LU504545B1 - Evaluation method of battery energy state based on adaptive feedback correction of forgetting factors - Google Patents
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- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 4
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- 229910000625 lithium cobalt oxide Inorganic materials 0.000 description 1
- BFZPBUKRYWOWDV-UHFFFAOYSA-N lithium;oxido(oxo)cobalt Chemical compound [Li+].[O-][Co]=O BFZPBUKRYWOWDV-UHFFFAOYSA-N 0.000 description 1
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Abstract
The invention provides an evaluation method of battery energy state based on adaptive feedback correction of forgetting factors, comprising: S1, acquiring model parameters of the lithium ion battery, and discretizing the model parameters to obtain discrete model parameters; S2, estimating model parameters based on an adaptive forgetting factor recursive least squares algorithm and separating the model parameters to obtain separated parameters; S3, obtaining a model error based on the discrete model parameters and the separation parameters; S4, based on the model error, adopting a fuzzy logic controller to circularly execute S2-S3, and adaptively adjusting the forgetting factor to obtain the final model parameters; S5, estimating the energy state based on the final model parameters to obtain a final energy state prediction result. On that basis of fully considering the group work of lithium ion battery and combining with the establishment of SOE estimation.
Description
DESCRIPTION LU504545
EVALUATION METHOD OF BATTERY ENERGY STATE BASED ON ADAPTIVE
FEEDBACK CORRECTION OF FORGETTING FACTORS
The invention relates to the technical field of measurement and control of new energy batteries, in particular to an evaluation method of battery energy state based on adaptive feedback correction of forgetting factors.
During the whole life cycle of lithium-ion batteries, the monitoring and adjustment of the core parameter SOE (state of energy) by Battery Management System (BMS) will affect the effect and safety of emergency power output. Therefore, it is very necessary to monitor the change of this parameter in real time and ensure the working performance of lithium-ion batteries based on this. Because the group SOE estimation technology in
BMS is not mature, the potential safety hazards in the use process seriously restrict the development of lithium-ion batteries.
For lithium-ion batteries, reliable BMS management depends on accurate SOE value; when the value is known, it can not only carry out reliable energy management and safety control, but also avoid the premature damage of lithium-ion batteries and prolong their service life. Therefore, accurate estimation of SOE value is very important to ensure the working performance, energy and safety management of lithium-ion batteries. The SOC (state of charge) estimation model construction and accurate estimation of lithium-ion batteries are worth obtaining, which has become the core issue of its energy and safety management. Lithium-ion batteries are composed of lithium cobalt oxide batteries with high energy density and closed-circuit voltage, and their safety is affected by their working conditions. SOE represents the energy that can be continuously released by lithium battery in the current state, and its unit is Wh, which 180504545 the most basic and important key parameter of battery management system. In addition, the charging and discharging process of lithium-ion batteries includes complex electrical energy, chemical energy and thermal energy conversion, and overcharge and overdischarge are easy to cause safety accidents. Accurate SOE estimation plays an important role in preventing overcharge and overdischarge. In the application of lithium-ion batteries, its safety is still the most concerned issue, and SOE estimation is the basis and premise of its safe use. The lithium-ion battery pack adopts the cascade structure of battery cells, which meets the capacity and voltage requirements in the process of auxiliary power supply. However, due to the inevitable differences in materials and processes, the inconsistency between monomers exists objectively and cannot be avoided; Moreover, this phenomenon will become more and more obvious with the increase of the number of cycles, which makes the expression and correction of inconsistency between monomers an important part of group SOE estimation, and also brings great challenges to the accurate estimation of group SOE.
In view of the necessity and urgency of SOE estimation, relevant research institutions and universities, such as MIT, Penn State University, University of South
Carolina, Leeds University, Robert Gordon University, National Renewable Energy Office,
Leiden Energy Company, Infineon Technology Company, Tsinghua University, Beihang
University, Beijing Institute of Technology, Beijing Jiaotong University, Chongqing
University, China University of Science and Technology and Harbin Institute of
Technology, have launched SOE estimation.
At present, the estimation methods of SOE include integration method, open circuit voltage method, data-driven method and model-based method. Among them, data-driven methods include neural network algorithm, principal component analysis and support vector machine. Model-based methods include Kalman filter algorithm and particle filter algorithm. Data-driven methods often need a large number of accurate experimental data as training samples, which has a large amount of calculation, and the training results under different working conditions can only be used in the same situation, so the generalization ability is poor. The integral method is convenient to calculate, but it requires higher initial value of SOE and has poor anti-interference ability, and the errblJ504545 can not be repaired after it is caused by interference.
The purpose of the invention is to solve the problems in the prior art and provide an evaluation method of battery energy state based on adaptive feedback correction of forgetting factors.
The invention provides an evaluation method of battery energy state based on adaptive feedback correction of forgetting factors, comprising:
S1, acquiring model parameters of the lithium ion battery, and discretizing the model parameters to obtain discrete model parameters;
S2, estimating model parameters based on an adaptive forgetting factor recursive least squares algorithm and separating the model parameters to obtain separated parameters;
S3, obtaining a model error based on the discrete model parameters and the separation parameters;
S4, based on the model error, adopting a fuzzy logic controller to circularly execute
S2-S3, and adaptively adjusting the forgetting factor to obtain the final model parameters;
S5, estimating the energy state based on the final model parameters to obtain a final energy state prediction result.
Optionally, the formula for obtaining the model parameters of lithium-ion battery in
S1 and discretizing the model parameters is as follows:
Yea =Ue TU, =cl +o fi, + EU, +eu +e, (1), where Voc is the open circuit voltage; U, is the observed variable of working voltage output; “is the system input control variable; c1, c2, c3, c4 and c5 are corresponding constant coefficients; Feu is the difference of time equation of k+1;
subscript k is the k-th moment; subscript k+1 is the k+1-th moment; subscript k-1 is tH&J504545 k-1-moment; subscript k-2 is the k-2-moment.
Optionally, a formula for estimating model parameters based on an adaptive forgetting factor recursive least squares algorithm and separating the model parameters to obtain separated parameters includes:
Y,=h'0+v, à =[Y Yes Ur Ups Wu] (2), 0 =|c, c, cc, Cs] ln = 1 Ly + Bh (3), wherein I is the difference of equation at time k, 9 is the variable of the system to be identified, and 6 is the estimated value of the system variable to be identified; Vi is the observation noise of the system at time k, that is, the noise matrix; EB is the covariance matrix of prediction error at time k; T is the sampling time of voltage and current of power lithium ion battery; ” is the forgetting factor; # is the parameter matrix; subscript k is the k-th moment; subscript k+1 is the k+1-th moment; subscript k-1 is the k-1-moment; subscript k-2 is the k-2-moment.
Optionally, the process of energy state estimation based on the final separation parameters includes: performing prior estimation based on the final model parameters to obtain an initial prediction value; calculating a Kalman gain: performing posterior estimation and correction on the initial prediction value based on the Kalman gain to obtain a final energy state prediction result.
Optionally, performing prior estimation based on the final model parameters tdJ504545 obtain an initial prediction value includes: based on Kirchhoff voltage law and Kirchhoff current law, obtaining the energy state equation of lithium-ion battery; obtaining a prediction equation based on the lithium-ion battery energy state observation equation and the final model parameters; performing prior estimation based on the prediction equation to obtain an initial prediction value.
Optionally, the calculation formula of the Kalman gain is as follows: -1
Ki. = pC" (cp. C” + Rı) (4), wherein Len is the covariance matrix of the prediction error at k+1; Kin is the
Kalman gain at k+1; C is the system observation matrix; T is the sampling time of voltage and current of power lithium ion battery; Rı is the observed covariance at time k; subscript k is the kth moment; the subscript k+1 is the k+1-moment.
Optionally, performing posterior estimation and correction on the initial prediction value based on the Kalman gain to obtain a final energy state prediction result comprises:
BL =(E-K, OW, y, Ae Heed A RE (5) wherein "*+1 is the prediction error of observed variables at time k+1; Ay is the predictor of state variables at time k+1; 1x is the state variable at k+1 based on the prediction of the state variable at K; uk+1 is the system input control variable at time k+1;
C and D are system observation matrices; Kk+1 is the Kalman gain at k+1; Fe is k+1 3 moment error covariance matrix; Fons is the k+1 moment error covariance matrix predicted at k moment; E is identity matrix; Subscript k is the kth moment; the subscript/504545 k+1 indicates the k+1st moment; Yin is the observed variable at k+1.
The invention has the following technical effects.
In the existing BMS application of lithium-ion batteries, the SOE estimation method based on watt-hour integration and open circuit voltage can not accurately represent the accumulated error in SOE estimation, and can not be combined with the current state for parameter correction; through the analysis of the existing SOE estimation methods, the
SOE estimation of lithium batteries based on AFFRLS (Forgetting Factor Recursive
Least Squares) and EKF (Extended Kalman Filter Algorithm) is studied. The closed-circuit voltage and current are taken as real-time input parameters, and the working condition information of lithium-ion batteries is considered in the SOE estimation process, which overcomes the shortcomings of large error and gradual accumulation caused by insufficient real-time correction of traditional SOE estimation methods.
Aiming at the problem of characterization of polarization characteristics of lithium batteries, the invention constructs a second-order RC equivalent circuit model of lithium-ion batteries; Aiming at the problem that the online parameter identification method of FFRLS can not adapt well to the change of working conditions, a fuzzy logic controller is proposed to realize online adaptive tuning of forgetting factor. On the basis of battery equivalent circuit model, the establishment of SOE estimation model and mathematical iterative operation of SOE value of lithium ion battery pack are realized by using AFFRLS and EKF algorithms. The research on SOE estimation of lithium batteries based on AFFRLS and EKF is put forward, and the SOE estimation model is constructed and verified by experiments.
BRIEF DESCRIPTION OF THE FIGURES LU504545
In order to explain the embodiments of the present invention or the technical scheme in the prior art more clearly, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For ordinary people in the field, other drawings can be obtained according to these drawings without paying creative labor.
Fig. 1 is a schematic structural diagram of SOE estimation model for lithium ion batteries in an embodiment of the present invention;
Fig. 2 shows the adaptive tuning of forgetting factor by using fuzzy logic controller according to model error in the embodiment of the present invention;
Fig. 3 is a diagram showing the results of SOE estimation of lithium ion batteries using different methods in the embodiment of the present invention;
Fig. 4 is an error chart of SOE estimation for lithium ion batteries using different methods in the embodiment of the present invention.
In the following, the technical scheme in the embodiment of the invention will be clearly and completely described with reference to the attached drawings. Obviously, the described embodiment is only a part of the embodiment of the invention, but not the whole embodiment. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in the field without creative labor belong to the scope of protection of the present invention.
Example 1
As shown in Fig. 1, the embodiment discloses an evaluation method of battery energy state based on adaptive feedback correction of forgetting factors, including the following steps:
S1, identifying parameters of a lithium ion battery model on line, and discretizing the lithium ion battery model;
S2, estimating parameters and separating the parameters by adopting an adaptiveJ504545 forgetting factor recursive least square algorithm;
S3, using a fuzzy logic controller to realize the adaptive tuning of the forgetting factor according to the model error;
S4, according to the separated parameters, calculating the one-step prediction of state space variables and its variance matrix, and directly obtaining the following prediction equation according to the state equation, namely performing prior estimation;
S5, calculating an expression of the Kalman gain K(k);
S6, correcting the initial prediction value and updating the process noise according to the Kalman gain K(k); the SOE, polarization voltage and system residual at this moment are obtained, that is, posterior estimation is carried out.
As a preferred technical scheme, in S1, the method comprises the following steps: using the difference equation of the following formula (1) to obtain the discrete difference equation of the equivalent model of the lithium ion battery, so as to facilitate the parameter identification of the model;
Y =U, -U,=¢Y, +c) ,+cu, +cu, +cu, , (1), where Voc is the open circuit voltage; U, is the observed variable of working voltage output; “is the system input control variable; c1, c2, c3, c4 and c5 are corresponding constant coefficients; Feu is the difference of time equation of k+1; subscript k is the k-th moment; subscript k+1 is the k+1-th moment; subscript k-1 is the k-1-moment; subscript k-2 is the k-2-moment.
As a preferred technical scheme, as shown in FIG. 2, in S2, the method is as follows: using fuzzy logic adaptive forgetting factor recursive least squares algorithm to realize adaptive online tuning calculation of lithium ion battery model parameters;
Y.=h'0+v, à =[Y Yes Ur Ups Wu] (2), 0=|c, c, cc, Cs]
= 0, + Feafifia | Ye a Laß | 7504545
Feu =H ly +h corti 1 : (3), wherein I is the difference of equation at time k, 9 is the variable of the system to be identified, and O is the estimated value of the system variable to be identified; Vi is the observation noise of the system at time k, that is, the noise matrix; EB is the covariance matrix of prediction error at time k; T is the sampling time of voltage and current of power lithium ion battery; ” is the forgetting factor; h is the parameter matrix; subscript k is the k-th moment; subscript k+1 is the k+1-th moment; subscript k-1 is the k-1-moment; subscript k-2 is the k-2-moment.
As a preferred technical scheme, in step S4, the method is as follows: according to
Kirchhoff's voltage law and Kirchhoff's current law, the energy state observation equation of the lithium ion battery is written.
X(k+1) = A(K)X(k)+ Bau, +w,
Ve =CX + D, +4,
X(k+1) is the state matrix at k+1; A(k) is the state transition matrix at time k; X(k) is the state matrix at moment k; Bx is the system input control matrix at time k; Uk is the system input control variable at time k; Yk is the observed variable at time k; Ck and Dx are the system observation matrices at time k; wk is the system noise at time k; Vk is the observed noise at time k;
As a preferred technical scheme, in step S5, the method comprises the following steps: -1
Ki. = pC" (cp. C” + Rı) (4), wherein Len is the covariance matrix of the prediction error at k+1; Kin is the
Kalman gain at k+1; C is the system observation matrix; T is the sampling time of voltage and current of power lithium ion battery; Rı is the observed covariance at time k; subscript k is the kth moment; the subscript k+1 is the k+1-moment.
As a preferred technical scheme, in S6, the method comprises the following stepsU504545
Vea = Vea Cx + Du)
Ve F Xe Ka Pos wherein Ye is the prediction error of observed variables at time k+1; Vist is the predictor of state variables at time k+1; 1x is the state variable at k+1 based on the prediction of the state variable at K; uk+1 is the system input control variable at time k+1;
C and D are system observation matrices; Kk+1 is the Kalman gain at k+1; fax is k+1 > moment error covariance matrix; Fie is the k+1 moment error covariance matrix predicted at k moment; E is identity matrix; Subscript k is the kth moment; the subscript k+1 indicates the k+1st moment; Yin is the observed variable at k+1.
As shown in Fig. 3, it is the result diagram of SOE estimation of lithium-ion batteries using different methods in the embodiment of the present invention;
As shown in Fig. 4, it is an error chart of SOE estimation for lithium ion batteries using different methods in the embodiment of the present invention.
Aiming at the problem of SOE estimation when lithium-ion batteries are used in groups, the invention provides a battery energy state evaluation method with adaptive feedback correction of forgetting factors of lithium-ion batteries, and the effective characterization of SOE estimation of lithium-ion batteries is realized through intermittent aging degree determination and real-time calibration calculation processing; The battery energy state evaluation method based on adaptive feedback correction of forgetting factor obtains the mathematical expression of the influence of aging factors by calculating the influence coefficient of aging state on energy on the basis of normalized characterization of capacity. The battery energy state evaluation method based on adaptive feedback correction of forgetting factor is based on periodic measurement and calibration, and the functional relationship of superposition cycle number correction is obtained through synchronous acquisition and correction of the related values of rated energy state and cycle number. The battery energy state evaluation method based on adaptive feedback correction of forgetting factor is based on the correction calculation 6504545 aging influence coefficient and cycle times, and combined with the superposition calculation of the two factors, the calculation method of the influence correction of aging process on rated energy is obtained. On the basis of fully considering the group work of lithium-ion batteries and the establishment of SOE estimation, this method realizes the mathematical expression of the aging process characteristics of lithium-ion batteries, and constructs a battery energy state evaluation method based on adaptive feedback correction of forgetting factors.
The basic principle, main features and advantages of the present invention have been shown and described above. It should be understood by those skilled in the art that the present invention is not limited by the above-mentioned embodiments, and what is described in the above-mentioned embodiments and descriptions only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, there will be various changes and improvements in the present invention, which fall within the scope of the claimed invention. The scope of that present invention is defined by the appended claim and their equivalents.
Claims (7)
1. An evaluation method of battery energy state based on adaptive feedback correction of forgetting factors, comprising: S1, acquiring model parameters of the lithium ion battery, and discretizing the model parameters to obtain discrete model parameters; S2, estimating model parameters based on an adaptive forgetting factor recursive least squares algorithm and separating the model parameters to obtain separated parameters; S3, obtaining a model error based on the discrete model parameters and the separation parameters; S4, based on the model error, adopting a fuzzy logic controller to circularly execute S2-S3, and adaptively adjusting the forgetting factor to obtain the final model parameters; S5, estimating the energy state based on the final model parameters to obtain a final energy state prediction result.
2. The evaluation method of battery energy state based on adaptive feedback correction of forgetting factors according to claim 1, wherein the formula for obtaining the model parameters of lithium-ion battery in S1 and discretizing the model parameters is as follows: Y =U, -U,=¢Y, +c) ,+cu, +cu, +cu, , (1), where Voc is the open circuit voltage; U, is the observed variable of working voltage output; “is the system input control variable; c1, c2, c3, c4 and c5 are corresponding constant coefficients; Ya is the difference of time equation of k+1; subscript k is the k-th moment; subscript k+1 is the k+1-th moment; subscript k-1 is the k-1-moment; subscript k-2 is the k-2-moment.
3. The evaluation method of battery energy state based on adaptive feedbadk/504545 correction of forgetting factors according to claim 1, wherein a formula for estimating model parameters based on an adaptive forgetting factor recursive least squares algorithm and separating the model parameters to obtain separated parameters includes: Y,=h'0+v, à =[Y Yes Ur Ups Wu] (2), 0 =|c, c, cc, Cs] ln = 1 Ly + Bh (3), wherein I is the difference of equation at time k, 9 is the variable of the system to be identified, and 6 is the estimated value of the system variable to be identified; Vi is the observation noise of the system at time k, that is, the noise matrix; EB is the covariance matrix of prediction error at time k; T is the sampling time of voltage and current of power lithium ion battery; ” is the forgetting factor; # is the parameter matrix; subscript k is the k-th moment; subscript k+1 is the k+1-th moment; subscript k-1 is the k-1-moment; subscript k-2 is the k-2-moment.
4. The evaluation method of battery energy state based on adaptive feedback correction of forgetting factors according to claim 1, wherein the process of energy state estimation based on the final separation parameters includes: performing prior estimation based on the final model parameters to obtain an initial prediction value; calculating a Kalman gain: performing posterior estimation and correction on the initial prediction value based on the Kalman gain to obtain a final energy state prediction result.
5. The evaluation method of battery energy state based on adaptive feedbadk/504545 correction of forgetting factors according to claim 4, wherein performing prior estimation based on the final model parameters to obtain an initial prediction value includes: based on Kirchhoff voltage law and Kirchhoff current law, obtaining the energy state equation of lithium ion battery; obtaining a prediction equation based on the lithium ion battery energy state observation equation and the final model parameters; performing prior estimation based on the prediction equation to obtain an initial prediction value.
6. The evaluation method of battery energy state based on adaptive feedback correction of forgetting factors according to claim 4, wherein the calculation formula of the Kalman gain is as follows: -1
Ki. = pC" (cp. C” + Rı) (4), wherein Len is the covariance matrix of the prediction error at k+1; Kin is the Kalman gain at k+1; C is the system observation matrix; T is the sampling time of voltage and current of lithium ion battery; Rı is the observed covariance at time k; subscript k is the k-th moment; the subscript k+1 is the k+1-moment.
7. The evaluation method of battery energy state based on adaptive feedback correction of forgetting factors according to claim 6, wherein performing posterior estimation and correction on the initial prediction value based on the Kalman gain to obtain a final energy state prediction result comprises: Xe F Nr FR PL ={E-K, CP à Æ+i { KR } adi (5),
wherein Frew is the prediction error of observed variables at time k+1; Tyg is the 94545 predictor of state variables at time k+1; 1x is the state variable at k+1 based on the prediction of the state variable at K; uk+1 is the system input control variable at time k+1; C and D are system observation matrices; Kk+1 is the Kalman gain at k+1; Ls is error covariance matrix at k+1 moment; Ba is the k+1 moment error covariance matrix predicted at k moment; E is identity matrix; subscript k is the k-th moment; the subscript k+1 indicates the k+1st moment; Yin is the observed variable at k+1.
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