CN109633479B - Lithium battery SOC online estimation method based on embedded type volume Kalman filtering - Google Patents
Lithium battery SOC online estimation method based on embedded type volume Kalman filtering Download PDFInfo
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Abstract
The invention belongs to the field of battery management systems of electric automobiles, and discloses an embedded capacity Kalman filtering-based lithium battery SOC online estimation method, which comprises the steps of obtaining battery performance parameter information, establishing a second-order RC lithium battery equivalent circuit model, and constructing an equation for describing the dynamic characteristics of a lithium battery; identifying equivalent circuit model parameters and obtaining a function of OCV (open circuit voltage) with respect to SOC (state of charge); establishing an embedded type volume Kalman filtering observer; and collecting data such as real-time voltage and current of the lithium battery, and estimating the SOC. The method solves the problems that the spherical volume point of the traditional volume Kalman filtering algorithm possibly exceeds an integral area and the calculation process is complex, has the characteristics of high precision and good convergence, is a new practice of a novel algorithm in the field of lithium battery SOC estimation, and is suitable for the real-time SOC estimation of a power lithium battery management system platform.
Description
Technical Field
The invention belongs to the field of battery management systems of electric automobiles, and particularly relates to an on-line estimation method for the SOC of a lithium battery based on embedded volume Kalman filtering.
Background
Due to the rapid development of Electric Vehicles (EVs), lithium batteries as energy systems are widely used in the electric vehicle industry. The performance of the battery may affect the safety, reliability and efficiency of the electric vehicle, and thus a Battery Management System (BMS) plays a crucial role in monitoring the operation and providing the state of the lithium battery. State of charge (SOC) is one of the most critical parameters in BMS, and accurate SOC estimation can optimize management strategies for energy and battery balancing, reliability, safety, etc., specifically embodied as:
(1) and protecting the lithium battery. For the lithium battery which is charged and discharged circularly, the lithium battery is permanently damaged by overcharging and overdischarging, and the service life of the lithium battery is greatly shortened. Accurate SOC estimation, Vehicle Control Unit (VCU) will control lithium cell SOC at reasonable range, prevent overcharge and overdischarge the condition, prolong lithium cell life.
(2) The performance of the electric automobile is improved. The prepared SOC estimation value can enable the electric automobile to select an accurate control strategy, the performance of the lithium battery is fully exerted, and the performance of the whole automobile is improved.
In general, methods for estimating SOC can be classified into model and non-model methods. Non-model based methods are typically open-loop algorithms, and typically include ampere-hour integration methods, open-circuit voltage (OCV) methods, and artificial intelligence algorithms, among others.
The ampere-hour integration method highly depends on the precision of the sensor and the accuracy of the initial SOC; the open circuit voltage method is a simple method for determining SOC from the OCV-SOC relationship curve, but this method requires a long time to obtain OCV of the lithium battery for calculation and is not applicable when the battery is in an operating state; in addition, with the development of the technology, some artificial intelligence algorithms are also applied to the estimation of the SOC of the lithium battery, and the performance of the algorithms depends on a large amount of training data, but the data cannot represent all conditions of the operation of the lithium battery, and besides, the calculation amount of the algorithms is very large.
Model-based methods are generally based on a lithium battery model, and due to the complexity of electrochemical modeling, Equivalent Circuit Model (ECM) -based Kalman Filters (KFs), Extended Kalman Filters (EKFs), Unscented Kalman Filters (UKFs), and volumetric kalman filters (CKFs) have been widely used in SOC estimation, which are easier to implement than algorithms such as artificial intelligence. A sum has been proposed.
However, the process of linearizing a highly non-linear lithium battery system using a first order taylor equation based on EKF SOC estimation greatly reduces the accuracy of the estimation and leads to instability of the filtering. Although the UKF overcomes the error caused by EKF local linearization and avoids the inconvenience of solving the Jacobian matrix, the central point weight of the UKF in the process of Unscented Transformation (UT) is possibly negative, so that the numerical value of the UKF or UT transformation is unstable. The CKF is a nonlinear filter proposed in recent years, and has better numerical stability and higher filtering precision compared with the EKF and the UKF. However, CKF also has a problem that the spherical volume point may exceed the integration region and the calculation process is complicated.
In summary, the online SOC estimation method based on kalman filtering has the following problems:
the SOC estimation precision is not high, the convergence speed is not fast, and the stability of the filter is poor;
the linearization process reduces the estimation accuracy of the EKF;
the weight of the central point during the Unscented Transformation (UT) may be negative, resulting in unstable UKF filtering;
the CKF volume points may be complex, causing instability of the CKF filter, reducing filter accuracy, etc.
The significance of solving the technical problems is as follows:
in order to obtain the SOC online estimation method with high precision, good stability and high convergence rate, the invention provides the SOC online estimation method based on the embedded type volume Kalman filtering lithium battery. The method has the characteristics of stable estimation performance, high precision, strong robustness and strong white noise resistance, and is easy to realize.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an on-line estimation method for the SOC of a lithium battery based on embedded volume Kalman filtering. The method can obtain more stable and higher-precision filtering than the traditional CKF.
The technical scheme adopted by the invention is that the lithium battery SOC online estimation method based on the embedded type volume Kalman filtering comprises the following steps:
the method comprises the steps of firstly, obtaining performance parameter information of the lithium battery and determining an initial SOC value;
step two, establishing a second-order RC lithium battery equivalent circuit model, and calculating to obtain a state equation and an output equation of the lithium battery:
establishing a second-order RC equivalent circuit model of the lithium battery, comprising resistors R connected in series0、R1、R2Open circuit voltage UocCapacitor C1And a resistor R1Parallel connection, a capacitor C2And a resistor R2In parallel
Step three, identifying parameters of the equivalent circuit model and obtaining a function U of OCV relative to SOCoc；
The calibration curve of the battery OCV-SOC is obtained by fitting a fifth-order polynomial, wherein the fifth-order polynomial formula is as follows:
Uoc＝a0+a1*SOC+a2*SOC2+a3*SOC3+a4*SOC4+a5*SOC5
wherein, aiAnd (i ═ 0, 1., 5) is a polynomial coefficient, and the SOC is the state of remaining charge of the lithium battery.
And step four, establishing the embedded type volume Kalman observer by taking the SOC (state of charge) and the second-order polarization voltage of the lithium battery as a state equation of the system and taking an equivalent circuit model terminal voltage equation as a measurement equation. In general, the kalman filter observer includes the following state equations and measurement equations:
wherein x iskState vector, ykTo measure the vector, ukAs input variable, ωkIs the process noise, upsilon, with mean 0 and variance QkIs the measurement noise with mean 0 and variance R. The state equation of the embedded type volume Kalman observer is established by combining a lithium battery dynamic characteristic equation as follows:
the measurement equation is:
Ut,k＝Uoc,k+I0,kR0+U1,k+U2,k
wherein Q is the rated capacity of the lithium battery.
Further, the estimation process of the embedded volume kalman observer includes:
1) setting initial value of state equation, and setting process noise Q0Measuring noise R0Covariance value P of sum state error0；
2) Calculating and propagating embedded volume points;
further, the embedded volume point ξ is comparable to a traditional volume pointiThe calculation expression is as follows:
wherein, delta is a free parameter, and the estimation precision of the embedded type volume Kalman filtering can be improved by a proper value;
3) updating time and obtaining a state vector and a state error covariance at the current moment;
wherein, the state vector calculation expression is:
xi,k|k-1＝f(χi,k-1|k-1,uk-1)
compared with the traditional cubature Kalman filtering method, the method has the advantages that the weight value omega isiThe calculation expression is as follows:
where δ is a free parameter that determines the form of the embedded volume criterion and the accuracy of the filter.
4) Recalculating and transmitting the embedded volume points according to the state covariance in the step 3), wherein the expression is as follows;
5) obtaining a measurement equation vector and a measurement error covariance according to the embedded volume point updated in the step 4);
6) obtaining the covariance of the posterior error and the embedded Kalman gain of the volume at the current moment;
7) correcting the prior state vector by using the gain matrix, and updating the state value and the error covariance at the current moment;
8) repeating steps 1) to 7) with the updated state vector and error covariance to estimate the next time instant.
The invention also aims to provide a lithium battery SOC estimation control system based on embedded volume Kalman filtering, which is used for realizing the lithium battery SOC online estimation method based on embedded volume Kalman filtering.
In summary, the advantages and positive effects of the invention are:
the invention has the beneficial effects that: the invention relates to an embedded volume Kalman filtering algorithm-based lithium battery SOC online estimation method, which comprises the steps of obtaining battery performance parameter information and initializing a battery SOC value; establishing a second-order RC lithium battery equivalent circuit model, and establishing an equation for describing the dynamic characteristics of the lithium battery; identifying equivalent circuit model parameters and obtaining OCV function U related to SOCoc(ii) a Establishing an embedded type volume Kalman filter observer by taking the SOC and the second-order polarization voltage of a lithium battery as a state equation of a system and taking the terminal voltage of the lithium battery as a measurement equation of the system; and collecting data such as real-time voltage and current of the lithium battery, and estimating the SOC.
The method utilizes the embedded volume criterion and the gamma function property to obtain a filter with better traditional CKF, solves the problems that the spherical volume point possibly exceeds an integral area and the calculation process is complex in the traditional volume Kalman filtering algorithm, can quickly converge and obtain an accurate SOC estimation value, and is easy to realize.
Drawings
Fig. 1 is a flow chart of an embedded volume kalman filter-based online estimation method for SOC of a lithium battery according to an embodiment of the present invention.
FIG. 2 is a schematic diagram of an equivalent circuit model of a lithium battery of the lithium battery SOC online estimation method based on embedded volume Kalman filtering, provided by the embodiment of the invention, including an open-circuit voltage U of the batteryOC(SOC), internal resistance of Battery R0Polarization resistance R in second order RC ring1、R2And a polarization capacitor C1、C2。
Fig. 3 is a comparison diagram of SOC estimation errors of an embodiment of the embedded volume kalman filter-based online estimation method for SOC of a lithium battery according to the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The SOC estimation precision is not high, the convergence speed is not fast, and the stability of the filter is poor;
the linearization process reduces the estimation accuracy of the EKF;
the weight of the central point during the Unscented Transformation (UT) may be negative, resulting in unstable UKF filtering;
the CKF volume points may be complex, causing instability of the CKF filter and reducing the precision of the filter
In order to solve the above problems, the present invention will be described in detail with reference to the following embodiments.
As shown in fig. 1, the method for online estimating the SOC of the lithium battery based on the embedded volume kalman filter algorithm provided by the embodiment of the present invention includes the following steps:
the method comprises the steps of firstly, obtaining performance parameter information of the lithium battery and determining an initial SOC value;
step two, establishing a second-order RC lithium battery equivalent circuit model, and calculating to obtain a state equation and an output equation of the battery:
step three, identifying parameters of the equivalent circuit model and obtaining a function U of OCV relative to SOCoc；
And step four, establishing the embedded type volume Kalman observer by taking the SOC (state of charge) and the second-order polarization voltage of the lithium battery as a state equation of the system and taking an equivalent circuit model terminal voltage equation as a measurement equation.
And fifthly, collecting data such as real-time voltage and current of the lithium battery, and estimating the SOC on line.
As a preferred embodiment of the present invention, the first step includes:
using the function U from the first acquired voltage dataocAnd the obtained SOC value is used as an initial value of the SOC estimation of the lithium battery.
In the embodiment of the invention, reference is made to fig. 2, which is an equivalent circuit model diagram of a specific embodiment of an embedded volume kalman filtering-based lithium battery SOC online estimation method of the invention.
In the second step, a lithium battery equivalent circuit model is established, and the circuit model is composed of a voltage source, an ohmic ammeter, a polarization resistor and a polarization capacitor to simulate the dynamic characteristics of the lithium battery, including the ohmic resistor R connected in series0Polarization resistance R1And R2Open circuit voltage UocAnd a polarization resistance R1Parallel polarization capacitor C1And a polarization resistance R2Parallel polarization capacitor C2. Then, obtaining a lithium battery dynamic characteristic equation according to kirchhoff's theorem:
where Δ t is the sampling time interval, I0To flow through ohmic resistance R0The current of (2).
As a preferred embodiment of the present invention, step three includes the following steps:
the lithium battery model parameter online identification method adopts a least square algorithm identification method based on forgetting factors.
Obtaining a calibration curve of the OCV-SOC of the battery, and fitting by utilizing a fifth-order polynomial, wherein the fifth-order polynomial formula is as follows:
Uoc＝a0+a1*SOC+a2*SOC2+a3*SOC3+a4*SOC4+a5*SOC5
wherein, UocIs an open circuit voltage, aiAnd (i ═ 0, 1., 5) is a polynomial coefficient, and the SOC is the state of remaining charge of the lithium battery.
As the preferred embodiment of the invention, an embedded type volume Kalman filtering observer is established in the fourth step, and the derivation process is as follows:
according to the embedded volume criterion, the third-order volume kalman expression is:
where 0 and I represent density matrices of zero and one, respectively, and δ is a free parameter (taking value in this embodiment)
) Which determines the embedded volume criterion form, in which the embedded volume point xiiAnd weight ωiThe calculation expression is as follows:
and (4) placing the embedded volume points and the weight under the framework of the traditional Kalman filtering to obtain the embedded volume Kalman filtering. In general, the kalman filter observer includes the following state equations and measurement equations:
wherein x iskState vector, ykTo measure the vector, ukAs input variable, ωkIs the process noise, upsilon, with mean 0 and variance QkIs the measurement noise with mean 0 and variance R.
As a preferred embodiment of the present invention, in combination with the lithium battery dynamic characteristic equation established in step two, the lithium battery SOC and the second-order polarization voltage are used as a state equation of the system, the equivalent circuit model terminal voltage equation is used as a measurement equation, and the state equation of the embedded volume kalman observer is established as follows:
the measurement equation is:
Ut,k＝Uoc,k+I0,kR0+U1,k+U2,k
wherein Q is the rated capacity of the lithium battery.
In the embodiment of the invention, the estimation of the SOC of the battery based on the embedded type cubature Kalman filtering method comprises the following steps:
1) setting initial value of state equation, and setting process noise Q0Measuring noise R0Covariance value P of sum state error0；
2) Calculating and propagating embedded volume points, wherein the expression is as follows:
the embedded volume point xiiThe method is characterized in that:
wherein, delta is a free parameter, and the estimation precision of the embedded type volume Kalman filtering can be improved by a proper value;
Sk-1|k-1is the Cholesky decomposition of the covariance of the state error, Sk-1|k-1＝chol(Pk-1)。
3) Updating time and obtaining a state vector and a state error covariance at the current moment, wherein the expression of the state vector at the current moment is as follows:
xi,k|k-1＝f(χi,k-1|k-1,uk-1)
the state error covariance expression is:
the calculation expression wherein the weight value ω isiThe calculation expression is as follows:
4) recalculating and propagating embedded volume points according to the state covariance in the step 3), wherein the expression is as follows:
Sk|k-1is the Cholesky decomposition of the covariance of the state error, Sk|k-1＝chol(Pk)
5) Obtaining a measurement equation vector and a measurement error covariance according to the embedded volume points updated in the step 4), wherein the calculation expression of the measurement equation vector is as follows:
the measurement error covariance calculation expression is:
6) obtaining the covariance of the posterior error and the embedded Kalman gain of the volume at the current moment, wherein the calculation expression of the covariance of the posterior error is as follows:
the gain calculation expression is:
7) correcting the prior state vector by using the gain matrix, and updating the state value and the error covariance at the current moment; wherein, the state vector update expression is:
the error covariance update expression is:
8) repeating steps 1) to 7) with the updated state vector and error covariance to estimate the next time instant.
Fig. 3 is a comparison diagram of estimation errors of a lithium battery SOC according to an embodiment of the online estimation method of a lithium battery SOC based on embedded volumetric kalman filtering according to the preferred embodiment of the present invention, including comparison results with EKF, UKF, and CKF for online estimation. The result shows that the invention provides the power lithium battery SOC on-line estimation algorithm which has high precision and fast convergence and is suitable for the battery management system platform, the SOC of the lithium battery can be estimated in real time by acquiring the voltage and current information of the lithium battery in real time, and the invention is a new practice applying a novel algorithm.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (5)
1. The lithium battery SOC online estimation method based on embedded type volume Kalman filtering is characterized by comprising the following steps:
acquiring performance parameter information of a lithium battery, and determining an initial SOC value;
step two, establishing a second-order RC lithium battery equivalent circuit model, calculating to obtain a state equation and an output equation of the lithium battery, and establishing the second-order RC lithium battery equivalent circuit model comprising resistors R connected in series0、R1、R2Open circuit voltage UocCapacitor C1And a resistor R1Parallel connection, a capacitor C2And a resistor R2Parallel connection;
identifying parameters of the equivalent circuit model and obtaining a function U of OCV relative to SOCoc；
Step four, establishing an embedded volume Kalman observer by taking the SOC (state of charge) and the second-order polarization voltage of the lithium battery as a state equation of the system and taking an equivalent circuit model terminal voltage equation as a measurement equation;
in the third step, a calibration curve of the battery OCV-SOC is obtained, and fitting is carried out by utilizing a fifth-order polynomial, wherein the fifth-order polynomial formula is as follows:
Uoc＝a0+a1*SOC+a2*SOC2+a3*SOC3+a4*SOC4+a5*SOC5
wherein, ai(i ═ 0, 1., 5) is a polynomial coefficient, and SOC is the state of remaining charge of the lithium battery;
in the fourth step, the state equation of the embedded type volume Kalman observer is established by combining the dynamic characteristic equation of the lithium battery as follows:
the measurement equation is:
Ut,k＝Uoc,k+I0,kR0+U1,k+U2,k
wherein Q is the rated capacity of the lithium battery.
2. The embedded capacity Kalman filtering based lithium battery SOC online estimation method of claim 1, characterized in that the embedded capacity Kalman filtering based lithium battery SOC online estimation method further comprises:
1) setting an initial value of a state equation, and setting covariance values of process noise, measurement noise and state errors;
2) calculating and propagating embedded volume points;
3) updating time and obtaining a state vector and a state error covariance at the current moment;
4) recalculating and transmitting the embedded volume points according to the state covariance in the step 3);
5) obtaining a measurement equation vector and a measurement error covariance according to the embedded volume point updated in the step 4);
6) obtaining the covariance of the posterior error and the embedded Kalman gain of the volume at the current moment;
7) correcting the prior state vector by using the gain matrix, and updating the state value and the error covariance at the current moment;
8) repeating steps 1) to 7) with the updated state vector and error covariance to estimate the next time instant.
3. The embedded capacity Kalman filtering based lithium battery SOC online estimation method of claim 2, characterized in that in the step 2), the embedded capacity point is ξiThe calculation expression is as follows:
where δ is a free parameter.
4. The embedded capacity Kalman filtering based lithium battery SOC online estimation method of claim 2, characterized in that in step 3), the state vector calculation expression is:
xi,k|k-1＝f(χi,k-1|k-1,uk-1)
5. the lithium battery SOC estimation control system based on the embedded volume Kalman filtering is used for realizing the lithium battery SOC online estimation method based on the embedded volume Kalman filtering according to claim 1.
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| CN110196393B (en) * | 2019-05-31 | 2024-03-12 | 中国矿业大学 | Combined on-line estimation method for lithium battery charge state, energy state and power state |
| CN110824363B (en) * | 2019-10-21 | 2022-01-11 | 江苏大学 | Lithium battery SOC and SOE joint estimation method based on improved CKF |
| CN111707953A (en) * | 2019-11-24 | 2020-09-25 | 华南理工大学 | Lithium battery SOC online estimation method based on backward smoothing filtering framework |
| CN112858928B (en) * | 2021-03-08 | 2024-02-06 | 安徽理工大学 | Lithium battery SOC estimation method based on online parameter identification |
| CN113125962A (en) * | 2021-04-21 | 2021-07-16 | 东北大学 | Lithium titanate battery state estimation method under temperature and time variation |
| CN115327385A (en) * | 2022-07-29 | 2022-11-11 | 武汉理工大学 | Power battery SOC value estimation method and system |
| CN115902667B (en) * | 2023-02-15 | 2023-05-23 | 广东电网有限责任公司东莞供电局 | Lithium battery SOC estimation method based on weight and volume point self-adaption |
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