CN105182245A - High-capacity battery system charge state estimation method based on unscented Kalman filter - Google Patents

Abstract

The invention discloses a high-capacity battery system charge state estimation method based on unscented Kalman filter. A high-capacity battery system is an M*N battery system, wherein M individual batteries are connected in series to form a battery string, and N battery strings are connected in parallel to form the high-capacity battery system. The method comprises the steps that a high-capacity battery system equivalent circuit model based on a battery charge state is established; the battery charge state meaning is combined, and a battery system space state equation is established; unscented Kalman filter is used to carry out charge state estimation on the battery system; the output voltage of the battery system and a voltage estimation value are detected online to update an unscented Kalman filter gain matrix; and recurrence is carried out to acquire a new battery charge state estimation value. According to the invention, a high-capacity battery system charge state estimation algorithm is more accurate and robust than an extended Kalman filter algorithm, and the method is applicable to the battery system and individual batteries.

Description

Based on the high capacity cell system state-of-charge method of estimation of Unscented kalman filtering

Technical field

The invention belongs to the design of MW level battery energy storage system and control technology field in intelligent grid, relate to a kind of high capacity cell system state-of-charge method of estimation based on Unscented kalman filtering.

Background technology

Along with the regenerative resource such as wind-powered electricity generation, photovoltaic generation and electrical network is intelligentized greatly develops, battery system, as the main carriers of battery energy storage system stored energy, is subject to concern and the application of countries in the world more and more.The continuous expansion of regenerative resource scale simultaneously and the quick growth of power load, also will impel battery system to high capacity (MW level) future development.But, because the complicacy (as level and smooth in level fluctuating power second, a contour dynamic case of high frequency) of applied environment and battery electric quantity such as directly can not to measure at the factor, accurate estimation battery system state-of-charge (StateofCharge, SOC) not only directly determine that can battery system safe, reliable, efficiently run, and battery system is distributed rationally, design and controls etc. most important.

Traditional SOC algorithm for estimating mainly contains: ampere-hour method, impedance method, open-circuit voltage method etc., neural network, fuzzy logic method, support vector machine and the advanced algorithm such as standard Kalman filter method, EKF method (ExtendedKalmanFilter, EKF) are in succession there is in recent years.For the battery system of nonlinear time-varying, often adopt EKF at present, and obtain good effect, but there is self calculation of complex, ignore the problems such as higher order term due to EKF, must certain error be produced, make the SOC estimated accuracy of battery still treat further research.

Summary of the invention

The problem that the present invention solves is to provide a kind of based on Unscented kalman filtering (UnscentedKalmanFilter, UKF) high capacity cell system state-of-charge method of estimation, solve that high capacity cell system performance parameter affects by SOC, EKF method calculation of complex, precision is not high and cause battery system SOC to be difficult to the problem being accurately, estimating, reaches the object of accurately estimation high capacity cell system SOC.

The present invention seeks to be achieved through the following technical solutions:

The invention provides a kind of high capacity cell system, this system is by M battery cell through being connected into battery strings, being formed by N number of battery series-parallel connection, and wherein M, N are the natural number being greater than 1.

A kind of high capacity cell system state-of-charge method of estimation based on Unscented kalman filtering is as follows: according to known lithium-ion battery monomer performance parameter, utilize the relation of series parallel circuits operating characteristic and screening method determination battery system performance parameter and battery cell performance parameter, determine battery system output end voltage equation in conjunction with Kirchhoff's law KVC again, set up battery system equivalent model (1); Using the terminal voltage of 2 RC parallel circuits in the state-of-charge SOC of battery system and equivalent model as state variable, using the electric current of battery system and output voltage as system input quantity and output quantity, in conjunction with battery system equivalent-circuit model, obtain cell system space state equation (2); Using the state variable of the terminal voltage of the battery system SOC in cell system space state equation (2), 2 RC parallel circuits as Unscented kalman filtering algorithm UKF; The input state space equation of cell system space state equation (2), output voltage state space equation respectively as UKF algorithm nonlinear state equation and measure equation; The battery terminal voltage estimated value obtained by actual value and the UKF algorithm of voltage sensor measurement battery system terminal voltage (4) upgrades gain matrix (5), last by UKF algorithm through loop iteration, thus obtain the estimated value of battery system SOC in real time.

Described high capacity cell system equivalent-circuit model (1) is second order equivalent-circuit model, and model main circuit is by 2 RC parallel circuits, controlled voltage source U _b0and internal resistance of cell R (SOC) _bdeng composition.Set up the relation that the key of battery system equivalent-circuit model is accurately how to determine according to battery working characteristic battery system performance parameter and battery cell performance parameter.In the present invention, high capacity cell system performance parameter and battery cell performance parameter relational expression are:

$\left\{\begin{array}{c}{U}_{b0}\left(t\right)=m{U}_{i0}\left(t\right)\\ R_b\left(t\right)=\frac{m}{n}{R}_i\left(t\right)\\ R_{bs}\left(t\right)=\frac{m}{n}{R}_{is}\left(t\right)\\ C_{bs}\left(t\right)=\frac{n}{m}{C}_{is}\left(t\right)\\ R_{b1}\left(t\right)=\frac{m}{n}{R}_{i1}\left(t\right)\\ C_{b1}\left(t\right)=\frac{n}{m}{C}_{i1}\left(t\right)\end{array}\right.$

In formula, R _bs, R _bl, C _bs, C _blrepresent resistance and the electric capacity of 2 RC parallel circuits in battery system model respectively; Subscript i represents i-th battery cell; U _i0, R _irepresent the open-circuit voltage of battery cell, internal resistance respectively; R _is, R _il, C _is, C _ilrepresent resistance and the electric capacity of 2 RC parallel circuits in battery cell model respectively; U _i0, R _i, R _is, R _il, C _is, C _ilall relevant with SOC, SOC is defined as: wherein, SOC ₀for battery cell SOC initial value, be generally the constant of 0 ~ 1; Q _ut () is the unavailable capacity of battery cell, Q ₀for battery cell rated capacity.U _i0(SOC), R _is, R _iland C _is, C _il, R _icalculating respectively as follows: $U_{i0}\left(SOC\right)=a_0{e}^{a_{1}SOC\left(t\right)}+a_2+a_{3}SOC\left(t\right)-a_4{\mathrm{SOC}}^2\left(t\right)+a_5{\mathrm{SOC}}^3\left(t\right),R_{is}\left(t\right)=c_0{e}^{-c_{1}SOC\left(t\right)}+c_2,$ $C_{is}\left(t\right)=d_0{e}^{-d_{1}SOC\left(t\right)}+d_2,R_{i1}\left(t\right)=e_0{e}^{-e_{1}SOC\left(t\right)}+e_2,C_{i1}\left(t\right)=f_0{e}^{-f_{1}SOC\left(t\right)}+f_2,$ $R_i\left(t\right)=b_0{e}^{-b_{1}SOC\left(t\right)}+b_2+b_{3}SOC\left(t\right)-b_4{\mathrm{SOC}}^2\left(t\right)+b_5{\mathrm{SOC}}^3\left(t\right),$ Wherein, a ₀~ a ₅, c ₀~ c ₂, d ₀~ d ₂, e ₀~ e ₂, f ₀~ f ₂, b ₀~ b ₅be model coefficient, can be obtained through matching by battery measurement data.

The foundation of described high capacity cell system space state equation (2) is as follows: a, with the state-of-charge SOC of battery system _band in equivalent model the terminal voltage of 2 RC parallel circuits as state variable, with the electric current I of battery system _bfor system input quantity, setting up cell system space state equation according to equivalent-circuit model is

$\left[\begin{array}{c}{\mathrm{SOC}}_{b,k+1}\\ U_{bs,k+1}\\ U_{bl,k+1}\end{array}\right]=\left[\begin{array}{ccc}1& 0& 0\\ 0& \exp \left(-\mathrm{\Δ}t/{\mathrm{\τ}}_1\right)& 0\\ 0& 0& \exp \left(-\mathrm{\Δ}t/{\mathrm{\τ}}_2\right)\end{array}\right]\×\left[\begin{array}{c}{\mathrm{SOC}}_{b,k}\\ U_{bs,k}\\ U_{bl,k}\end{array}\right]+\left[\begin{array}{c}\frac{-\mathrm{\η}\mathrm{\Δ}t}{Q_N}\\ R_{bs}\[1-\exp \left(-\mathrm{\Δ}t/{\mathrm{\τ}}_1\right)\]\\ R_{bl}\[1-\exp \left(-\mathrm{\Δ}t/{\mathrm{\τ}}_2\right)\]\end{array}\right]I_{b,k}+w_k$

In formula, U _bs, U _blbe 2 RC parallel circuit terminal voltages, R _bs, R _blbe the resistance of 2 RC parallel circuits, Q _nfor battery system specified electric quantity, τ ₁, τ ₂for time constant, w _kfor systematic perspective process noise, Δ t is the sampling period, k be greater than 1 natural number; B, according to Kirchhoff's second law, in conjunction with battery system equivalent-circuit model, can obtain battery system output voltage equation is: U _b(t)=U _b0(t)-R _b(t) I _b(t)-U _bl(t)-U _bs(t), in formula, U _bfor battery system terminal voltage, R _bfor battery system internal resistance.

The key step of described Unscented kalman filtering algorithm UKF is: 1) init state variable x average E () and square error P ₀: ${\hat{x}}_0=E\left(x_0\right),{\hat{P}}_0=E\[(x_0-{\hat{x}}_0){(x_0-{\hat{x}}_0)}^T\];$ 2) sampled point x is obtained _iand respective weights ω: $\left\{\begin{array}{c}{x}_{0,k-1}={\hat{x}}_{k-1}\\ x_{i,k-1}={\hat{x}}_{k-1}+{\left(\sqrt{(n+\mathrm{\γ})P_{i,k-1}}\right)}_i,i=1,2,...,n\\ x_{i+n,k-1}={\hat{x}}_{k-1}-{\left(\sqrt{(n+\mathrm{\γ})P_{i,k-1}}\right)}_i,i=n+1,n+2,...,2n\end{array}\right.,$ $\left\{\begin{array}{c}{\mathrm{\ω}}_0^m=\mathrm{\λ}/(n+\mathrm{\λ})\\ {\mathrm{\ω}}_0^c=\mathrm{\λ}/(n+\mathrm{\λ})+(1+\mathrm{\β}-{\mathrm{\α}}^2)\\ {\mathrm{\ω}}_i^m={\mathrm{\ω}}_i^c=1/2(n+\mathrm{\λ}),i\≠0\end{array}\right.$ In formula, λ=α ²(n+h)-n; 3) time of state estimation and square error upgrades: the state estimation time is updated to $\left\{\begin{array}{c}{x}_{i,k/k-1}=f_{k-1}\left(x_{i,k-1}\right)+q_{k-1},i=0,1,...,2n\\ {\hat{x}}_{k/k-1}=\underset{i=0}{\overset{2n}{\mathrm{\Σ}}}{\mathrm{\ω}}_i^m{f}_{k-1}\left(x_{i,k-1}\right)+q_{k-1}\end{array}\right.,$ The square error time is updated to $P_{k/k-1}=\underset{i=0}{\overset{2n}{\Σ}}{\mathrm{\ω}}_i^c(x_{i,k/k-1}-{\hat{x}}_{k/k-1}){(x_{i,k/k-1}-{\hat{x}}_{k/k-1})}^T+Q_{k-1},$ System output time is updated to $\left\{\begin{array}{c}{\mathrm{\χ}}_{i,k/k-1}=g_{k-1}\left(x_{i,k-1}\right)+r_{k-1}\\ {\hat{y}}_{k/k-1}=\underset{i=0}{\overset{2n}{\mathrm{\Σ}}}{\mathrm{\ω}}_i^m{g}_{k-1}\left(x_{i,k-1}\right)+r_{k-1}\end{array}\right.$ In formula, g _k-1() is for measuring equation; 4) calculated gains matrix (5): in formula, $\left\{\begin{array}{c}{P}_{y,k}=\underset{i=0}{\overset{2n}{\Σ}}{\mathrm{\ω}}_i^c({\mathrm{\χ}}_{i,k/k-1}-{\hat{y}}_{k/k-1}){({\mathrm{\χ}}_{i,k/k-1}-{\hat{y}}_{k/k-1})}^T\\ P_{xy,k}=\underset{i=0}{\overset{2n}{\Σ}}{\mathrm{\ω}}_i^c(x_{i,k/k-1}-{\hat{x}}_{k/k-1}){({\mathrm{\χ}}_{i,k/k-1}-{\hat{y}}_{k/k-1})}^T\end{array}\right.;$ 5) measurement updaue of state estimation and square error: state estimation measurement updaue is square error measurement updaue is $P_k=P_{k/k-1}-L_k{P}_{y,k}{L}_k^T.$

Carry out compared with battery system SOC estimates with employing expanded Kalman filtration algorithm EKF, the present invention has following useful technique effect: one is whole discharge process, it is higher that UKF algorithm of the present invention carries out UKF estimated accuracy when battery system SOC estimates than EKF algorithm, especially discharge the initial stage and latter stage effect more obvious; Two be adopted UKF algorithm than EKF algorithm can more rapid convergence be in experimental data, robustness is better.

Accompanying drawing explanation

Fig. 1 is high capacity cell system architecture schematic diagram;

Fig. 2 is 12 × 2 high capacity cell system architecture schematic diagram;

Fig. 3 is the battery system equivalent-circuit model figure containing 2 RC parallel circuits;

Fig. 4 is Unscented kalman filtering algorithm flow chart;

Fig. 5-1 ~ Fig. 5-4 is SOC ₀battery constant-current discharge characteristic time different, wherein Fig. 5-1 is SOC ₀sOC situation of change when=1, Fig. 5-2 is SOC ₀battery system terminal voltage situation of change when=1, Fig. 5-3 is SOC ₀sOC situation of change when=0.8, Fig. 5-4 is SOC ₀battery system terminal voltage situation of change when=0.8;

Fig. 6-1 ~ Fig. 6-4 is SOC ₀cell pulse discharge characteristic time different, wherein Fig. 6-1 is SOC ₀sOC situation of change when=1, Fig. 6-2 is SOC ₀battery system terminal voltage situation of change when=1, Fig. 6-3 is SOC ₀sOC situation of change when=0.8, Fig. 6-4 is SOC ₀battery system terminal voltage situation of change when=0.8.

Embodiment

Below in conjunction with concrete example, the present invention is described in further detail, and described is explanation of the invention instead of restriction.

1, high capacity cell system and equivalent-circuit model thereof

1.1 high capacity cell systems

High capacity cell system is that its structural drawing as shown in Figure 1 by M battery cell through being connected into battery strings, being formed by N number of battery series-parallel connection.For ease of analyzing, suppose high capacity cell system in this example by 12 battery cells through being connected into battery strings, being formed by 2 battery series-parallel connections again, i.e. 12 × 2 high capacity cell systems, as shown in Figure 2.In battery system, the rated voltage of each battery cell is 3.2V, and rated capacity is 25Ah, and discharge cut-off voltage is 2.5V.

1.212 × 2 battery system equivalent-circuit models

High capacity cell system equivalent-circuit model (1) is second order equivalent-circuit model, and model main circuit is by 2 RC parallel circuits, controlled voltage source U _b0and internal resistance of cell R (SOC) _bdeng composition, as shown in Figure 3.Battery system performance parameter, by obtaining with the relation of battery cell performance parameter, is specifically calculated as follows: U _b0(t)=12*U ₀(t), R _b(t)=6*R (t), R _bs(t)=6*R _s(t), C _bs(t)=C _s(t)/6, R _bl(t)=6*R _l(t), C _bl(t)=C _l(t)/6, in each above formula, battery cell performance parameter U ₀(t), R _s(t), R _l(t) and C _s(t), C _lt the calculating of () is as follows respectively: $U_0\left(t\right)=a_0{e}^{-a_{1}SOC\left(t\right)}+a_2+a_{3}SOC\left(t\right)-a_4{\mathrm{SOC}}^2\left(t\right)+a_5{\mathrm{SOC}}^3\left(t\right),R_s\left(t\right)=c_0{e}^{-c_{1}SOC\left(t\right)}+c_2,$ $C_s\left(t\right)=d_0{e}^{-d_{1}SOC\left(t\right)}+d_2,R_1\left(t\right)=e_0{e}^{-e_{1}SOC\left(t\right)}+e_2,C_1\left(t\right)=f_0{e}^{-f_{1}SOC\left(t\right)}+f_2,$ $R\left(t\right)=b_0{e}^{-b_{1}SOC\left(t\right)}+b_2+b_{3}SOC\left(t\right)-b_4{\mathrm{SOC}}^2\left(t\right)+b_5{\mathrm{SOC}}^3\left(t\right),$ Wherein, a ₀~ a ₅value is respectively-0.602 ,-10.365,3.395,0.267 ,-0.202,0.105, c ₀~ c ₂value is respectively 0.1058 ,-59.96,0.0036, d ₀~ d ₂value is respectively-196 ,-142,295, e ₀~ e ₂value is respectively 0.00697 ,-60.8,0.0022, f ₀~ f ₂value is respectively-2996 ,-175,5122, b ₀~ b ₅value is respectively-0.0558 ,-29.96,0.0055,0.0062,0.0121,0.0066.

2, high capacity cell system space state equation

A, with the state-of-charge SOC of battery system _band the terminal voltage U of 2 RC parallel circuits in equivalent model _bs, U _blas state variable, with the electric current I of battery system _bfor system input quantity, setting up battery system input state space equation according to equivalent-circuit model (1) is

$\left[\begin{array}{c}{\mathrm{SOC}}_{b,k+1}\\ U_{bs,k+1}\\ U_{bl,k+1}\end{array}\right]=\left[\begin{array}{ccc}1& 0& 0\\ 0& \exp \left(-\mathrm{\Δ}t/{\mathrm{\τ}}_1\right)& 0\\ 0& 0& \exp \left(-\mathrm{\Δ}t/{\mathrm{\τ}}_2\right)\end{array}\right]\×\left[\begin{array}{c}{\mathrm{SOC}}_{b,k}\\ U_{bs,k}\\ U_{bl,k}\end{array}\right]+\left[\begin{array}{c}\frac{-\mathrm{\η}\mathrm{\Δ}t}{Q_N}\\ R_{bs}\[1-\exp \left(-\mathrm{\Δ}t/{\mathrm{\τ}}_1\right)\]\\ R_{bl}\[1-\exp \left(-\mathrm{\Δ}t/{\mathrm{\τ}}_2\right)\]\end{array}\right]I_{b,k}+w_k$

In formula, U _bs, U _blbe 2 RC parallel circuit terminal voltages, R _bs, R _blbe the resistance of 2 RC parallel circuits, Q _nfor battery system specified electric quantity, τ ₁, τ ₂for time constant, w _kfor systematic perspective process noise, Δ t is the sampling period, k be greater than 1 natural number.

B, according to Kirchhoff's second law, in conjunction with battery system equivalent-circuit model, can obtain battery system output voltage equation is: $U_{b,k+1}\left(t\right)=U_{b0,k+1}\left(t\right)-R_{b,k+1}\left(t\right)I_{b,k+1}\left(t\right)-\[\begin{array}{ccc}0& 1& 1\end{array}\]\×\left[\begin{array}{c}SO{C}_{b,k+1}\\ U_{bs,k+1}\\ U_{bl,k+1}\end{array}\right],$ In formula, U _bfor battery system terminal voltage, R _bfor battery system internal resistance, k be greater than 1 natural number.

3, the high capacity cell system state-of-charge based on Kalman filtering method is estimated

Using the state variable x of the terminal voltage of the battery system SOC in cell system space state equation, 2 RC parallel circuits as Unscented kalman filtering algorithm UKF; The input state space equation of cell system space state equation, output voltage state space equation are respectively as the nonlinear state Equation f of UKF algorithm _k-1() and measurement equation g _k-1(); The actual value y of battery system terminal voltage (4) is measured by voltage sensor _kthe battery terminal voltage estimated value obtained with UKF algorithm upgrade gain matrix (5), finally carry out loop iteration by UKF algorithm, as shown in Figure 4, in an iterative process, state variable x initial value is [100], and α value is 1, β value be 2, h value is 0; The last estimated value SOC obtaining battery system SOC in real time _k.

4, system emulation result and Contrast on effect

L-G simulation test mainly comprises constant current and pulse two kinds of operating modes, and one is constant current operating mode, and namely battery is outwards powered with current constant mode (25A); Two is pulse operations, namely outwards supplies discharge of electricity with impulse current system, is specially: first with 25A constant current operation 600s, after leaving standstill 600s, then with 25A constant current operation 600s, so circulates.For verifying the high robust of UKF, in constant current and pulse two kinds of operating modes respectively with SOC ₀be 1,0.8 two kind of situation be analyzed.Fig. 5-1 ~ Fig. 5-4 is SOC ₀battery constant-current discharge characteristic time different, wherein Fig. 5-1 is SOC ₀sOC situation of change when=1, Fig. 5-2 is SOC ₀battery system terminal voltage situation of change when=1, Fig. 5-3 is SOC ₀sOC situation of change when=0.8, Fig. 5-4 is SOC ₀battery system terminal voltage situation of change when=0.8; From Fig. 5-1 and Fig. 5-2, in whole discharge process, EKF and UKF can predict the change of battery system SOC and terminal voltage thereof well, but UKF precision is higher, and especially discharge latter stage (3000s).From Fig. 5-3 and Fig. 5-4, no matter be battery system SOC or terminal voltage, two kinds of algorithms all preferably to experimental data convergence, can demonstrate two kinds of algorithms and all have good robustness, but not only when the initial stage of electric discharge, because UKF is less than EKF calculated amount, its speed of convergence is faster, and discharging latter stage, because EKF itself ignores higher order term, UKF closer to experimental data, thus proves that UKF predicts the outcome more accurately than EKF, robustness is better in constant-current discharge situation than EKF simulation result.

Fig. 6-1 ~ Fig. 6-4 is SOC ₀cell pulse discharge characteristic time different, wherein Fig. 6-1 is SOC ₀sOC situation of change when=1, Fig. 6-2 is SOC ₀battery system terminal voltage situation of change when=1, Fig. 6-3 is SOC ₀sOC situation of change when=0.8, Fig. 6-4 is SOC ₀battery system terminal voltage situation of change when=0.8.Higher than EKF precision of prediction from Fig. 6-1 and Fig. 6-2, UKF, especially discharging latter stage.From Fig. 6-3 and Fig. 6-4, in whole discharge process, UKF more mates with experimental data than EKF simulation result, especially discharges the initial stage (before 600s) and two stages of latter stage (after 6000s); Meanwhile, UKF can converge on experimental data quickly, demonstrates UKF under pulse operation further and can more accurately estimate battery system SOC value and robustness is better.

Claims (5)

1. the present invention discloses a kind of high capacity cell system state-of-charge method of estimation based on Unscented kalman filtering, it is characterized in that described high capacity cell system is that wherein M, N are the natural number being greater than 1 by M battery cell through being connected into battery strings, being formed by N number of battery series-parallel connection.

Said method comprising the steps of:

According to known lithium-ion battery monomer performance parameter, utilize the relation of series parallel circuits operating characteristic and screening method determination battery system performance parameter and battery cell performance parameter, determine battery system output end voltage equation in conjunction with Kirchhoff's law KVC again, set up battery system equivalent model (1).

Using the terminal voltage of 2 RC parallel circuits in the state-of-charge SOC of battery system and equivalent model as state variable, using the electric current of battery system and output voltage as system input quantity and output quantity, in conjunction with battery system equivalent-circuit model, obtain cell system space state equation (2).

Using the state variable of the terminal voltage of the battery system SOC in cell system space state equation (2), 2 RC parallel circuits as Unscented kalman filtering algorithm UKF; The input state space equation of cell system space state equation (2), output voltage state space equation respectively as UKF algorithm nonlinear state equation and measure equation; The battery terminal voltage estimated value obtained by actual value and the UKF algorithm of voltage sensor measurement battery system terminal voltage (4) upgrades gain matrix (5), last by UKF algorithm through loop iteration, thus obtain the estimated value of battery system SOC in real time.

2. a kind of high capacity cell system state-of-charge method of estimation based on Unscented kalman filtering according to claim 1, is characterized in that set up high capacity cell system model (1) is the second order equivalent-circuit model containing 2 RC parallel circuits.

3. a kind of high capacity cell system state-of-charge method of estimation based on Unscented kalman filtering according to claim 1, is characterized in that described cell system space state equation (2) is as follows: system state space equation and system output equation are respectively

$\left[\begin{array}{c}{\mathrm{SOC}}_{b,k+1}\\ U_{bs,k+1}\\ U_{bl,k+1}\end{array}\right]=\left[\begin{array}{ccc}1& 0& 0\\ 0& \exp \left(-\mathrm{\Δ}t/{\mathrm{\τ}}_1\right)& 0\\ 0& 0& \exp \left(-\mathrm{\Δ}t/{\mathrm{\τ}}_2\right)\end{array}\right]\×\left[\begin{array}{c}{\mathrm{SOC}}_{b,k}\\ U_{bs,k}\\ U_{bl,k}\end{array}\right]+\left[\begin{array}{c}\frac{-\mathrm{\η}\mathrm{\Δ}t}{Q_N}\\ R_{bs}\[1-\exp \left(-\mathrm{\Δ}t/{\mathrm{\τ}}_1\right)\]\\ R_{bl}\[1-\exp \left(-\mathrm{\Δ}t/{\mathrm{\τ}}_2\right)\]\end{array}\right]I_{b,k}+w_k$ , [U _b,k]=U _{bo, k}-R _b,ki _b,k-U _{bs, k}-U _{bl, k}+ v _k, SOC in formula _bfor battery system state-of-charge, U _bs, U _blbe 2 RC parallel circuit terminal voltages, R _bs, R _blbe the resistance of 2 RC parallel circuits, τ ₁, τ ₂for time constant, v _k, w _kbe respectively systematic observation noise and process noise, Δ t is the sampling period, U _b, U _b0be respectively battery system terminal voltage and open terminal voltage, R _b, I _bbe respectively battery system internal resistance and electric current, Q _nfor battery system specified electric quantity, k be greater than 1 natural number.

4. a kind of high capacity cell system state-of-charge method of estimation based on Unscented kalman filtering according to claim 1, is characterized in that described Unscented kalman filtering UKF algorithm steps is as follows: 1) init state variable x average E () and square error P ₀; 2) sampled point x is obtained _iand respective weights ω; 3) time of state estimation and square error upgrades; 4) calculated gains matrix; 5) measurement updaue of state estimation and square error.

5. a kind of high capacity cell system state-of-charge method of estimation based on Unscented kalman filtering according to claim 1, is characterized in that described modeling method is not only applicable to battery system, also can be applicable to battery module or monomer.