CN105487017A - Valve-regulation sealed lead acid battery state estimation and prediction method used for transformer substation UPS - Google Patents

Abstract

The invention provides a valve-regulation sealed lead acid battery state estimation and prediction method used for a transformer substation UPS, and relates to the field of equipment reliability. The invention aims to solve the problems that an existing storage battery management method leads to poor operation reliability of an electric power system and the maintainability of the storage battery health state is poor. The method comprises the steps of: selecting a single storage battery in a serial battery group of the transformer substation UPS; establishing an electrochemical module for the storage battery; according to a Ritz approximate method, giving a storage battery load current; obtaining a storage battery output voltage; inputting the same DST conditions to the electrochemical module and adjusting a parameter set by being combined with a genetic algorithm; enabling an electrochemical module output voltage to be equal to the storage battery output voltage Vcell which is measured in real, and obtaining the parameter set in the storage batter at the moment; and simulating a storage battery end voltage curve under any charging and discharging current Id conditions, and thereby finishing the storage battery state estimation and prediction. The method is used for predicting the internal state of the storage battery.

Description

A kind of transformer station UPS valve controlled sealed lead-acid accumulator state estimation and Forecasting Methodology

Technical field

The present invention relates to a kind of health status method of estimation of transformer station UPS lead-acid accumulator, can for the operation of battery, safeguard the foundation of science be more provided.Belong to equipment dependability field.

Background technology

Lead-acid accumulator is the important component part of transforming plant DC power-supply system; taking AC rectification plant failure dead electricity in emergency circumstances; be the direct supply for subsequent use of the important load such as relay protection and aut.eq., robotization and telemechanical apparatus, emergency lighting, isolating switch closing loop, therefore its reliability is most important for the safety and stability of electric system.

Valve controlled sealed lead-acid accumulator (abbreviation accumulator) due to good seal performance, the advantages such as electrolytic solution and distilled water, large current discharging capability be strong need not be supplemented, be widely adopted in power industry.In accumulator working service, China's power industry is many according to electric system code DLT724-2000 (electric system accumulator direct current supply plant running and maintenance engineering order at present, hereinafter referred to as " code "), adopt the mode of on-line monitoring voltage, electric current, temperature and the off-line regularly property checked electric discharge to carry out the work, its drawback is: on-line monitoring cannot reflect the true discharge capacity of accumulator; The property the checked inter-spike intervals time is oversize, makes accumulator lack health status information for a long time, and the property checked electric discharge is larger to the infringement of battery performance itself frequently.

The present situation that battery management means fall behind has had a strong impact on the reliability of Operation of Electric Systems; In addition, owing to lacking the health and fitness information of accumulator, field management maintainer generally lacks confidence to accumulator health status, although designed life is general more than 10 years, serviceable life, usually less than 5 years, causes a large amount of wastes.

For the problems referred to above, a kind of health status method of estimation based on storage battery chemical model is proposed.Comprise: battery electrochemical modelling by mechanism and numerical solution, inner health characteristics can't harm identification, the accumulator Behavior modeling of arbitrary load situation, the battery performance prediction etc. that develops based on health characteristics.This method has important construction value for raising transformer station direct current system reliability of operation, reduction maintenance cost.

Summary of the invention

The present invention is the poor reliability causing Operation of Electric Systems in order to solve the existing method to battery management, and the problem of maintainability difference to accumulator health status.A kind of transformer station UPS valve controlled sealed lead-acid accumulator state estimation and Forecasting Methodology are now provided.

A kind of transformer station UPS valve controlled sealed lead-acid accumulator state estimation and Forecasting Methodology, it comprises the following steps:

Step one, from transformer station UPS series battery, choose single battery and detect, this accumulator is set up to the electrochemical model of accumulator,

Step 2, according to inner hereby approximation method, discretize is carried out to the electrochemical model of the accumulator in step one, given storage battery negative charged current, obtains accumulator output voltage,

Parameter set initial value in the electrochemical model of the accumulator in step 3, selecting step one, inputs same DST operating mode to electrochemical model and in conjunction with genetic algorithm adjustment parameter set, makes electrochemical model output voltage value equals the actual accumulator output voltage V recorded _cellvalue, obtains the parameter set of internal storage battery now, thus obtains the reflection of actual internal storage battery state,

Electrochemical model in step 4, employing step one and step 2 and numerical algorithm, the actual battery parameter set adopting step 3 to obtain, emulates any charging and discharging currents I _daccumulator voltage curve under condition, so calculate accumulator discharge capacity Q, instant discharge power P and discharge time t _d, thus the estimation completed battery condition and prediction.

Beneficial effect of the present invention is: state estimation mechanism electrochemical model being applied to lead-acid accumulator; Based on model emulation technology, comprehensive estimation of the battery discharge ability relevant to transformer station typical DC part throttle characteristics can be obtained; Based on the battery life predicting of inside battery parameter evolutions rule, be different from the battery capacity prediction of simple data-driven.The method to experiment condition and equipment requirement low, not by the impact of external environment, simple to operate, simple.The method can also be applied to the state estimation of the electrochemical power source of different principle.Under the long-term floating charge state of the present invention, the ability of partial discharge task can be completed; Can predict its discharge capability based on the change of inside battery character.Can in battery life cycle its inner critical healthy feature of Non-Destructive Testing, and need to expand the function of battery management system according to operation maintenance.

Accompanying drawing explanation

Fig. 1 is the structural representation of lead-acid accumulator in a kind of transformer station UPS valve controlled sealed lead-acid accumulator state estimation described in embodiment one and Forecasting Methodology;

Fig. 2 is the electric current of DST operating mode and the curve map of time;

Fig. 3 is the voltage of DST operating mode and the curve map of time;

Fig. 4 is the process flow diagram of genetic algorithm;

Fig. 5 is model aging parametric regression, forecasting process schematic diagram, and Reference numeral 1 represents training data, and Reference numeral 2 represents regression curve, and Reference numeral 3 represents predicted data;

Fig. 6 is the battery accelerated deterioration charging and discharging curve estimation curve figure of the 1st week under DST operating mode, and Reference numeral 4 is actual measurement voltage, and Reference numeral 5 is analog voltage;

Fig. 7 is the battery accelerated deterioration charging and discharging curve estimation curve figure of the 4th week under DST operating mode,

Fig. 8 is the battery accelerated deterioration charging and discharging curve estimation curve figure of the 8th week under DST operating mode,

Fig. 9 is the battery accelerated deterioration charging and discharging curve estimation curve figure of the 12nd week under DST operating mode.

Embodiment

Embodiment one: illustrate present embodiment referring to figs. 1 through Fig. 9, a kind of transformer station UPS valve controlled sealed lead-acid accumulator state estimation described in present embodiment and Forecasting Methodology, it comprises the following steps:

Step one, from transformer station UPS series battery, choose single battery and detect, this accumulator is set up to the electrochemical model of accumulator,

Step 2, according to inner hereby approximation method, discretize is carried out to the electrochemical model of the accumulator in step one, given storage battery negative charged current, obtains accumulator output voltage,

Parameter set initial value in the electrochemical model of the accumulator in step 3, selecting step one, inputs same DST operating mode to electrochemical model and in conjunction with genetic algorithm adjustment parameter set, makes electrochemical model output voltage value equals the actual accumulator output voltage V recorded _cellvalue, obtains the parameter set of internal storage battery now, thus obtains the reflection of actual internal storage battery state,

Electrochemical model in step 4, employing step one and step 2 and numerical algorithm, the actual battery parameter set adopting step 3 to obtain, emulates any charging and discharging currents I _daccumulator voltage curve under condition, so calculate accumulator discharge capacity Q, instant discharge power P and discharge time t _d, thus the estimation completed battery condition and prediction.

In present embodiment, select DST (DynamicStressTest) operating mode as identification of Model Parameters operating mode, as shown in Figures 2 and 3.This operating mode contains the situation of the various process of reflection battery, and the voltage of acquisition, electric current, electric quantity data collection abundant information, the result robustness of parameter identification is stronger.The universal battery charge-discharge test instrument simulation of standard DST operating mode, the voltage simultaneously obtaining actual battery exports.

Parameter in the electrochemical model of accumulator is divided into two parts to be preset parameter and variable element, preset parameter, once determining, in the whole life cycle of battery, thinks substantially constant; Variable element, changes with the aging of battery, needs regular identification in the use procedure of battery.The target of parameter identification is selection one group of parameter set, when inputting same DST operating mode, making electrochemical model emulate voltage and exporting v is exported with actual cell voltage _cellbetween error minimum, objective function is as shown in formula (19).

$\left\{\begin{array}{c}OBJ=\underset{i=1}{\overset{N}{\Σ}}{\[V_{cell}-{\hat{V}}_{cell}\left(X\right)\]}^2\\ X=(\mathrm{\ϵ},D^{eff},a_c,a_d,{\mathrm{\κ}}^{eff},\mathrm{\γ},i_0,{\mathrm{\α}}_a,{\mathrm{\α}}_c,a_{dl})\∈S\⊆R^{10}\end{array}\right.---\left(19\right)$

Wherein, X is parameter set to be identified, adopts positive analyses, includes 10 parameters to be identified; S is search volume.

Embodiment two: present embodiment is described further a kind of transformer station UPS valve controlled sealed lead-acid accumulator state estimation described in embodiment one and Forecasting Methodology, in present embodiment, in step one, the electrochemical model setting up accumulator is:

Electrolyte species conservation:

$\mathrm{\ϵ}\frac{\∂c}{\∂t}=D^{eff}\frac{{\∂}^{2}c}{\∂x^2}+\frac{a_2}{2F}aj$ (formula 1),

In formula, c is concentration of electrolyte, D ^efffor effective diffusion cofficient, F is Faraday constant, and j is current density, and ε is electrolytic solution volume fraction, and a is the interfacial area of electrode, for positive pole a ₂=3-2t ₊, for negative pole a ₂=1-2t ₊, wherein t ₊for hydrionic transmission number;

Electrolytic solution charge conservation:

${\mathrm{\κ}}^{eff}\frac{{\∂}^2{\mathrm{\φ}}_e}{\∂x^2}+{\mathrm{\κ}}_d^{eff}\frac{{\∂}^{2}c}{\∂x^2}+aj+i_{dl}=0$ (formula 2),

In formula, κ ^efffor effective ion conductivity, φ _efor liquid phase electromotive force, i _dlfor double-layer capacitance volume averaging electric current;

Double-layer capacitance volume averaging electric current:

$i_{dl}=a_{dl}{C}_{dl}\frac{\∂({\mathrm{\φ}}_s-{\mathrm{\φ}}_e)}{\∂t}$ (formula 3),

In formula, a _dlfor double-layer capacitance area, C _dlfor double-layer capacitance capacity, φ _sfor solid phase electromotive force, t is the time;

Linearization B-V equation:

$j=\frac{R_a}{a}\mathrm{\η}$ (formula 4);

Wherein, i ₀for exchange current density, c _reffor reference H ⁺concentration, α _afor anode transport coefficient, α _cfor cathodic migration coefficient, R is universal gas constant, and γ is the index in B-V equation, i ₀for exchange current density, for liquid phase mean concentration, T is thermodynamic temperature;

η is overpotential,

U _pbO2=1.9228+0.0641ln (c)+0.0120ln ²(c)+0.0060ln ³(c)+0.0012ln4 (c), φ _spfor positive pole solid phase electromotive force, φ _smfor negative pole solid phase electromotive force;

Positive pole input current equation:

${\∫}_0^{L_1}A\[R_a({\mathrm{\φ}}_{sp}-{\mathrm{\φ}}_e-U_{{\mathrm{PbO}}_2})+a_{dl}{C}_{dl}\frac{\∂({\mathrm{\φ}}_{sp}-{\mathrm{\φ}}_e)}{\∂t}\]dx=I\left(t\right)$ (formula 5),

In formula, A is that cell cross-section amasss, L ₁for the coordinate system of positive pole and barrier film;

Negative pole input current equation:

${\∫}_{L_2}^{L}A\[R_a({\mathrm{\φ}}_{sm}-{\mathrm{\φ}}_e)+a_{dl}{C}_{dl}\frac{\∂({\mathrm{\φ}}_{sm}-{\mathrm{\φ}}_e)}{\∂t}\]dx=-I\left(t\right)$ (formula 6),

In formula, L ₂for the coordinate system of negative pole and barrier film, L is cell thickness;

Absorbing boundary equation:

wherein, x=0, x=L (formula 7),

${\left({\mathrm{\κ}}^{eff}\frac{\∂{\mathrm{\φ}}_e}{\∂}+{\mathrm{\κ}}_d^{eff}\frac{\∂c}{\∂x}\right)|}_{+}={\left({\mathrm{\κ}}^{eff}\frac{\∂{\mathrm{\φ}}_e}{\∂x}+{\mathrm{\κ}}_d^{eff}\frac{\∂c}{\∂x}\right)|}_{-},$ Wherein, x=L ₁, x=L ₂(formula 8);

wherein, x=L ₁, x=L ₂(formula 9),

In formula, κ ^efffor effective ion conductivity, for effectively spreading electrolyte coefficient, D ^efffor coefficient of diffusion.

Embodiment three: present embodiment is described further a kind of transformer station UPS valve controlled sealed lead-acid accumulator state estimation described in embodiment two and Forecasting Methodology, in present embodiment, in step 2, according to inner hereby approximation method, discretize is carried out to the electrochemical model of the accumulator in step one, given storage battery negative charged current, the process obtaining accumulator output voltage is:

The electrolytic solution charge conservation equation of formula in step one 2 is multiplied by trial function ψ _n(x), at whole battery area domain integral, and application boundary condition, obtain:

${\∫}_0^L\left({\mathrm{\ψ}}_n\right(x){\mathrm{\κ}}^{eff}\frac{{\∂}^2{\mathrm{\φ}}_e}{\∂x^2}+{\mathrm{\ψ}}_n(x){\mathrm{\κ}}_d^{eff}\frac{{\∂}^{2}c}{\∂x^2}+{\mathrm{\ψ}}_n(x)R_a\mathrm{\η}+{\mathrm{\ψ}}_n(x\left)a_{dl}{C}_{dl}\right({\stackrel{\·}{\mathrm{\φ}}}_s-{\stackrel{\·}{\mathrm{\φ}}}_e\left)\right)dx=0$ (formula 10),

By φ _erepresent with c Fourier series, obtain:

$c(x,t)=\underset{m=0}{\overset{N-1}{\Σ}}\left({\mathrm{\ψ}}_m\right(x\left)c_m\right(t\left)\right)$ (formula 11),

${\mathrm{\φ}}_e(x,t)=\underset{m=1}{\overset{N-1}{\Σ}}\left({\mathrm{\ψ}}_m\right(x\left){\mathrm{\φ}}_m\right(t\left)\right)$ (formula 12),

Wherein, m is the integer between 1 to N-1, N by the exponent number of employing Fourier series, c _mand φ _mfor the coefficient of Fourier series;

Bring formula formula 11, formula 12 into formula 10, obtain:

Write formula 13 as matrix form, for:

$M_e{\stackrel{\·}{\mathrm{\φ}}}_e+M_{es}{\stackrel{\·}{\mathrm{\φ}}}_s=K_{ec}c+K_e{\mathrm{\φ}}_e+K_{es}{\mathrm{\φ}}_s+B_{e}u$ (formula 14),

In formula, $u=\left[\begin{array}{c}I\\ {\stackrel{\‾}{U}}_{{\mathrm{PbO}}_2}\end{array}\right],{\mathrm{\φ}}_e=\left[\begin{array}{c}{\mathrm{\φ}}_{e,1}\left(t\right)\\ .\\ .\\ .\\ {\mathrm{\φ}}_{e,N-1}\left(t\right)\end{array}\right],{\mathrm{\φ}}_s=\left[\begin{array}{c}{\mathrm{\φ}}_{sp}\left(t\right)\\ {\mathrm{\φ}}_{sm}\left(t\right)\end{array}\right],c=\left[\begin{array}{c}{c}_0\left(t\right)\\ .\\ .\\ .\\ c_{N-1}\left(t\right)\end{array}\right],{\stackrel{\‾}{U}}_{{\mathrm{PbO}}_2}$ For the intercept of open circuit potential on voltage coordinate, M _e, M _es, K _ec, K _e, K _es, B _ebe matrix of coefficients;

For the both positive and negative polarity current relationship formula 5 in step one and formula 6, because solid phase electrical conductivity is comparatively large, thinks that both positive and negative polarity solid phase electromotive force is equal in the zone, do not change with L, thus no longer with trial function ψ _nx () does multiplication;

Formula 11 and formula 12 are substituted into formula 5, obtain:

${\∫}_0^{L_1}\[R_a({\mathrm{\φ}}_{sp}-\underset{m=1}{\overset{N-1}{\Σ}}\left({\mathrm{\ψ}}_m\left(x\right){\mathrm{\φ}}_m\left(t\right)\right)-{\stackrel{\‾}{U}}_{{\mathrm{PbO}}_2}-{\tilde{U}}_{{\mathrm{PbO}}_2}\underset{m=0}{\overset{N-1}{\Σ}}({\mathrm{\ψ}}_m\left(x\right)c_m\left(t\right)\left)\right)+a_{dl}{C}_{dl}({\stackrel{\·}{\mathrm{\φ}}}_{sp}-\underset{m=1}{\overset{N-1}{\Σ}}({\mathrm{\ψ}}_m\left(x\right){\stackrel{\·}{\mathrm{\φ}}}_m\left(t\right)\left)\right)\]dx=\frac{I\left(t\right)}{A}$

Transposition merges like terms and obtains:

$\begin{array}{c}-{\∫}_0^{L1}{a}_{dl}{C}_{dl}{\stackrel{\·}{\mathrm{\φ}}}_{sp}dx+{\∫}_0^{L1}{a}_{dl}{C}_{dl}\underset{m=1}{\overset{N-1}{\Σ}}\left({\mathrm{\ψ}}_m\left(x\right){\stackrel{\·}{\mathrm{\φ}}}_m\left(t\right)\right)dx\\ =-{\∫}_0^{L_1}{R}_a{\tilde{U}}_{{\mathrm{PbO}}_2}\underset{m=1}{\overset{N-1}{\Σ}}\left({\mathrm{\ψ}}_m\left(x\right)c_m\left(t\right)\right)dx-{\∫}_0^{L_1}{R}_a\underset{m=1}{\overset{N-1}{\Σ}}\left({\mathrm{\ψ}}_m\left(x\right){\mathrm{\φ}}_m\left(t\right)\right)dx+{\∫}_0^{L_1}{R}_a{\mathrm{\φ}}_{sp}dx-{\∫}_0^{L_1}{R}_a{\stackrel{\‾}{U}}_{{\mathrm{PbO}}_2}dx-\frac{I\left(t\right)}{A}\end{array}$

(formula 15),

Formula 11 and formula 12 are substituted into formula 6, obtain:

${\∫}_{L_2}^L\[R_a({\mathrm{\φ}}_{sn}-\underset{m=1}{\overset{N-1}{\Σ}}{\mathrm{\ψ}}_m\left(x\right){\mathrm{\φ}}_m\left(t\right))+a_{dl}{C}_{dl}({\stackrel{\·}{\mathrm{\φ}}}_{sn}-\underset{m=1}{\overset{N-1}{\Σ}}({\mathrm{\ψ}}_m\left(x\right){\stackrel{\·}{\mathrm{\φ}}}_m\left(t\right)\left)\right)\]dx=-\frac{I\left(t\right)}{A}$

Transposition merges like terms and obtains:

$-{\∫}_{L_2}^L{a}_{dl}{C}_{dl}{\stackrel{\·}{\mathrm{\φ}}}_{sp}dx+{\∫}_{L_2}^L{a}_{dl}{C}_{dl}\underset{m=1}{\overset{N-1}{\Σ}}\left({\mathrm{\ψ}}_m\left(x\right){\stackrel{\·}{\mathrm{\φ}}}_m\left(t\right)\right)dx=-{\∫}_{L_2}^L{R}_a\underset{m=1}{\overset{N-1}{\Σ}}\left({\mathrm{\ψ}}_m\left(x\right){\mathrm{\φ}}_m\left(t\right)\right)dx+{\∫}_{L_2}^L{R}_a{\mathrm{\φ}}_{sn}dx+\frac{I\left(t\right)}{A}$

(formula 16),

Write formula 15 and formula 16 as matrix form:

$M_s{\stackrel{\·}{\mathrm{\φ}}}_s+M_{se}{\stackrel{\·}{\mathrm{\φ}}}_e=K_{sc}c+K_{se}{\mathrm{\φ}}_e+K_s{\mathrm{\φ}}_s+B_{s}u$ (formula 17),

In formula, M _s, M _se, K _sc, K _se, K _s, B _sbe matrix of coefficients;

For the electrolyte species conservation equation of formula in step one 1, trial function ψ is multiplied by both sides simultaneously _n(x), at whole battery area domain integral, and application boundary condition, obtain:

${\∫}_0^L{\mathrm{\ψ}}_n\left(x\right)\mathrm{\ϵ}\frac{\∂c}{\∂t}dx=-{\∫}_0^L{D}^{eff}{\mathrm{\ψ}}_n^{\′}\left(x\right)\frac{\∂c}{\∂x}dx+{\∫}_0^L{\mathrm{\ψ}}_n\left(x\right)\frac{a_2}{2F}{R}_a\mathrm{\η}dx$ (formula 18),

Bring formula formula 11, formula 12 into formula formula 18, obtain:

Write formula 19 as matrix form:

$M_c\stackrel{\·}{c}=K_{c}c+K_{ce}{\mathrm{\φ}}_e+K_{cs}{\mathrm{\φ}}_s+B_{c}u$ (formula 20),

In formula, $u=\left[\begin{array}{c}I\\ {\stackrel{\‾}{U}}_{{\mathrm{PbO}}_2}\end{array}\right],{\mathrm{\φ}}_e=\left[\begin{array}{c}{\mathrm{\φ}}_{e,1}\left(t\right)\\ .\\ .\\ .\\ {\mathrm{\φ}}_{e,N-1}\left(t\right)\end{array}\right],{\mathrm{\φ}}_s=\left[\begin{array}{c}{\mathrm{\φ}}_{sp}\left(t\right)\\ {\mathrm{\φ}}_{sm}\left(t\right)\end{array}\right],c=\left[\begin{array}{c}{c}_0\left(t\right)\\ .\\ .\\ .\\ c_{N-1}\left(t\right)\end{array}\right],{\stackrel{\‾}{U}}_{{\mathrm{PbO}}_2}$ For the intercept of open circuit potential on voltage coordinate, M _c, K _c, K _ce, K _cs, B _cbe matrix of coefficients,

Formula 14, formula 17 and formula 20 are combined in state-space model, obtain:

$M_1\stackrel{\·}{x}=M_{2}x+M_{3}u$ (formula 21),

Wherein, $M_1=\left[\begin{array}{ccc}{M}_c& 0& 0\\ 0& M_e& M_{es}\\ 0& M_{se}& M_s\end{array}\right],M_2=\left[\begin{array}{ccc}{K}_c& K_{ce}& K_{cs}\\ K_{ec}& K_e& K_{es}\\ K_{sc}& K_{se}& K_s\end{array}\right],x=\left[\begin{array}{c}c\\ {\mathrm{\φ}}_e\\ {\mathrm{\φ}}_s\end{array}\right],M_3=\left[\begin{array}{c}{B}_c\\ B_e\\ B_s\end{array}\right],$

Accumulator output voltage is:

V (t)=φ _sp-φ _sn(formula 22).

Embodiment four: present embodiment is described further a kind of transformer station UPS valve controlled sealed lead-acid accumulator state estimation described in embodiment one and Forecasting Methodology, in present embodiment, in step 3, DST operating mode adopts the simulation of battery charging and discharging tester.

Embodiment five: present embodiment is described further a kind of transformer station UPS valve controlled sealed lead-acid accumulator state estimation described in embodiment one and Forecasting Methodology, in present embodiment, in step 3, parameter set initial value in the electrochemical model of the accumulator in selecting step one, input same DST operating mode to electrochemical model and in conjunction with genetic algorithm adjustment parameter set, make electrochemical model output voltage value equals the actual accumulator output voltage V recorded _cellvalue, the formula obtaining the parameter set of internal storage battery is now:

$\left\{\begin{array}{c}OBJ=\underset{i=1}{\overset{N}{\Σ}}{\[V_{cell}-{\hat{V}}_{cell}\left(X\right)\]}^2\\ X=(\mathrm{\ϵ},D^{eff},a_c,a_d,{\mathrm{\κ}}^{eff},\mathrm{\γ},i_0,{\mathrm{\α}}_a,{\mathrm{\α}}_c,a_{dl})\∈S\⊆R^{10}\end{array}\right.$ (formula 19),

In formula, X is parameter set to be identified, and adopt positive analyses, include 10 parameters to be identified, S is search volume.

Embodiment six: present embodiment is described further a kind of transformer station UPS valve controlled sealed lead-acid accumulator state estimation described in embodiment one and Forecasting Methodology, in present embodiment, adopt the electrochemical model in step one and step 2 and numerical algorithm, the actual battery parameter set adopting step 3 to obtain, emulates any charging and discharging currents I _daccumulator voltage curve under condition, so calculate accumulator discharge capacity Q, instant discharge power P and discharge time t _dprocess be:

According to formula:

Q=∫ I _ddt (formula 20),

Obtain discharge capacity Q;

According to formula:

P=I _dv _cell(formula 21),

Obtain instant discharge power P;

According to discharge current and sparking voltage cut off, obtain t discharge time according to discharge curve _d, t _dfor the duration lower than discharge cut-off voltage from electric discharge start time to cell voltage.

In present embodiment, the battery performance based on electrochemical model emulation is estimated and prediction, and first, the ageing parameter value applying current grasp carries out regretional analysis.The free curve fitting software CurveExpert1.4 of recommendation, or use Matlab Curve Fitting Toolbox.Obtain ageing parameter relation over time, and predict the parameter value in certain stage following accordingly, as shown in Figures 6 to 9.

Application current parameter value and predicted parameter value, can realize advanced battery performance estimation function by emulation mode:

(1) charging and discharging curve prediction

Battery charging and discharging voltage in arbitrary load situation can be simulated, thus the estimation realized charging and discharging curve and prediction.Charging and discharging curve prediction under DST operating mode as shown in Figures 6 to 9.

(2) discharge capacity prediction

Given discharge current and sparking voltage cut off, discharge capacity Q can be predicted according to discharge curve:

Q＝∫I _d·dt(20)

Under different load condition can be predicted, the degenerate case of the actual active volume of battery.

(3) discharge power

Given discharge current and sparking voltage cut off, instant discharge power P can be predicted according to discharge curve:

P＝I _d·V _cell(21)

Under different load condition can be predicted, the degenerate case of battery real power.

(4) discharge time

Given discharge current and sparking voltage cut off, can predict t discharge time according to discharge curve _d.T _dfor the duration lower than discharge cut-off voltage from electric discharge start time to cell voltage.

Based on above data, user can complete to certain loads from different angles investigation batteries such as energy, power, single time, life-spans and discharge, and complete the ability of regulation electric discharge task, estimating and forecasting is accurately made to it, thus for battery operation, safeguard the foundation of science be provided.

Claims (6)

1. transformer station UPS valve controlled sealed lead-acid accumulator state estimation and a Forecasting Methodology, it is characterized in that, it comprises the following steps:

Step one, from transformer station UPS series battery, choose single battery and detect, this accumulator is set up to the electrochemical model of accumulator,

Step 2, according to inner hereby approximation method, discretize is carried out to the electrochemical model of the accumulator in step one, given storage battery negative charged current, obtains accumulator output voltage,

Parameter set initial value in the electrochemical model of the accumulator in step 3, selecting step one, inputs same DST operating mode to electrochemical model and in conjunction with genetic algorithm adjustment parameter set, makes electrochemical model output voltage value equals the actual accumulator output voltage V recorded _cellvalue, obtains the parameter set of internal storage battery now, thus obtains the reflection of actual internal storage battery state,

Electrochemical model in step 4, employing step one and step 2 and numerical algorithm, the actual battery parameter set adopting step 3 to obtain, emulates any charging and discharging currents I _daccumulator voltage curve under condition, so calculate accumulator discharge capacity Q, instant discharge power P and discharge time t _d, thus the estimation completed battery condition and prediction.

2. a kind of transformer station UPS valve controlled sealed lead-acid accumulator state estimation according to claim 1 and Forecasting Methodology, is characterized in that, in step one, the electrochemical model setting up accumulator is:

Electrolyte species conservation:

$\mathrm{\ϵ}\frac{\∂c}{\∂t}=D^{eff}\frac{{\∂}^{2}c}{\∂x^2}+\frac{a_2}{2F}aj$ (formula 1),

In formula, c is concentration of electrolyte, D ^efffor effective diffusion cofficient, F is Faraday constant, and j is current density, and ε is electrolytic solution volume fraction, and a is the interfacial area of electrode, for positive pole a ₂=3-2t ₊, for negative pole a ₂=1-2t ₊, wherein t ₊for hydrionic transmission number;

Electrolytic solution charge conservation:

${\mathrm{\κ}}^{eff}\frac{{\∂}^2{\mathrm{\φ}}_e}{\∂x^2}+{\mathrm{\κ}}_d^{eff}\frac{{\∂}^{2}c}{\∂x^2}+aj+i_{dl}=0$ (formula 2),

In formula, κ ^efffor effective ion conductivity, for effectively spreading electrolyte, φ _efor liquid phase electromotive force, i _dlfor double-layer capacitance volume averaging electric current;

Double-layer capacitance volume averaging current i _dl:

$i_{dl}=a_{dl}{C}_{dl}\frac{\∂({\mathrm{\φ}}_s-{\mathrm{\φ}}_e)}{\∂t}$ (formula 3),

In formula, a _dlfor double-layer capacitance area, C _dlfor double-layer capacitance capacity, φ _sfor solid phase electromotive force, t is the time;

The linearization B-V equation of current density:

$j=\frac{R_a}{a}\mathrm{\η}$ (formula 4),

Wherein, i ₀for exchange current density, c _reffor reference H ⁺concentration, α _afor anode transport coefficient, α _cfor cathodic migration coefficient, R is universal gas constant, and γ is the index in B-V equation, for liquid phase mean concentration, T is thermodynamic temperature;

η is overpotential,

$U_{{\mathrm{PbO}}_2}=1.9228+0.0641ln\left(c\right)+0.0120{\ln }^2\left(c\right)+0.0060{\ln }^3\left(c\right)+0.0012ln4\left(c\right),$ For the purpose of simplification, right carry out linearization, φ _spfor positive pole solid phase electromotive force, φ _snfor negative pole solid phase electromotive force;

Positive pole input current equation:

${\∫}_0^{L_1}A\[R_a({\mathrm{\φ}}_{sp}-{\mathrm{\φ}}_e-U_{{\mathrm{PbO}}_2})+a_{dl}{C}_{dl}\frac{\∂({\mathrm{\φ}}_{sp}-{\mathrm{\φ}}_e)}{\∂t}\]dx=I\left(t\right)$ (formula 5),

In formula, A is that cell cross-section amasss, L ₁for the coordinate system of positive pole and barrier film;

Negative pole input current equation:

${\∫}_{L_2}^{L}A\[R_a({\mathrm{\φ}}_{sn}-{\mathrm{\φ}}_e)+a_{dl}{C}_{dl}\frac{\∂({\mathrm{\φ}}_{sn}-{\mathrm{\φ}}_e)}{\∂t}\]dx=-I\left(t\right)$ (formula 6),

In formula, L ₂for the coordinate system of negative pole and barrier film, L is cell thickness;

Absorbing boundary equation:

$\frac{\∂{\mathrm{\φ}}_e}{\∂x}=0,\frac{\∂c}{\∂x}=0,$ Wherein x=0, x=L (formula 7),

$({\mathrm{\κ}}^{eff}\frac{\∂{\mathrm{\φ}}_e}{\∂x}+{\mathrm{\κ}}_d^{eff}\frac{\∂c}{\∂x}){|}_{+}=({\mathrm{\κ}}^{eff}\frac{\∂{\mathrm{\φ}}_e}{\∂x}+{\mathrm{\κ}}_d^{eff}\frac{\∂c}{\∂x}){|}_{-},$ Wherein x=L ₁, x=L ₂(formula 8),

$D^{eff}\frac{\∂c}{\∂x}{|}_{+}=D^{eff}\frac{\∂c}{\∂x}{|}_{-},$ Wherein x=L ₁, x=L ₂(formula 9),

In formula, κ ^efffor effective ion conductivity, for effectively spreading electrolyte coefficient, D ^efffor coefficient of diffusion.

3. a kind of transformer station UPS valve controlled sealed lead-acid accumulator state estimation according to claim 2 and Forecasting Methodology, it is characterized in that, in step 2, according to inner hereby approximation method, discretize is carried out to the electrochemical model of the accumulator in step one, given storage battery negative charged current, the process obtaining accumulator output voltage is:

The electrolytic solution charge conservation equation of formula in step one 2 is multiplied by trial function ψ _n(x), at whole battery area domain integral, and application boundary condition, obtain:

${\∫}_0^L\left({\mathrm{\ψ}}_n\right(x){\mathrm{\κ}}^{eff}\frac{{\∂}^2{\mathrm{\φ}}_e}{\∂x^2}+{\mathrm{\ψ}}_n(x){\mathrm{\κ}}_d^{eff}\frac{{\∂}^{2}c}{\∂x^2}+{\mathrm{\ψ}}_n(x)R_a\mathrm{\η}+{\mathrm{\ψ}}_n(x\left)a_{dl}{C}_{dl}\right({\stackrel{\·}{\mathrm{\φ}}}_s-{\stackrel{\·}{\mathrm{\φ}}}_e\left)\right)dx=0$ (formula 10),

By φ _erepresent with c Fourier series, obtain:

$c(x,t)=\underset{m=0}{\overset{N-1}{\Σ}}\left({\mathrm{\ψ}}_m\right(x\left)c_m\right(t\left)\right)$ (formula 11),

${\mathrm{\φ}}_e(x,t)=\underset{m=1}{\overset{N-1}{\Σ}}\left({\mathrm{\ψ}}_m\right(x\left){\mathrm{\φ}}_m\right(t\left)\right)$ (formula 12),

Wherein, m is the integer between 1 to N-1, N by the exponent number of employing Fourier series, c _mand φ _mfor the coefficient of Fourier series;

Bring formula formula 11, formula 12 into formula 10, obtain:

${\∫}_0^{L_1}{\mathrm{\ψ}}_n\left(x\right)a_{dl}{C}_{dl}\underset{m=1}{\overset{N-1}{\mathrm{\Σ}}}\left({\mathrm{\ψ}}_m\right(x\left){\stackrel{\·}{\mathrm{\φ}}}_m\right(x\left)\right)dx+{\∫}_{L_2}^L{\mathrm{\ψ}}_n\left(x\right)a_{dl}{C}_{dl}\underset{m=1}{\overset{N-1}{\mathrm{\Σ}}}\left({\mathrm{\ψ}}_m\right(x\left){\stackrel{\·}{\mathrm{\φ}}}_m\right(x\left)\right)dx-{\∫}_0^{L_1}{\mathrm{\ψ}}_n\left(x\right)a_{dl}{C}_{dl}{\stackrel{\·}{\mathrm{\φ}}}_{sp}dx$

$-{\∫}_{L_2}^L{\mathrm{\ψ}}_n\left(x\right)a_{dl}{C}_{dl}{\stackrel{\·}{\mathrm{\φ}}}_{sn}dx$

$=-{\∫}_0^L{\mathrm{\κ}}_d^{eff}{\mathrm{\ψ}}_n^{\′}\left(x\right)\underset{m=1}{\overset{N-1}{\mathrm{\Σ}}}\left({\mathrm{\ψ}}_m^{\′}\right(x\left)c_m\right(x\left)\right)dx-{\∫}_0^{L_1}{\mathrm{\ψ}}_n\left(x\right)R_a{\tilde{U}}_{{\mathrm{PbO}}_2}\underset{m=1}{\overset{N-1}{\mathrm{\Σ}}}\left({\mathrm{\ψ}}_m\right(x\left)c_m\right(x\left)\right)dx$

$-{\∫}_0^L{\mathrm{\κ}}^{eff}{\mathrm{\ψ}}_n^{\′}\left(x\right)\underset{m=1}{\overset{N-1}{\mathrm{\Σ}}}\left({\mathrm{\ψ}}_m^{\′}\right(x\left){\mathrm{\φ}}_m\right(x\left)\right)dx-{\∫}_0^{L_1}{\mathrm{\ψ}}_n\left(x\right)R_a\underset{m=1}{\overset{N-1}{\mathrm{\Σ}}}\left({\mathrm{\ψ}}_m\right(x\left){\mathrm{\φ}}_m\right(x\left)\right)dx-{\∫}_{L_2}^L{\mathrm{\ψ}}_n\left(x\right)R_a\underset{m=1}{\overset{N-1}{\mathrm{\Σ}}}\left({\mathrm{\ψ}}_m\right(x\left){\mathrm{\φ}}_m\right(x\left)\right)dx+$

${\∫}_0^{L_1}{\mathrm{\ψ}}_n\left(x\right)R_a{\mathrm{\φ}}_{sp}dx+{\∫}_{L_2}^L{\mathrm{\ψ}}_n\left(x\right)R_a{\mathrm{\φ}}_{sn}dx-{\∫}_0^{L_1}{\mathrm{\ψ}}_n\left(x\right)R_a{\stackrel{\‾}{U}}_{{\mathrm{PbO}}_2}dx$ (formula 13),

Write formula 13 as matrix form, for:

$M_e{\stackrel{\·}{\mathrm{\φ}}}_e+M_{es}{\stackrel{\·}{\mathrm{\φ}}}_s=K_{ec}c+K_e{\mathrm{\φ}}_e+K_{es}{\mathrm{\φ}}_s+B_{e}u$ (formula 14),

In formula, $u=\left[\begin{array}{c}I\\ {\stackrel{\‾}{U}}_{{\mathrm{PbO}}_2}\end{array}\right],{\mathrm{\φ}}_e=\left[\begin{array}{c}{\mathrm{\φ}}_{e,1}\left(t\right)\\ \·\\ \·\\ \·\\ {\mathrm{\φ}}_{e,N-1}\left(t\right)\end{array}\right],{\mathrm{\φ}}_s=\left[\begin{array}{c}{\mathrm{\φ}}_{sp}\left(t\right)\\ {\mathrm{\φ}}_{sm}\left(t\right)\end{array}\right],c=\left[\begin{array}{c}{c}_0\left(t\right)\\ \·\\ \·\\ \·\\ c_{N-1}\left(t\right)\end{array}\right],{\stackrel{\‾}{U}}_{{\mathrm{PbO}}_2}$ For the intercept of open circuit potential on voltage coordinate, M _e, M _es, K _ec, K _e, K _es, B _ebe matrix of coefficients;

For the both positive and negative polarity current relationship formula 5 in step one and formula 6, because solid phase electrical conductivity is comparatively large, thinks that both positive and negative polarity solid phase electromotive force is equal in the zone, do not change with L, thus no longer with trial function ψ _nx () does multiplication;

Formula 11 and formula 12 are substituted into formula 5, obtain:

${\∫}_0^{L_1}\[R_a({\mathrm{\φ}}_{sp}-\underset{m=1}{\overset{N-1}{\mathrm{\Σ}}}({\mathrm{\ψ}}_m\left(x\right){\mathrm{\φ}}_m\left(x\right))-{\stackrel{\‾}{U}}_{{\mathrm{PbO}}_2}-{\tilde{U}}_{{\mathrm{PbO}}_2}\underset{m=1}{\overset{N-1}{\mathrm{\Σ}}}\left({\mathrm{\ψ}}_m\right(x\left)c_m\right(x\left)\right))+a_{dl}{C}_{dl}({\stackrel{\·}{\mathrm{\φ}}}_{sp}-\underset{m=1}{\overset{N-1}{\mathrm{\Σ}}}({\mathrm{\ψ}}_m\left(x\right){\stackrel{\·}{\mathrm{\φ}}}_m\left(x\right)))\]dx=\frac{I\left(t\right)}{A}$

Transposition merges like terms and obtains:

$-{\∫}_0^{L1}{a}_{dl}{C}_{dl}{\stackrel{\·}{\mathrm{\φ}}}_{sp}dx+{\∫}_0^{L1}{a}_{dl}{C}_{dl}\underset{m=1}{\overset{N-1}{\Σ}}\left({\mathrm{\ψ}}_m\right(x\left){\stackrel{\·}{\mathrm{\φ}}}_m\right(t\left)\right)dx$

$=-{\∫}_0^{L_1}{R}_a{\tilde{U}}_{{\mathrm{PbO}}_2}\underset{m=0}{\overset{N-1}{\Σ}}\left({\mathrm{\ψ}}_m\right(x\left)c_m\right(t\left)\right)dx-{\∫}_0^{L_1}{R}_a\underset{m=1}{\overset{N-1}{\Σ}}\left({\mathrm{\ψ}}_m\right(x\left){\mathrm{\φ}}_m\right(t\left)\right)dx+{\∫}_0^{L_1}{R}_a{\mathrm{\φ}}_{sp}dx-{\∫}_0^{L_1}{R}_a{\stackrel{\‾}{U}}_{{\mathrm{PbO}}_2}dx-\frac{I\left(t\right)}{A}$

(formula 15),

Formula 11 and formula 12 are substituted into formula 6, obtain:

${\∫}_{L_2}^L\[R_a({\mathrm{\φ}}_{sn}-\underset{m=1}{\overset{N-1}{\Σ}}{\mathrm{\ψ}}_m(x\left){\mathrm{\φ}}_m\right(t\left)\right)+a_{dl}{C}_{dl}({\stackrel{\·}{\mathrm{\φ}}}_{sn}-\underset{m=1}{\overset{N-1}{\Σ}}({\mathrm{\ψ}}_m\left(x\right){\stackrel{\·}{\mathrm{\φ}}}_m\left(t\right)))\]dx=-\frac{I\left(t\right)}{A}$

Transposition merges like terms and obtains:

$-{\∫}_{L_2}^L{a}_{dl}{C}_{dl}{\stackrel{\·}{\mathrm{\φ}}}_{sn}dx+{\∫}_{L_2}^L{a}_{dl}{C}_{dl}\underset{m=1}{\overset{N-1}{\Σ}}\left({\mathrm{\ψ}}_m\right(x){\stackrel{\·}{\mathrm{\φ}}}_m\left(t\right))dx=-{\∫}_{L_2}^L{R}_a\underset{m=1}{\overset{N-1}{\Σ}}\left({\mathrm{\ψ}}_m\right(x\left){\mathrm{\φ}}_m\right(t\left)\right)dx+{\∫}_{L_2}^L{R}_a{\mathrm{\φ}}_{sn}dx+\frac{I\left(t\right)}{A}$

(formula 16),

Write formula 15 and formula 16 as matrix form:

$M_s{\stackrel{\·}{\mathrm{\φ}}}_s+M_{se}{\stackrel{\·}{\mathrm{\φ}}}_e=K_{sc}c+K_{se}{\stackrel{\·}{\mathrm{\φ}}}_e+K_s{\stackrel{\·}{\mathrm{\φ}}}_s+B_{s}u$ (formula 17),

In formula, M _s, M _se, K _sc, K _se, K _s, B _sbe matrix of coefficients;

For the electrolyte species conservation equation of formula in step one 1, trial function ψ is multiplied by both sides simultaneously _n(x), at whole battery area domain integral, and application boundary condition, obtain:

${\∫}_0^L{\mathrm{\ψ}}_n\left(x\right)\mathrm{\ϵ}\frac{\∂c}{\∂t}dx=-{\∫}_0^L{D}^{eff}{\mathrm{\ψ}}_n^{\′}\left(x\right)\frac{\∂c}{\∂x}dx+{\∫}_0^L{\mathrm{\ψ}}_n\left(x\right)\frac{a_2}{2F}{R}_a\mathrm{\η}dx$ (formula 18),

Bring formula formula 11, formula 12 into formula formula 18, obtain:

${\∫}_0^L{\mathrm{\ψ}}_n\left(x\right)\mathrm{\ϵ}\underset{m=0}{\overset{N-1}{\Σ}}\left({\mathrm{\ψ}}_m\right(x\left){\stackrel{\·}{c}}_m\right(t\left)\right)dx$

$=-{\∫}_0^L{D}^{eff}{\mathrm{\ψ}}_n^{\′}\left(x\right)\underset{m=1}{\overset{N-1}{\mathrm{\Σ}}}\left({\mathrm{\ψ}}_m^{\′}\right(x\left)c_m\right(t\left)\right)dx-{\∫}_0^{L_1}{\mathrm{\ψ}}_n\left(x\right)\frac{a_2}{2F}{R}_a{\tilde{U}}_{{\mathrm{PbO}}_2}\underset{m=1}{\overset{N-1}{\mathrm{\Σ}}}\left({\mathrm{\ψ}}_m\right(x\left)c_m\right(t\left)\right)dx$

$-{\∫}_0^{L1}{\mathrm{\ψ}}_n\left(x\right)\frac{a_2}{2F}{R}_a\underset{m=1}{\overset{N-1}{\mathrm{\Σ}}}\left({\mathrm{\ψ}}_m\right(x\left){\mathrm{\φ}}_m\right(x\left)\right)dx-{\∫}_{L_2}^L{\mathrm{\ψ}}_n\left(x\right)\frac{a_2}{2F}{R}_a\underset{m=1}{\overset{N-1}{\mathrm{\Σ}}}\left({\mathrm{\ψ}}_m\right(x\left){\mathrm{\φ}}_m\right(x\left)\right)dx$

$+{\∫}_0^{L1}{\mathrm{\ψ}}_n\left(x\right)\frac{a_2}{2F}{R}_a{\mathrm{\φ}}_{sp}dx+{\∫}_{L_2}^L{\mathrm{\ψ}}_n\left(x\right)\frac{a_2}{2F}{R}_a{\mathrm{\φ}}_{sp}dx-{\∫}_0^{L1}{\mathrm{\ψ}}_n\left(x\right)\frac{a_2}{2F}{R}_a{\stackrel{\‾}{U}}_{{\mathrm{PbO}}_2}dx$ (formula 19),

Write formula 19 as matrix form:

$M_c\stackrel{\·}{c}=K_{c}c+K_{ce}{\mathrm{\φ}}_e+K_{cs}{\mathrm{\φ}}_s+B_{c}u$ (formula 20),

In formula, $u=\left[\begin{array}{c}I\\ {\stackrel{\‾}{U}}_{{\mathrm{PbO}}_2}\end{array}\right],{\mathrm{\φ}}_e=\left[\begin{array}{c}{\mathrm{\φ}}_{e,1}\left(t\right)\\ \·\\ \·\\ \·\\ {\mathrm{\φ}}_{e,N-1}\left(t\right)\end{array}\right],{\mathrm{\φ}}_s=\left[\begin{array}{c}{\mathrm{\φ}}_{sp}\left(t\right)\\ {\mathrm{\φ}}_{sn}\left(t\right)\end{array}\right],c=\left[\begin{array}{c}{c}_0\left(t\right)\\ \·\\ \·\\ \·\\ c_{N-1}\left(t\right)\end{array}\right],{\stackrel{\‾}{U}}_{{\mathrm{PbO}}_2}$

Intercept on coordinate, M _c, K _c, K _ce, K _cs, B _cbe matrix of coefficients,

Formula 14, formula 17 and formula 20 are combined in state-space model, obtain:

$M_1\stackrel{\·}{x}=M_{2}x+M_{3}u$ (formula 21),

Wherein, $M_1=\left[\begin{array}{ccc}{M}_c& 0& 0\\ 0& M_e& M_{es}\\ 0& M_{se}& M_s\end{array}\right],M_2=\left[\begin{array}{ccc}{K}_c& K_{ce}& K_{cs}\\ K_{ec}& K_e& K_{es}\\ K_{sc}& K_{se}& K_s\end{array}\right],x=\left[\begin{array}{c}c\\ {\mathrm{\φ}}_e\\ {\mathrm{\φ}}_s\end{array}\right],M_3=\left[\begin{array}{c}{B}_c\\ B_e\\ B_s\end{array}\right],$

Accumulator output voltage is:

V (t)=φ _sp-φ _sn(formula 22).

4. a kind of transformer station UPS valve controlled sealed lead-acid accumulator state estimation according to claim 1 and Forecasting Methodology, is characterized in that, in step 3, DST operating mode adopts the simulation of battery charging and discharging tester.

5. a kind of transformer station UPS valve controlled sealed lead-acid accumulator state estimation according to claim 1 and Forecasting Methodology, it is characterized in that, in step 3, parameter set initial value in the electrochemical model of the accumulator in selecting step one, input same DST operating mode to electrochemical model and in conjunction with genetic algorithm adjustment parameter set, make electrochemical model output voltage value equals the actual accumulator output voltage V recorded _cellvalue, the formula obtaining the parameter set of internal storage battery is now:

$\left\{\begin{array}{c}OBJ=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{\[V_{cell}-{\hat{V}}_{cell}\left(X\right)\]}^2\\ X=(\mathrm{\ϵ},D^{eff},a_c,a_d,{\mathrm{\κ}}^{eff},\mathrm{\γ},i_0,{\mathrm{\α}}_a,{\mathrm{\α}}_c,a_{dl})\∈S\⊆R^{10}\end{array}\right.$ (formula 23),

In formula, X is parameter set to be identified, and adopt positive analyses, include 10 parameters to be identified, S is search volume.

6. a kind of transformer station UPS valve controlled sealed lead-acid accumulator state estimation according to claim 1 and Forecasting Methodology, it is characterized in that, adopt the electrochemical model in step one and step 2 and numerical algorithm, the actual battery parameter set adopting step 3 to obtain, emulates any charging and discharging currents I _daccumulator voltage curve under condition, so calculate accumulator discharge capacity Q, instant discharge power P and discharge time t _dprocess be:

According to formula:

Q=∫ I _ddt (formula 24),

Obtain discharge capacity Q;

According to formula:

P=I _dv _cell(formula 25),

Obtain instant discharge power P;

According to discharge current and sparking voltage cut off, obtain t discharge time according to discharge curve _d, t _dfor the duration lower than discharge cut-off voltage from electric discharge start time to cell voltage.