CN103558556A - Power battery SOH estimation method - Google Patents

Abstract

The invention provides a lithium battery SOH estimation method. According to the lithium battery SOH estimation method, a system parameter estimation method is used for estimating the actual capacity and Ohm internal resistance of a battery; a redundancy processing method is used for processing the estimated actual capacity and Ohm internal resistance; the actual capacity and the Ohm internal resistance are used for estimating the health state of the battery respectively, and a true health state of the battery is obtained by means of weighting the relation between the actual capacity and the Ohm internal resistance; a smoothing weighting method is used for processing the health state value of the battery. By means of the lithium battery SOH estimation method, the purpose of accurately reflecting the true health state of the battery can be achieved.

Description

A kind of electrokinetic cell SOH evaluation method

Technical field

The invention belongs to new-energy automobile battery management system field, relate to a kind of electrokinetic cell health status (SOH) evaluation method.

Background technology

Obtain people's attention and obtained large development today that efficiently, the electric automobile of energy-saving and environmental protection goes from bad to worse at environment, petroleum-based energy is day by day exhausted, therefore the health status (SOH, State of Health) of accurate estimation electrokinetic cell seems particularly important for further optimization and the further developing of electric automobile of the whole control strategy of electric automobile.

Battery as time goes by with the use of battery, battery occurs aging, cause thering is different battery behaviors at different times, be specifically reflected in the size of SOH value of battery, so SOH value has reflected the overall performance of battery and the ability that discharges electric energy under certain condition intuitively.For a new battery, its SOH value is more than or equal to 100% often, because expendable physical chemical factor causes the slow aging SOH of the causing value of battery, declines gradually.At present the calculating of SOH value is not had to general computing formula, such as in ieee standard 1188-1996 with SOH=Qnow/Qnew computing formula regulation when battery capacity drop to new battery capacity 80% time, while being SOH<80%, battery just should be replaced.

Health status and the internal resistance of cell due to battery, charging and discharging capabilities, the many factors such as self discharge are relevant, therefore in testing laboratory, may obtain by test the SOH value of battery, but the various conditions that there is no testing laboratory in battery use procedure, therefore cannot directly obtain by measuring method the SOH value of battery, if the battery recycling of electric automobile is carried out to the measurement of SOH value to testing laboratory, will waste a large amount of manpower and financial resources, therefore obtaining battery SOH value by battery characteristics parameter identification is the method for a human-saving and financial resources, therefore at present a lot of battery management system developers are are researching and developing the evaluation method of SOH, but also do not have ripe method to be applied to product at present.

Summary of the invention

The present invention is directed to the deficiencies in the prior art, thereby propose a kind of internal resistance of expanded Kalman filtration algorithm estimating battery and battery capacity based on battery status spatial model and estimate battery SOH evaluation method, reach the object that more accurately reflects the real health status of battery.

The present invention proposes a kind of lithium battery SOH evaluation method, and it is to utilize systematic parameter evaluation method to estimate actual capacity and the battery ohmic internal resistance of battery; Utilize redundancy processing method to process actual capacity and the ohmic internal resistance of estimation; Utilize actual capacity and the ohmic internal resistance health status of estimating battery respectively, and obtain the real health status of battery by the weighting processing that is related between them; Adopt the health status value of filtration combined weighted disposal methods battery.

Evaluation method of the present invention comprises that step is as follows:

Step 1: set up following state-space model and use Kalman filtering algorithm identification system parameter according to electrokinetic cell circuit model:

V _B(k)＝OCV(k)-R ₀(k)I _B(k)-V _p(k)+v(k) (2)

V in formula (1) _pbattery polarization voltage; OCV is battery open circuit voltage, R _oit is battery ohmic internal resistance; C _bit is battery actual capacity;

for state-noise.V in formula (2) _bit is the terminal voltage of battery; I _bbe the electric current of battery, v is for measuring noise.

It is battery state of charge (SOC) that this step has embodied open-circuit voltage OCV and (SOC (k) T), considers the impact on battery open circuit voltage of battery temperature and state-of-charge to funtcional relationship OCV (the k)=F of temperature (T).

Step 2: gather electric current, voltage and the temperature value of lithium battery, and use Recursive Formulas of Kalman Filter appraising model parameters R _o, C _b.

This step is introduced filter gain correction factor matrix ξ=[ξ in Recursive Formulas of Kalman Filter ₁ξ ₂ξ ₃ξ ₄ξ ₅ξ ₆] and dynamic noise variance correction factor

concrete introducing formula is as follows:

$K\left(k\right)=\mathrm{\&xi;}*P(k+1|k){\hat{C}}^T(k+1){[\hat{C}(k+1)P(k+1|k){\hat{C}}^T(k+1)+{\mathrm{\&xi;}}_{R_k}*R_k]}^{-1}$

$P(k+1|k)=\hat{A}\left(k\right)P\left(k\right){\hat{A}}^T\left(k\right){\mathrm{\&xi;}}_{Q_k}*Q_k.$

In above formula, K is Kalman filtering gain matrix; P varivance matrix;

it is state-transition matrix;

observing matrix; R is process noise; Q is observation noise.

Step 3: when electric current is between interval [I, I] time and lasting N minute time, start to calculate battery actual capacity C _bwith battery ohmic internal resistance R _oaccumulated value C=Σ C _b, R=Σ R _o.

Step 4: when electric current is between interval [I, I] time and lasting N+M minute time, stop calculating battery actual capacity and battery ohmic internal resistance accumulated value, and calculate average cell actual capacity and average cell ohmic internal resistance $\stackrel{\&OverBar;}{C}=C/M,\stackrel{\&OverBar;}{R}=R/M,$ In formula, M is the duration.

Step 3,4 is got rid of the parameter of identification algorithm estimation and is dispersed situation, guarantees that the internal resistance of cell and the battery capacity value of estimation converges on true value.

Step 5: utilize estimated value to calculate the health status (SOH) of battery:

${\mathrm{SOH}}_C=\frac{C_{\mathrm{New}}-\stackrel{\&OverBar;}{C}}{C_{\mathrm{New}}-C_{\mathrm{End}}},{\mathrm{SOH}}_R=1-\frac{\stackrel{\&OverBar;}{R}-R_{\mathrm{New}}}{{\stackrel{\&OverBar;}{R}}_{\mathrm{End}}-R_{\mathrm{New}}}$

C in formula _newnew electrokinetic cell actual capacity; C _endactual capacity when electrokinetic cell is aged to the discontented car load of performance and needs; R _newnew electrokinetic cell internal resistance value;

internal resistance value when electrokinetic cell is aged to the discontented car load of performance and needs;

Step 6: utilize method of weighting to obtain the SOH of battery, its weighted value is w,

SOH＝w*SOH _C+(1-w)*SOH _R

Step 5,6 utilizes weighting coefficient w to make battery capacity and internal resistance of cell combined reaction go out the real health status of battery.

Step 7: the SOH value of battery is carried out to wavelet filteration method processing and meet car load application requirements,

γ in formula _ibe wavelet filtering coefficient, m is wavelet coefficient number, SOH _iit is the health status in battery past.

Step 7 is utilized wavelet filteration method, makes the current health status of battery affected by health status in the past.

Compared with prior art, the invention has the advantages that:

1, the present invention utilizes System Discrimination algorithm to pick out the actual capacity of battery and the ohmic internal resistance of battery simultaneously, and utilizes these two important calculation of characteristic parameters to go out the SOH value of battery, can more accurately reflect the real health status of battery.

2, the present invention introduces dynamic noise variance and filter gain correction factor matrix makes identification algorithm possess stronger antijamming capability.

3, the SOH value that the present invention is applicable to various operating modes and estimation can reflect electrokinetic cell health status preferably.

The present invention is applicable to various driven energy saving vapour Vehicular dynamic battery health status estimations.

Accompanying drawing explanation

In order to be illustrated more clearly in example of the present invention or technical scheme of the prior art, will the accompanying drawing of required use in example or description of the Prior Art be briefly described below.

Fig. 1 electrokinetic cell SOH evaluation method process flow diagram.

Embodiment

For the ease of those skilled in the art's understanding, below in conjunction with accompanying drawing 1 and specific embodiment, enforcement journey mode of the present invention is described.

The method that the embodiment of the present invention proposes comprises the steps:

Step 1: set up following state-space model and use the Kalman filtering algorithm identification system parameter in system identifying method according to electrokinetic cell circuit model:

V _B(k)＝OCV(k)-R ₀(k)I _B(k)-V _p(k)+v(k) (2)

In formula (1)

for state-noise; V is for measuring noise; V _pbattery polarization voltage; OCV is battery open circuit voltage; R _oit is the internal resistance of cell; C _bit is battery actual capacity.V in formula (2) _bthe terminal voltage of battery, open-circuit voltage OCV be battery state of charge and temperature funtcional relationship OCV (k)=F (SOC (k), T).

Step 2: utilize the current/voltage temperature value gathering, and use Recursive Formulas of Kalman Filter appraising model parameters R _o, C _b.

In Recursive Formulas of Kalman Filter, introduce filter gain correction factor matrix ξ=[ξ ₁ξ ₂ξ ₃ξ ₄ξ ₅ξ ₆] and dynamic noise variance correction factor

concrete introducing formula is as follows:

$K\left(k\right)=\mathrm{\&xi;}*P(k+1|k){\hat{C}}^T(k+1){[\hat{C}(k+1)P(k+1|k){\hat{C}}^T(k+1)+{\mathrm{\&xi;}}_{R_k}*R_k]}^{-1}$

$P(k+1|k)=\hat{A}\left(k\right)P\left(k\right){\hat{A}}^T\left(k\right){\mathrm{\&xi;}}_{Q_k}*Q_k$

Step 3: when electric current is between interval [I, I] time and lasting N minute time, start to calculate battery actual capacity C _bwith battery ohmic internal resistance R _oaccumulated value C=Σ C _b, R=Σ R _o.

Step 4: when electric current is between interval [I, I] time and lasting N+M minute time, stop calculating battery actual capacity and battery ohmic internal resistance accumulated value, and calculate average cell actual capacity and average cell ohmic internal resistance $\stackrel{\&OverBar;}{C}=C/M,\stackrel{\&OverBar;}{R}=R/M,$ In formula, M is the duration.

Step 5: utilize estimated value to calculate the health status (SOH) of battery:

${\mathrm{SOH}}_C=\frac{C_{\mathrm{New}}-\stackrel{\&OverBar;}{C}}{C_{\mathrm{New}}-C_{\mathrm{End}}},{\mathrm{SOH}}_R=1-\frac{\stackrel{\&OverBar;}{R}-R_{\mathrm{New}}}{{\stackrel{\&OverBar;}{R}}_{\mathrm{End}}-R_{\mathrm{New}}}$

C in formula _newnew electrokinetic cell actual capacity; C _endactual capacity when electrokinetic cell is aged to the discontented car load of performance and needs; R _newnew electrokinetic cell internal resistance value;

internal resistance value when electrokinetic cell is aged to the discontented car load of performance and needs;

Step 6: utilize method of weighting to obtain the SOH of battery, its weighted value is w,

SOH＝w*SOH _C+(1-w)*SOH _R

Step 7: the SOH value of battery is carried out to wavelet filteration method processing and meet car load application requirements, γ in formula _ibe wavelet filtering coefficient, m is wavelet coefficient number, SOH _iit is the health status in battery past.

Claims (2)

1. a lithium battery health status evaluation method, is characterized in that, described method is to utilize systematic parameter evaluation method to estimate actual capacity and the battery ohmic internal resistance of battery; Utilize redundancy processing method to process actual capacity and the battery ohmic internal resistance of estimation; Utilize actual capacity and the battery ohmic internal resistance health status of estimating battery respectively, and obtain the real health status of battery by the weighting processing that is related between them; Finally adopt the health status value of filtration combined weighted disposal methods battery.

2. lithium battery health status evaluation method according to claim 1, is characterized in that, described evaluation method comprises that step is as follows:

Step 1: set up following state-space model and use Kalman filtering algorithm identification system parameter according to electrokinetic cell circuit model:

V _B(k)＝OCV(k)-R ₀(k)I _B(k)-V _p(k)+v(k) (2)

V in formula (1) _pbattery polarization voltage; OCV is battery open circuit voltage, R _oit is battery ohmic internal resistance; C _bit is battery actual capacity;

for state-noise.V in formula (2) _bit is the terminal voltage of battery; I _bbe the electric current of battery, v is for measuring noise; Open-circuit voltage OCV be battery state of charge (SOC) with funtcional relationship OCV (the k)=F of temperature (T) (SOC (k), T).

Step 2: detect lithium battery electric current, voltage and temperature value, and use Recursive Formulas of Kalman Filter appraising model parameters R _o, C _b.

In Recursive Formulas of Kalman Filter, introduce filter gain correction factor matrix ξ=[ξ ₁ξ ₂ξ ₃ξ ₄ξ ₅ξ ₆] and dynamic noise variance correction factor

concrete introducing formula is as follows:

$K\left(k\right)=\mathrm{\&xi;}*P(k+1|k){\hat{C}}^T(k+1){[\hat{C}(k+1)P(k+1|k){\hat{C}}^T(k+1)+{\mathrm{\&xi;}}_{R_k}*R_k]}^{-1}$

$P(k+1|k)=\hat{A}\left(k\right)P\left(k\right){\hat{A}}^T\left(k\right){\mathrm{\&xi;}}_{Q_k}*Q_k$

In above formula, K is Kalman filtering gain matrix; P varivance matrix;

it is state-transition matrix;

observing matrix; R is process noise; Q is observation noise.

Step 3: when electric current is between interval [I, I] time and lasting N minute time, start to calculate battery actual capacity C _bwith battery ohmic internal resistance R _oaccumulated value C=Σ C _b, R=Σ R _o;

Step 4: when electric current is between interval [I, I] time and lasting N+M minute time, stop calculating battery actual capacity and battery ohmic internal resistance accumulated value, and calculate average cell actual capacity and average cell ohmic internal resistance $\stackrel{\&OverBar;}{C}=C/M,\stackrel{\&OverBar;}{R}=R/M,$ In formula, M is the duration;

Step 5: utilize estimated value to calculate the health status (SOH) of battery:

${\mathrm{SOH}}_C=\frac{C_{\mathrm{New}}-\stackrel{\&OverBar;}{C}}{C_{\mathrm{New}}-C_{\mathrm{End}}},{\mathrm{SOH}}_R=1-\frac{\stackrel{\&OverBar;}{R}-R_{\mathrm{New}}}{{\stackrel{\&OverBar;}{R}}_{\mathrm{End}}-R_{\mathrm{New}}}$

C in formula _newnew electrokinetic cell actual capacity; C _endactual capacity when electrokinetic cell is aged to the discontented car load of performance and needs; R _newnew electrokinetic cell internal resistance value;

internal resistance value when electrokinetic cell is aged to the discontented car load of performance and needs;

Step 6: utilize method of weighting to obtain the SOH of battery, its weighted value is w,

SOH＝w*SOH _C+(1-w)*SOH _R

Step 7: the SOH value to battery is carried out wavelet filteration method processing,

γ in formula _ibe wavelet filtering coefficient, m is wavelet coefficient number, SOH _iit is the health status in battery past.

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