CN102486529A - Method for detecting state of charge of series super-capacitor bank for urban rail vehicle - Google Patents

Abstract

The invention relates to a method for detecting the state of charge of a series super-capacitor bank for an urban rail vehicle. The method comprises the following steps of: (1) real-timely collecting a charge current value, a discharge current value, and a terminal voltage value of each single super-capacitor when the series super-capacitor bank works; (2) computing a state value of charge of each single super-capacitor according to the current values and the voltage values; (3) determining a maximum value SOCmax and a minimum value SOCmin of the state of charge of a single body of the current series super-capacitor bank through compassion; (4) judging and respectively determining the state value of charge of the series super-capacitor bank when a train is started and braked; and (5) returning to the step (1), and real-timely detecting the state of charge of the series super-capacitor bank. Compared with the prior art, according to the method provided by the invention, each single body in the series super-capacitor bank cannot cause overcharge, overdischarge or other phenomena, so that the service life of the series super-capacitor bank can be prolonged, the safety of a whole energy storage system is ensured simultaneously, and the like.

Description

A kind of city rail vehicle is used the series super capacitor bank charge state detection method

Technical field

The present invention relates to bank of super capacitors EMS technical field, especially relate to a kind of city rail vehicle and use the series super capacitor bank charge state detection method.

Background technology

The world today, energy problem become the major issue of restriction socio-economic development day by day.Energy-conservation, reduction of discharging, low-carbon (LC), environmental protection become the new theme of energy utilization.As urban transportation main delivery vehicle---track vehicle is no exception.The distinguishing feature of urban track traffic is that site distance is shorter, so startup and the brake operating of city rail vehicle in actual motion is more frequent.The disposal route of the energy that braking produced to city rail vehicle in the past is that it is consumed on braking resistor; But this method had both been wasted energy; Cause the problem of temperature rise in platform and the tunnel to increase the weight of the burden of air-conditioning system owing on resistance, producing heat again, thereby cause bigger energy consumption.

The electric energy that produces when adopting this novel energy storage device of ultracapacitor to store the city rail vehicle braking can reasonable solution energy consumption problem.In practical application, for avoiding ultracapacitor to overcharge or the generation of improper situation such as overdischarge, its state-of-charge of monitoring (State of Charge is called for short SOC) in real time.Because ultracapacitor monomer voltage lower (generally being no more than 4V) therefore needs by the monomer series-connected use of a large amount of ultracapacitors in practical application.It is in full accord that but the performance of forming all ultracapacitor monomers of series super capacitor bank can not be accomplished, therefore every joint ultracapacitor energy stored can be different, and inconsistent degree is relevant between its difference and each monomer.

At present, mainly adopt the ampere-hour method to the detection of ultracapacitor monomer state-of-charge.And it is fewer for the research of whole series super capacitor bank charge state detection method.The ampere-hour method is according to charging and discharging currents the integration of time to be detected in real time the SOC of ultracapacitor.This method major defect has: the SOC that 1) can't confirm ultracapacitor under the original state; 2) accuracy of detection depends on the precision of current sensor to a great extent.Therefore use the ampere-hour method can not accurately detect the state-of-charge of ultracapacitor separately.

Summary of the invention

The object of the invention provides a kind of actual value that initial value also can accurately record state-of-charge that do not rely on for the defective that overcomes above-mentioned prior art existence; Each monomer in the series super capacitor bank all can not caused overcharge or phenomenon such as overdischarge; Thereby can prolong the serviceable life of series super capacitor bank, guarantee again that simultaneously the city rail vehicle of the security of whole accumulator system work uses the series super capacitor bank charge state detection method.

The object of the invention can be realized through following technical scheme: a kind of city rail vehicle is used the series super capacitor bank charge state detection method; It is characterized in that this method may further comprise the steps: charging and discharging currents value and each ultracapacitor monomer terminal voltage value when 1) gathering series super capacitor bank work in real time; 2), adopt Kalman filtering algorithm to calculate the state-of-charge value of each ultracapacitor monomer of series super capacitor bank according to current value of gathering in the step 1) and magnitude of voltage; 3) confirm the maximal value SOC of monomer state-of-charge in the current series super capacitor bank through the state-of-charge value of all monomers relatively _MaxMinimum value SOC with the monomer state-of-charge _Min4) judge that train operation operating mode next time is to start or braking, if start operating mode, then the state-of-charge value of current series super capacitor bank is SOC _MinIf damped condition, then the state-of-charge value of current series super capacitor bank is SOC _Max5) return step 1), detect the state-of-charge of series super capacitor bank in real time.

Described step 2) may further comprise the steps:

1) sets up the state-space model of ultracapacitor monomer according to the classical equivalent-circuit model of ultracapacitor monomer; Wherein with state-of-charge as a state variable; The state-space model of described ultracapacitor monomer comprises two parts, i.e. system state equation and system's output equation

System state equation is:

$\left[\begin{array}{c}S(k+1)\\ U_c(k+1)\end{array}\right]=\left[\begin{array}{cc}1& 0\\ 0& 1\end{array}\right]\·\left[\begin{array}{c}S\left(k\right)\\ U_c\left(k\right)\end{array}\right]+\left[\begin{array}{c}\frac{-\mathrm{\Δt}}{Q_0}\\ \frac{\mathrm{\Δt}}{C}\end{array}\right]\·I\left(k\right)$

System's output equation is:

U(k)＝U _c(SOC，k)-RI(k)+v(k)

Make X=[S U _c] ^T, claim that variable X is the state variable of system,

Make

and claim that matrix A is the system matrix of system

Make

and claim that matrix B is the input matrix of system

Make and claim that Matrix C is the output matrix of system; D=-R; Claim that D is the transfer matrix of system

Then the system state state-space model is rewritten as:

$\left\{\begin{array}{c}X(k+1)=\mathrm{AX}\left(k\right)+\mathrm{BI}\left(k\right)\\ U\left(k\right)=\mathrm{CX}\left(k\right)+\mathrm{DI}\left(k\right)+v\left(k\right)\end{array}\right.$

Wherein, S represents state-of-charge, U _CRepresent the voltage at electric capacity two ends in the ultracapacitor monomer equivalent electrical circuit, Q ₀Be the initial total volume of ultracapacitor monomer, Δ t is the time in SI, and I is a charging and discharging currents, and v (k) is for measuring noise, and its variance is R;

2) write Kalman filter equation according to ultracapacitor free state spatial model row, this Kalman filter equation comprises:

The Filtering Estimation equation:

$\hat{X}\left(k\right|k)=\hat{X}\left(k\right|k-1)+K\left(k\right)[U\left(k\right)-C\hat{X}\left(k\right|k-1)]$

The prediction estimate equation:

$\hat{X}\left(k\right|k-1)=A\hat{X}(k-1|k-1)+\mathrm{BI}(k-1)$

The filter gain matrix equation:

K(k)＝P(k|k-1)C ^T[CP(k|k-1)C ^T+R] ^-1

Filtering Estimation error variance battle array equation:

P(k|k)＝[I-K(k)C]P(k|k-1)[I-K(k)C] ^T+K(k)RK ^T(k)

Prediction estimation error variance battle array equation:

P(k|k-1)＝AP(k-1|k-1)A ^T；

3) algorithm is carried out initialization, comprise initialization given original state variate-value X (0) and evaluated error square formation initial value P (0) and noise variance R;

4) gather the charging and discharging currents and the monomer terminal voltage of series super capacitor bank in real time, use the state-of-charge that Kalman filtering algorithm is estimated the ultracapacitor monomer.

Compared with prior art, the present invention has the following advantages:

When 1, group serial connected super capacitor state-of-charge is put in order in estimation, considered the state-of-charge value of this each monomer of group, and provided the state-of-charge of current series super capacitor bank according to the actual operating mode of train.This method guarantees that overcharging all can not appear in train each monomer in the series super capacitor bank under different operating conditions or the generation of situation such as overdischarge; This both can prolong the serviceable life of series super capacitor bank, had guaranteed the security of whole accumulator system work simultaneously again.

2, because Kalman filtering algorithm has very strong robustness, and it is insensitive to the initial value error, even the error of initial set-point and actual value more also can converge to true value faster.Therefore the present invention method of proposing has overcome the shortcoming that the ampere-hour method depends on initial value.

Description of drawings

Fig. 1 is the classical equivalent-circuit model of ultracapacitor monomer;

Fig. 2 is ultracapacitor monomer state-of-charge testing process figure;

Fig. 3 is series super capacitor bank state-of-charge testing process figure.

Embodiment

Below in conjunction with accompanying drawing and specific embodiment the present invention is elaborated.

Embodiment

Before implementing series super capacitor bank state-of-charge evaluation method, need know parameters R, C and the initial total volume Q of the determined ultracapacitor monomer of Fig. 1 model in advance ₀These parameters before ultracapacitor dispatches from the factory by supplier through measuring and provide.

It is following that Fig. 2 and Fig. 3 describe embodiment of the present invention:

At first, the classical equivalent-circuit model according to super capacitor monomer shown in the accompanying drawing 1 can draw following mathematical model:

U _c(t)＝I(t)R+U(t)(1)

$I\left(t\right)=C\frac{{\mathrm{du}}_c}{\mathrm{dt}}---\left(2\right)$

$S\left(t\right)=S\left(t_0\right)-\frac{1}{Q_0}{\∫}_{t_0}^{t}I\left(t\right)\mathrm{dt}---\left(3\right)$

Formula (1), (2) and (3) are put in order and carried out can obtaining after the discretize ultracapacitor monomer

The discrete state spatial model is following:

$\left[\begin{array}{c}S(k+1)\\ U_c(k+1)\end{array}\right]=\left[\begin{array}{cc}1& 0\\ 0& 1\end{array}\right]\·\left[\begin{array}{c}S\left(k\right)\\ U_c\left(k\right)\end{array}\right]+\left[\begin{array}{c}\frac{-\mathrm{\Δt}}{Q_0}\\ \frac{\mathrm{\Δt}}{C}\end{array}\right]\·I\left(k\right)$

U(k)＝U _c(SOC，k)-RI(k)+v(k)

Make X=[S U _c] ^T, claim that variable X is the state variable of system.

Make

and claim that matrix A is the system matrix of system.

Make

and claim that matrix B is the input matrix of system.

Make

and claim that Matrix C is the output matrix of system.

Make D=-R, claim that D is the transfer matrix of system.

Then the system state state-space model is rewritten as:

$\left\{\begin{array}{c}X(k+1)=\mathrm{AX}\left(k\right)+\mathrm{BI}\left(k\right)\\ U\left(k\right)=\mathrm{CX}\left(k\right)+\mathrm{DI}\left(k\right)+v\left(k\right)\end{array}\right.$

Wherein, S represents state-of-charge, U _CRepresent the voltage at electric capacity two ends in the ultracapacitor monomer equivalent electrical circuit, Q ₀Be the initial total volume of ultracapacitor monomer, Δ t is the time in SI, and I is a charging and discharging currents, and v (k) is for measuring noise, and its variance is R.

Then, it is following to write Kalman filter equation according to ultracapacitor free state spatial model row:

The Filtering Estimation equation:

$\hat{X}\left(k\right|k)=\hat{X}\left(k\right|k-1)+K\left(k\right)[U\left(k\right)-C\hat{X}\left(k\right|k-1)]$

The prediction estimate equation:

$\hat{X}\left(k\right|k-1)=A\hat{X}(k-1|k-1)+\mathrm{BI}(k-1)$

The filter gain matrix equation:

K(k)＝P(k|k-1)C ^T[CP(k|k-1)C ^T+R] ^-1

Filtering Estimation error variance battle array equation:

P(k|k)＝[I-K(k)C]P(k|k-1)[I-K(k)C] ^T+K(k)RK ^T(k)

Prediction estimation error variance battle array equation:

P(k|k-1)＝AP(k-1|k-1)A ^T

Then, algorithm is carried out initialization.Comprise given original state variate-value X (0) and evaluated error square formation initial value P (0) and noise variance R.According to the estimated value of the current actual conditions of ultracapacitor, the error of initial value can not influence the estimated accuracy of Kalman filtering algorithm when wherein original state variate-value X (0) used for the user first; Evaluated error square formation initial value P (0) can select a smaller positive number; Noise variance R is the Measurement Variance of sensor.

Gather the charging and discharging currents I (k) and the monomer terminal voltage U (k) of series super capacitor bank in real time, k=1,2,3 ..., utilize Kalman filtering algorithm to estimate the state-of-charge of ultracapacitor monomer.

When city rail vehicle startup or damped condition end, the estimated value of its last moment is the state-of-charge of current ultracapacitor monomer, can confirm state-of-charge minimum value SOC in the whole group serial connected super capacitor through the state-of-charge value that compares each monomer _MinWith state-of-charge maximal value SOC _Max

At last, judge that train operation operating mode next time is to start or braking, operating mode next time just in time is opposite with this operating mode.If the startup operating mode, then the state-of-charge value of current series super capacitor bank is SOC _MinIf damped condition, then the state-of-charge value of current series super capacitor bank is SOC _Max

The present invention is when estimation series super capacitor bank state-of-charge; At first use the state-of-charge value of each ultracapacitor monomer of Kalman filtering algorithm estimation, confirm the state-of-charge of current series super capacitor bank then through the actual operating mode of judging train.This method guarantees under the different operating conditions of train that overcharging all can not appear in each monomer in the series super capacitor bank or situation such as overdischarge takes place; This both can prolong the serviceable life of series super capacitor bank, had guaranteed the security of whole accumulator system work simultaneously again.

Claims (2)

1. a city rail vehicle is used the series super capacitor bank charge state detection method, it is characterized in that, this method may further comprise the steps:

Charging and discharging currents value and each ultracapacitor monomer terminal voltage value when 1) gathering series super capacitor bank work in real time;

2), adopt Kalman filtering algorithm to calculate the state-of-charge value of each ultracapacitor monomer of series super capacitor bank according to current value of gathering in the step 1) and magnitude of voltage;

3) confirm the maximal value SOC of monomer state-of-charge in the current series super capacitor bank through the state-of-charge value of all monomers relatively _MaxMinimum value SOC with the monomer state-of-charge _Min

4) judge that train operation operating mode next time is to start or braking, if start operating mode, then the state-of-charge value of current series super capacitor bank is SOC _MinIf damped condition, then the state-of-charge value of current series super capacitor bank is SOC _Max

5) return step 1), detect the state-of-charge of series super capacitor bank in real time.

2. a kind of city rail vehicle according to claim 1 is used the series super capacitor bank charge state detection method, it is characterized in that described step 2) may further comprise the steps:

1) sets up the state-space model of ultracapacitor monomer according to the classical equivalent-circuit model of ultracapacitor monomer; Wherein with state-of-charge as a state variable; The state-space model of described ultracapacitor monomer comprises two parts, i.e. system state equation and system's output equation

System state equation is:

$\left[\begin{array}{c}S(k+1)\\ U_c(k+1)\end{array}\right]=\left[\begin{array}{cc}1& 0\\ 0& 1\end{array}\right]\·\left[\begin{array}{c}S\left(k\right)\\ U_c\left(k\right)\end{array}\right]+\left[\begin{array}{c}\frac{-\mathrm{\Δt}}{Q_0}\\ \frac{\mathrm{\Δt}}{C}\end{array}\right]\·I\left(k\right)$

System's output equation is:

U(k)＝U _c(SOC，k)-RI(k)+v(k)

Make X=[S U _c] ^T, claim that variable X is the state variable of system,

Make

and claim that matrix A is the system matrix of system

Make

and claim that matrix B is the input matrix of system

Make

and claim that Matrix C is the output matrix of system; D=-R; Claim that D is the transfer matrix of system

Then the system state state-space model is rewritten as:

$\left\{\begin{array}{c}X(k+1)=\mathrm{AX}\left(k\right)+\mathrm{BI}\left(k\right)\\ U\left(k\right)=\mathrm{CX}\left(k\right)+\mathrm{DI}\left(k\right)+v\left(k\right)\end{array}\right.$

Wherein, S represents state-of-charge, U _CRepresent the voltage at electric capacity two ends in the ultracapacitor monomer equivalent electrical circuit, Q ₀Be the initial total volume of ultracapacitor monomer, Δ t is the time in SI, and I is a charging and discharging currents, and v (k) is for measuring noise, and its variance is R;

2) write Kalman filter equation according to ultracapacitor free state spatial model row, this Kalman filter equation comprises:

The Filtering Estimation equation:

$\hat{X}\left(k\right|k)=\hat{X}\left(k\right|k-1)+K\left(k\right)[U\left(k\right)-C\hat{X}\left(k\right|k-1)]$

The prediction estimate equation:

$\hat{X}\left(k\right|k-1)=A\hat{X}(k-1|k-1)+\mathrm{BI}(k-1)$

The filter gain matrix equation:

K(k)＝P(k|k-1)C ^T[CP(k|k-1)C ^T+R] ^-1

Filtering Estimation error variance battle array equation:

P(k|k)＝[I-K(k)C]P(k|k-1)[I-K(k)C] ^T+K(k)RK ^T(k)

Prediction estimation error variance battle array equation:

P(k|k-1)＝AP(k-1|k-1)A ^T；

3) algorithm is carried out initialization, comprise initialization given original state variate-value X (0) and evaluated error square formation initial value P (0) and noise variance R;

4) gather the charging and discharging currents and the monomer terminal voltage of series super capacitor bank in real time, use the state-of-charge that Kalman filtering algorithm is estimated the ultracapacitor monomer.

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