacitor, and CEOL＝0.8×Cnew。

Claims (7)

1. An online estimation method for the service life of a super capacitor is characterized by comprising the following steps:
s10, establishing a dynamic model of the super capacitor, wherein the dynamic model comprises the equivalent resistance and the equivalent capacitance of the super capacitor; the supercapacitor is represented by a Stern model, the Stern model is formed by combining a Helmholtz model and a Gouy-Chapman model, and the dynamic description of the supercapacitor is as follows:
wherein, VcIs the supercapacitor voltage, IcIs a supercapacitor current, RsIs the resistance value of the equivalent resistor, CTIs the capacity of the equivalent capacitance; t is t1For the start time of sampling, t2Is the sampling termination time, and t1＜t2；
S20, establishing a supercapacitor voltage parameter identification rule according to the dynamic model, and establishing a self-adaptive method for online estimation of equivalent resistance and equivalent capacitance of the supercapacitor according to the voltage parameter identification rule, so as to realize online estimation of the equivalent resistance and the equivalent capacitance and obtain an estimated resistance value and an estimated capacity value;
s30, analyzing the stability and convergence of the supercapacitor equivalent resistance and equivalent capacitance online estimation adaptive method based on the Lyapunov stability theory and the convergence principle;
and S40, carrying out online estimation on the service life of the super capacitor according to the estimated resistance value or the estimated capacity value.
2. The method for online estimation of lifetime of super capacitor as claimed in claim 1, wherein in step S20, the super capacitor voltage parameter identification rule is embodied as the voltage estimation error e of super capacitor:
e＝Vc-Vc^
wherein, VcIs the voltage V of the super capacitorcThe parameter identification value of (1);
the supercapacitor estimated voltage can be expressed as:
wherein,is an estimated capacity value of the equivalent capacitance of the super capacitor,an estimated resistance value of the equivalent resistance of the super capacitor;
wherein, KpProportional gain, K, of a PID regulatoriIs the integral gain, K, of a PID regulatordE' is the derivative of the voltage estimation error e with respect to time t, which is the derivative gain of the PID regulator.
3. The on-line estimation method of lifetime of a supercapacitor according to claim 2, wherein in step S20, the model of the supercapacitor is rewritten as a regression model:
where Ψ ∈ R2As a regressive quantity, comprising the supercapacitor voltage VcAnd a supercapacitor current IcThe vector of (a); theta is epsilon to R2As a vector of parameters, including the equivalent capacitance CTVector of (a)1And equivalent resistance RsVector of (a)2：
θ2＝Rs
Obtaining an adaptive method for online estimation of equivalent resistance and equivalent capacitance according to the established supercapacitor voltage parameter identification rule, the regression model of the supercapacitor and the linear regression principle:
wherein,Θ*is the error of the parameter vector estimation, and Θ*The method comprises the following steps of (1) determining a parameter vector theta by using a vector equation, wherein theta-theta is an estimated value of the parameter vector theta;
expressing the self-adaptive method by adopting a state space expression form to obtain:
X′＝AX+BU
wherein,for the state vector, X' is the derivative of the state vector X with respect to time t, U ∈ R2＝ΨTΘ*For the input of the state space, A ∈ R2×2And B ∈ R2Is a stable matrix, and:
wherein,
4. the online estimation method for lifetime of super capacitor as claimed in claim 3, wherein in step S20, it includes:
s21 setting initial value e of super capacitor voltage estimation error e0Predefining a set of values as an estimated value theta ^ of the parameter vector theta;
s22, according to the established voltage parameter identification rule and the self-adaptive method for online estimation of equivalent resistance and equivalent capacitance of the super capacitor, a pole allocation method is used for placing a closed-loop pole at a required position or solving a Riccati equation to proportional gain K of the PID regulatorpIntegral gain KiAnd a differential gain KdPerforming adaptive selection to obtain a set of equivalent resistance values and equivalent capacitance values, wherein the Riccati equation is as follows:
ATP+PA＝-Q
wherein, P is a positive definite symmetric matrix, and Q is a positive definite matrix;
s23, calculating the voltage estimation error e of the super capacitor and judging whether e is more than or equal to e0If yes, go to step S26; otherwise, jumping to step S24;
s24 updating the parameter vector estimation difference value delta theta ^ according to delta theta ^ which is a derivative of the parameter vector estimation value theta ^ with respect to time t;
s25, updating the estimated value theta ^ of the parameter vector according to theta ^ (k) ^ theta (k-1) + delta theta ^ and jumping to the step S22, wherein k represents the sampling time, and theta ^ (k) represents the estimated value of the parameter vector at the k sampling time;
s26 outputs a parameter vector theta to complete the on-line estimation of the equivalent resistance and the equivalent capacitance, and the estimated resistance value and the estimated capacitance value are obtained.
5. The on-line estimation method for the lifetime of the supercapacitor according to claim 4, wherein in step S30, the stability analysis process comprises:
s31 defines the lyapunov function V:
V＝XTPX+Θ*TΓ-1Θ*
wherein r ═ γ1,γ2]，γ1And gamma2A constant gain that is positive;
s32 differentiates the lyapunov function V to obtain a derivative V':
V'＝X'TPX+XTPX'+2Θ*TΓ-1Θ^'
s33 according to Riccati equation ATP + PA ═ Q and the equation Θ ═ Γ Ψ BTPX updates the derivative V':
V'＝-XTQX＜0
s34, analyzing the stability of the system according to the updated derivative V ', specifically, Q is a positive definite matrix, X ≠ 0 is a balance point of global asymptotic stability, and for any X ≠ 0, V' < 0; the Lyapunov function V is bounded by V' < 0 and can be converged to a limited extreme value according to the condition thatThe Lyapunov direct method can realize the global gradual stabilization of the system; in addition, the state vector X, the supercapacitor voltage estimation error e and the integralError theta of parameter vector estimation*Are bounded and can converge to a finite value; and the regression amount psi is bounded, and the derivative X ' of the state vector is also bounded according to the state space expression of the adaptive method, so the second derivative V ' of the derivative V ' is also bounded, and the equivalent resistance and the equivalent capacitance of the super capacitor are estimated on line by the adaptive method to meet the stability requirement.
6. The on-line estimation method for lifetime of super capacitor as claimed in claim 5, wherein in step S30, the convergence analysis process comprises:
since the Lyapunov function V has a limit value at t → ∞ and the derivative V' continues uniformly with respect to the time t, the derivative V is therefore continuousAnd V ═ XTQX < 0 to obtainTherefore, it isAndthereby havingThe adaptive method for online estimation of the equivalent resistance and the equivalent capacitance of the super capacitor meets the convergence requirement.
7. The on-line estimation method for lifetime of super capacitor as claimed in any one of claims 1-6, wherein in step S40, the lifetime index L (100%) can be expressed as:
wherein R isEOLIs the resistance value, R, of the equivalent resistor at the end of the service life of the super capacitornewIs the resistance value of the equivalent resistor of a brand new super capacitor, and REOL＝2Rnew；CEOLThe capacity of the equivalent capacitance at the end of the life of the supercapacitor, CnewIs the capacity of the equivalent capacitance of a brand-new super capacitor, and CEOL＝0.8×Cnew。
CN201910240523.6A 2019-03-28 2019-03-28 Supercapacitor service life On-line Estimation method Pending CN110008558A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910240523.6A CN110008558A (en) 2019-03-28 2019-03-28 Supercapacitor service life On-line Estimation method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910240523.6A CN110008558A (en) 2019-03-28 2019-03-28 Supercapacitor service life On-line Estimation method

Publications (1)

Publication Number Publication Date
CN110008558A true CN110008558A (en) 2019-07-12

Family

ID=67168477

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910240523.6A Pending CN110008558A (en) 2019-03-28 2019-03-28 Supercapacitor service life On-line Estimation method

Country Status (1)

Country Link
CN (1) CN110008558A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112444677A (en) * 2019-09-02 2021-03-05 北京金风科创风电设备有限公司 Super capacitor life monitoring method and device and storage medium

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103904734A (en) * 2014-03-10 2014-07-02 浙江工业大学 Switching control method for charging and discharging processes of super-capacitor

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103904734A (en) * 2014-03-10 2014-07-02 浙江工业大学 Switching control method for charging and discharging processes of super-capacitor

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
HICHAM CHAOUI: "Online System Identification for Lifetime Diagnostic of Supercapacitors With Guaranteed Stability", 《IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY》 *
HICHAM CHAOUI等: "Online Lifetime Estimation of Supercapacitors", 《IEEE TRANSACTIONS ON POWER ELECTRONICS》 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112444677A (en) * 2019-09-02 2021-03-05 北京金风科创风电设备有限公司 Super capacitor life monitoring method and device and storage medium

Similar Documents

Publication Publication Date Title
Afshar et al. State-of-charge estimation using an EKF-based adaptive observer
Yang et al. Online parameters identification and state of charge estimation for lithium-ion capacitor based on improved Cubature Kalman filter
Jafari et al. Deterministic models of Li-ion battery aging: It is a matter of scale
Tang et al. A fast estimation algorithm for lithium-ion battery state of health
Guo et al. A multi-scale parameter adaptive method for state of charge and parameter estimation of lithium-ion batteries using dual Kalman filters
Wei et al. Comparative study of methods for integrated model identification and state of charge estimation of lithium-ion battery
CN107422269B (en) Lithium battery online SOC measurement method
RU2328753C2 (en) Device and method for evaluating state of charge of battery with use of neutral network
JP4473823B2 (en) State quantity and parameter estimation device using multiple partial models for electrical energy storage
WO2014156869A1 (en) Battery life estimation method and battery life estimation device
CN102831100B (en) Battery charge state evaluation method and device
CN108445402A (en) A kind of lithium-ion-power cell state-of-charge method of estimation and system
US20180045788A1 (en) Battery state estimating apparatus
WO2017119393A1 (en) State estimation device and state estimation method
WO2016080111A1 (en) Remaining stored power amount estimation device, method for estimating remaining stored power amount of storage battery, and computer program
CN105929340B (en) A method of battery SOC is estimated based on ARIMA
Lin et al. State of charge estimation error due to parameter mismatch in a generalized explicit lithium ion battery model
Jiang et al. An aging-aware soc estimation method for lithium-ion batteries using xgboost algorithm
CN113752903A (en) Cost-effective and accurate determination of the state of degradation of rechargeable batteries
CN109143097A (en) It is a kind of meter and temperature and cycle-index lithium ion battery SOC estimation method
CN112649747A (en) Fractional order extended Kalman lithium battery SOC estimation method
CN114325441A (en) Method and device for device-specific determination of the aging state of a battery based on internal resistance
WO2018188321A1 (en) Method for enhancing battery state estimation robustness
JP2014174172A (en) Method of determining residual capacity of battery
Havangi Adaptive robust unscented Kalman filter with recursive least square for state of charge estimation of batteries

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

Application publication date: 20190712

RJ01 Rejection of invention patent application after publication