CN109164391A - A kind of power battery charged state estimation on line method and system - Google Patents
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Abstract
A kind of power battery charged state estimation on line method and system the present invention provides process concision and compact, with strong noise resisting ability.The present invention is when carrying out dynamically track to battery time-varying parameter using the RLS algorithm with forgetting factor based on battery Order RC equivalent-circuit model, using covariance matrix Qiao Lesiji Decomposition iteration calculating process, calculation amount is small and stablizes, and diagonal matrix element can be used for adaptively adjusting forgetting factor；There is a problem of that open-circuit voltage and SOC one are unstable for certain type power batteries, introduces the simulation estimate to hysteresis voltage, and rectify a deviation for open-circuit voltage, guarantee final open-circuit voltage to the accuracy of SOC mapping relations；When carrying out SOC estimation using spreading kalman algorithm (EKF), using containing only SOC single argument state of charge equation and based on the observational equation of terminal voltage, the update calculating for having reduced or remitted redundant state and hypothesis and estimation to its statistical nature, ensure that the real-time and response efficiency of calculating.
Description
Technical field
The present invention relates to new-energy automobile fields, more particularly to a kind of power battery charged state online evaluation method and are
System.
Background technique
As country widelys popularize New-energy electric vehicle, battery-operated motor cycle, the market demand of power battery is more next
It is bigger, but the requirement simultaneously to the capacity of battery, service life and cost is also higher and higher, and wherein battery life largely depends on
In usage mode.In order to hold water using battery, avoid damaging battery, need sufficiently accurately to estimate battery
Present battery state-of-charge.The assessment of power battery charged state (SOC) and real time reaction speed have full-vehicle control important
Meaning.
Traditional SOC estimation method includes current integration method, open circuit voltage method, Kalman filtering method etc..Current integration method
It is to integrate to obtain the real-time change of electricity to charging or discharging current on the basis of initial quantity of electricity；Method advantage is simple easily realization, disadvantage
It is to be difficult to eliminate to initial value dependence and error accumulation.Open circuit voltage method is closed according to open-circuit voltage (OCV) is corresponding with the dullness of SOC
System calculates SOC by measurement OCV；The disadvantage is that OCV measurement need when battery does not work and sufficient standing for a period of time after ability
It carries out.Kalman filtering method is the optimal estimation made based on system model to battery status in Minimum Mean Square Error meaning, and method is excellent
Point is that error correcting capabilities are strong, the disadvantage is that higher to model accuracy dependence；Battery is as slow time-varying nonlinear system, parameter
Real-time change, model parameter uncertainty are stronger.
In addition, some methods using the least square method of recursion (RLS) with forgetting factor to battery time-varying parameter dynamic with
Track carries out system noise and observation noise in conjunction with spreading kalman algorithm to construct accurate battery system model
Filtering processing reduces estimation error, comparatively ideal solution of can yet be regarded as.However, tradition RLS method is in data statistics point
Calculation amount is bigger than normal in analysis, and what spreading kalman algorithm used is mostly three condition equation (SOC and two RC circuits on voltage) additional one
The system model of observational equation (terminal voltage), calculating process use excessive time-varying parameter (such as capacitor, resistance, internal resistance),
Error in RLS parameter identification is not only had accumulated, while calculates SOC and complicating.
To sum up, the estimation on line of SOC and its real time reaction speed are of great significance to full-vehicle control, however existing
Calculation amount is larger in SOC estimation method, calculating process is more complex, affects the realization of target.
Summary of the invention
To solve the above problems, the present invention be directed to presently, there are computational efficiency it is low, redundant computation is more the problems such as, provide
A kind of process concision and compact has power battery charged state (SOC) estimation on line method of strong noise resisting ability simultaneously.
The technical solution adopted by the present invention to solve the technical problems is:
A kind of power battery charged state estimation on line method, the evaluation method use following steps:
Step 1: being indicated and the covariance matrix Qiao Lesiji least square method of recursion decomposed based on voltage intermediate parameters
Open-circuit voltage, which returns, to be calculated；
Step 2: the open-circuit voltage correction based on hysteresis voltage analog；
Step 3: expanded Kalman filtration algorithm and simplified Sage-Husa based on SOC single argument state of charge equation
The SOC of adaptive observation variance method of adjustment, which is denoised, to be calculated.
Further, step 1 uses following sub-step:
Step 1a): Qiao of the initial intermediate parameters vector value θ of setting battery, initial forgetting factor λ and initial covariance matrix
Le Siji decomposed P=UDUT, wherein U is unit upper triangular matrix, and D is diagonal matrix；
Step 1b): read in battery current flow voltage observation vector valueIt calculatesAnd g=Df；
Step 1c): calculating matrix D and U are updated according to λ, f, g；
Step 1d): current gain vector K and prediction error e are calculated, battery intermediate parameters θ=θ+Ke is updated；
Step 1e): by intermediate parameters θ inverse battery initial parameter, including internal resistance Rohm, open-circuit voltage Voc, and calculate open circuit
The changing value dV of voltageoc；
Step 1f): forgetting factor dynamic adjusts, when | dVoc/ Δ t | > δV,max, increase forgetting factor λ=τup×λ+(1-
τup)×1,τup∈[0.95,1)；Otherwise, as | e | > δe,max, reduce forgetting factor λ=τdownλ,τdown∈[0.98,1)；
Step 1g): covariance dynamic adjusts, when D diagonal element | dii| < δD,min, set dii=δD,min；Work as θi| > δθ,max,
Set dii=dii+δD；Go to step 1b).
Further, step 2 uses following sub-step:
Step 2a): it is directed to charging and discharging process respectively, measures battery hysteresis voltage attenuation parameter beta, current efficiency parameter
ηI, half way maximum hysteresis voltage Vh,maxWith initial hysteresis voltage Vh,0；
Step 2b): establish hysteresis voltage change mathematical model
Step 2c): current hysteresis voltage V is calculated with difference method simulationh(tk)=Vh(tk-1)+βηII(tk-1)[Vh,max-
sign(I(tk-1))Vh(tk-1)]×Δt；
Step 2d): open-circuit voltage correction processing Vo=Voc(tk)-Vh(tk)。
Further, step 3 uses following sub-step:
Step 3a): establish and be based on the univariate state of charge equation SOC'(t of SOC)=ηII(t)/QN+ w, wherein QN=36
×Ahnominal, AhnominalIt is battery maximum charge capacity, w is systematic procedure noise；
Step 3b): according to RLS algorithm to the estimation V of battery terminal voltagepredict(t)=Voc(t)+IRohm(t)+Vdl(t)
+Vdf(t), terminal voltage systematic observation expression is obtained:
And then it establishes
Systematic observation equationLeft side observation according toMeter
It calculates, the right Vo(SOC, t) is calculated by Voc-SOC mapping table, and v is systematic survey noise；
Step 3c): in the initial stage, is directly rectified a deviation, tabled look-up according to the open-circuit voltage of RLS algorithm estimation and hysteresis voltage
To SOC value SOC=h (Vo,T),Vo=Voc-Vh；
Step 3d): in follow-up phase, the lotus of above-mentioned single state variable system is calculated using extended BHF approach method
Electricity condition value；
Step 3e): during calculating SOC using EKF algorithm, utilize the simplification Sage-Husa method with forgetting factor
The adaptively variance of dynamic debugging system measurement noise v, controlled estimation error.
The present invention also proposes a kind of a kind of power battery lotus using above-mentioned power battery charged state estimation on line method
Electricity condition estimation on line system, including battery measurement data input module, battery parameter update module, Parameter Switch module, in
Between parameter updating module, state-of-charge RLS-EKF cycle calculations module, algorithm parameter management module.
The beneficial effects of the present invention are:
1. based on battery Order RC equivalent-circuit model using the RLS algorithm with forgetting factor to battery time-varying parameter into
When Mobile state tracks, covariance matrix Qiao Lesiji Decomposition iteration calculating process is used, calculation amount is small and stablizes, diagonal matrix
Element can be used for adaptively adjusting forgetting factor；
2. there is a problem of that open-circuit voltage and SOC one are unstable for certain type power batteries, draw
Enter the simulation estimate to hysteresis voltage, and rectify a deviation for open-circuit voltage, guarantees final open-circuit voltage to SOC mapping relations
Accuracy；
3. when carrying out SOC estimation using spreading kalman algorithm (EKF), using containing only SOC single argument state of charge side
Journey and observational equation based on terminal voltage, the update for having reduced or remitted redundant state calculate and to the hypothesis of its statistical nature with estimate
It calculates, ensure that the real-time and response efficiency of calculating.
Detailed description of the invention
Fig. 1 is the estimation process figure of evaluation method of the present invention.
Fig. 2 is the frame construction drawing of estimating system of the present invention.
Specific embodiment
In order to which objects and advantages of the present invention are more clearly understood, the present invention is carried out with reference to embodiments further
It is described in detail.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used in the restriction present invention.
The embodiment is illustrated below with reference to Fig. 1-2.
The power battery charged state estimation on line method of the present embodiment, using following steps:
Step 1: being indicated and the covariance matrix Qiao Lesiji least square method of recursion decomposed based on voltage intermediate parameters
Open-circuit voltage, which returns, to be calculated；
Step 2: the open-circuit voltage correction based on hysteresis voltage analog；
Step 3: expanded Kalman filtration algorithm and simplified Sage-Husa based on SOC single argument state of charge equation
The SOC of adaptive observation variance method of adjustment, which is denoised, to be calculated.
In step 1, taking charging current I is positive sign, there is V=Voc+IRohm+Vdl+Vdf, wherein
V,Voc,Vdl,Vdf,RohmRespectively terminal voltage, open-circuit voltage, two capacitance voltages and internal resistance.By Order RC circuit
Model is discrete to be obtained:Wherein Vk,IkIt is discrete end
Road voltage and current, θk-1=(θ1,θ2,...,θ6)TIt is model intermediate parameters,It is input
Data.The output of step 1 includes the parameter of identificationEspecially open-circuit voltageWith the end of model prediction
Road voltage
In the calculating circulation of step 1, covariance matrix Qiao Lesiji decomposed P=UDUTRecurrence calculation include following step
Suddenly (λ ∈ [0.95,1) is forgetting factor, j=1,2 ..., 6):
In step 2, work as Ik-1When > 0, η is takenI=0.99, β=2.47 × 10-5(C-1)；Work as Ik-1When≤0, η is takenI=
1.0, β=3.70 × 10-5(C-1)。
In step 3, EKF includes following key step:
Initialization.K=0 setting and
SOC status predication
Mean-square error forecastWhereinIt is pre-set system noise variance；
Calculate gainHere
State updates
Mean square deviation updates
Variance is measured based on measurement noise statistics estimation to update, it is adaptive used here as simplified Sage-Husa
Answer observational variance method of adjustment:
B is forgetting factor, between value 0.95-0.99；
It is above-mentionedIt updates and calculates the adaptive method that uses, i.e., just updated when judging to filter exception, otherwise, just not more
Newly, calculation amount can be saved in this way.Filtering exceptional condition is to meet following inequality:
Fig. 2 is using the embodiment of the power battery charged state estimation on line system of above-mentioned estimation on line method, this is
System includes battery measurement data input module, battery parameter update module, Parameter Switch module, intermediate parameters update module, lotus
Electricity condition RLS-EKF cycle calculations module, algorithm parameter management module.
The present invention provides a kind of calculating process concision and compact while having the power battery charged state of strong noise resisting ability
(SOC) estimation on line method, this method are forgotten by data such as real-time measurement battery-end road electric current, voltage and temperature using band
The least square method of recursion (RLS) of the factor carries out regression statistical analysis to it on the basis of battery Order RC equivalent-circuit model,
The batteries time-varying parameter value such as open-circuit voltage is picked out, is carried out in combination with the further open-circuit voltage of hysteresis voltage computation model
Correction processing.Then Extended Kalman filter is utilized on the basis of state of charge equation and open-circuit voltage and SOC mapping relations
Algorithm (EKF) calculates stable SOC value.In order to avoid the complexity in the identification calculating of battery initial parameter, method is used
Tractable intermediate parameters and correlation Qiao Lesiji Decomposition iteration calculating process.Calculation stages are filtered in SOC, method is only used
The state of charge equation of single argument containing SOC and the observational equation based on terminal voltage, reduced or remitted redundant state update calculate and it is right
The hypothesis and estimation of its statistical nature.Above two processing strategie simplifies entire SOC calculating process, ensure that the real-time of calculating
Property.Meanwhile on the basis of considering hysteresis voltage, dynamic adjustment RLS forgetting factor and adaptive updates EKF observational variance, method
Effectively noise can be filtered, obtain stable and accurate SOC curve.It is returned in open-circuit voltage and calculates and utilize actual measurement
On the basis of SOC mapping table, method has stronger robustness to SOC initial value error.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, without departing from the principle of the present invention, it can also make several improvements and retouch, these improvements and modifications are also answered
It is considered as protection scope of the present invention.
Claims (5)
1. a kind of power battery charged state estimation on line method, which is characterized in that the evaluation method uses following steps:
Step 1: indicating the open circuit with the covariance matrix Qiao Lesiji least square method of recursion decomposed based on voltage intermediate parameters
Voltage return calculates；
Step 2: the open-circuit voltage correction based on hysteresis voltage analog；
Step 3: expanded Kalman filtration algorithm and simplified Sage-Husa based on SOC single argument state of charge equation are adaptive
It answers the SOC of observational variance method of adjustment to denoise to calculate.
2. a kind of power battery charged state estimation on line method as described in claim 1, which is characterized in that step 1 uses
Following sub-step:
Step 1a): the Qiao Lesi of the initial intermediate parameters vector value θ of setting battery, initial forgetting factor λ and initial covariance matrix
Base decomposed P=UDUT, wherein U is unit upper triangular matrix, and D is diagonal matrix；
Step 1b): read in battery current flow voltage observation vector valueIt calculatesAnd g=Df；
Step 1c): calculating matrix D and U are updated according to λ, f, g；
Step 1d): current gain vector K and prediction error e are calculated, battery intermediate parameters θ=θ+Ke is updated；
Step 1e): by intermediate parameters θ inverse battery initial parameter, including internal resistance Rohm, open-circuit voltage Voc, and calculate open-circuit voltage
Changing value dVoc；
Step 1f): forgetting factor dynamic adjusts, when | dVoc/ Δ t | > δV,max, increase forgetting factor λ=τup×λ+(1-τup)×
1,τup∈[0.95,1)；Otherwise, as | e | > δe,max, reduce forgetting factor λ=τdownλ,τdown∈[0.98,1)；
Step 1g): covariance dynamic adjusts, when D diagonal element | dii| < δD,min, set dii=δD,min；When | θi| > δθ,max, set
dii=dii+δD；Go to step 1b).
3. a kind of power battery charged state estimation on line method as described in claim 1, it is characterised in that: step 2 uses
Following sub-step:
Step 2a): it is directed to charging and discharging process respectively, measures battery hysteresis voltage attenuation parameter beta, current efficiency parameter ηI, half
Journey maximum hysteresis voltage Vh,maxWith initial hysteresis voltage Vh,0；
Step 2b): establish hysteresis voltage change mathematical model
Step 2c): current hysteresis voltage V is calculated with difference method simulationh(tk)=Vh(tk-1)+βηII(tk-1)[Vh,max-sign
(I(tk-1))Vh(tk-1)]×Δt；
Step 2d): open-circuit voltage correction processing Vo=Voc(tk)-Vh(tk)。
4. a kind of power battery charged state estimation on line method as described in claim 1, it is characterised in that: step 3 uses
Following sub-step:
Step 3a): establish and be based on the univariate state of charge equation SOC'(t of SOC)=ηII(t)/QN+ w, wherein QN=36 ×
Ahnominal, AhnominalIt is battery maximum charge capacity, w is systematic procedure noise；
Step 3b): according to RLS algorithm to the estimation V of battery terminal voltagepredict(t)=Voc(t)+IRohm(t)+Vdl(t)+Vdf
(t), terminal voltage systematic observation expression is obtained:
And then establish system
Observational equationLeft side observation according toIt calculates,
The right Vo(SOC, t) is calculated by Voc-SOC mapping table, and v is systematic survey noise；
Step 3c): in the initial stage, is directly rectified a deviation according to the open-circuit voltage of RLS algorithm estimation and hysteresis voltage, table look-up to obtain
SOC value SOC=h (Vo,T),Vo=Voc-Vh；
Step 3d): in follow-up phase, the charged shape of above-mentioned single state variable system is calculated using extended BHF approach method
State value；
Step 3e): it is adaptive using the simplification Sage-Husa method with forgetting factor during calculating SOC using EKF algorithm
Ground dynamic is answered to adjust the variance of systematic survey noise v, controlled estimation error.
5. using a kind of power battery charged state of power battery charged state estimation on line method described in claim 1-4
Estimation on line system, it is characterised in that: including battery measurement data input module, battery parameter update module, Parameter Switch mould
Block, intermediate parameters update module, state-of-charge RLS-EKF cycle calculations module, algorithm parameter management module.
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| CN111474481A (en) * | 2020-04-13 | 2020-07-31 | 深圳埃瑞斯瓦特新能源有限公司 | Battery SOC estimation method and device based on extended Kalman filtering algorithm |
| CN111999654A (en) * | 2020-08-04 | 2020-11-27 | 力高(山东)新能源技术有限公司 | Adaptive extended Kalman estimation SOC algorithm |
| CN111999654B (en) * | 2020-08-04 | 2023-05-12 | 力高(山东)新能源技术股份有限公司 | Self-adaptive extended Kalman estimation SOC algorithm |
| CN112415392A (en) * | 2020-11-03 | 2021-02-26 | 珠海格力电器股份有限公司 | Method for determining forgetting factor, electronic equipment, storage medium and device |
| CN112986848A (en) * | 2021-01-27 | 2021-06-18 | 力高(山东)新能源技术有限公司 | Method for estimating SOH of power battery |
| CN113030752A (en) * | 2021-04-12 | 2021-06-25 | 安徽理工大学 | Online parameter identification and SOC joint estimation method based on forgetting factor |
| CN113030752B (en) * | 2021-04-12 | 2024-03-29 | 安徽理工大学 | Online parameter identification and SOC joint estimation method based on variable forgetting factor |
| CN113655277A (en) * | 2021-08-13 | 2021-11-16 | 海南师范大学 | Insulation resistance value detection method during disconnection of power battery of electric automobile |
| CN113655279A (en) * | 2021-08-13 | 2021-11-16 | 海南师范大学 | Insulation resistance value detection method during disconnection of power battery of electric automobile |
| CN113655279B (en) * | 2021-08-13 | 2023-09-26 | 海南师范大学 | Insulation resistance value detection method during disconnection of electric automobile power battery |
| CN113655277B (en) * | 2021-08-13 | 2023-09-26 | 海南师范大学 | Insulation resistance value detection method during disconnection of electric automobile power battery |
| CN114966408A (en) * | 2022-04-29 | 2022-08-30 | 广东汇天航空航天科技有限公司 | Power battery online parameter identification method, device and equipment and manned aircraft |
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