CN105093114A - Battery online modeling and state of charge combined estimating method and system - Google Patents
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Abstract
The invention relates to a battery online modeling and charge state combined estimating method and system. The method comprises the steps of: utilizing a threshold model to carry out sectional linearization on a non-linear relation between an open circuit voltage OCV and a state of charge SOC contained in a battery model, and mapping the non-linear relation to a sectional linear relation between a battery end voltage and the state of charge SOC; only by using battery end voltage and current data obtained by means of online measurement, establishing an autoregressive moving average model in each sectional interval; converting the autoregressive moving average model into a battery model described by a corresponding state space, constructing a state observer, and estimating the state of charge serving as a state variable; and sliding a time window, and collecting a next group of battery end voltage and current data for calculation. By adopting the method provided by the invention, relatively high precision is realized for online estimation of the model parameter and the state of charge of the lithium ion battery at any time, and the method is liable to be realized.
Description
Technical Field
The invention relates to a joint estimation method and a joint estimation system for battery online modeling and state of charge, and belongs to the technical field of lithium ion battery management.
Background
In order to solve the problems of energy safety and environmental pollution, electric vehicles have been rapidly developed under the promotion of governments and automobile manufacturers in various countries in recent years. As a main energy carrier and power source of an electric vehicle, a battery and a management system thereof are one of the most core technologies of the electric vehicle. Among them, the lithium ion battery is widely used due to its advantages of high energy ratio, low self-discharge rate, no memory effect, high working voltage platform, long service life, low manufacturing cost, etc. And in cooperation with the system, a lithium ion power Battery Management System (BMS) is also widely regarded, researched and applied.
The core function of the BMS is to effectively manage and control the operational state of the battery by accurately tracking the dynamic behavior of the battery, which requires that a mathematical model that accurately describes the dynamic behavior of the battery must be established. From the viewpoint of economic, safe and reasonable use of the power battery for the electric automobile, the estimation of the state of charge (SOC) of the power battery by using the battery model parameters is more critical. In recent years, there are many battery model identification methods accompanying the development of batteries.
In the commercial application process of the electric automobile, the over-high price of the battery is the main reason for hindering the rapid popularization of the battery, and people fully utilize the electric quantity of the battery and reduce the cost of the battery by searching a better battery grouping mode. The grouping mode of the batteries is mainly related to the consistency of the batteries, and the grouping mode mainly depends on the identification efficiency of battery parameters and the improvement of SOC estimation precision, so that the reasonable utilization of the batteries is facilitated, and the practical service life of the batteries is prolonged. Therefore, it is necessary to find a method for obtaining the battery parameters and SOC accurately, quickly and online. The invention provides a lithium ion power battery modeling and SOC joint estimation method, which is a method for meeting the requirements.
Disclosure of Invention
The invention aims to solve the technical problem that the prior art is insufficient, and provides a battery online modeling and SOC combined estimation method and system, which are used for simultaneously acquiring battery parameters and SOC and realizing accurate, rapid and online identification of the battery parameters and accurate estimation of the SOC.
The technical scheme for solving the technical problems is as follows: a joint estimation method for battery online modeling and state of charge specifically comprises the following steps:
step 1: acquiring battery end voltage value data and battery end current value data in a current time window;
step 2: performing value domain division according to different voltage value data to obtain a plurality of segmented intervals, establishing an autoregressive moving average model for each segmented interval, converting the autoregressive moving average model into a battery model, and identifying parameters of the battery model;
and step 3: constructing a state observer, estimating the state of charge (SOC) serving as a state variable to obtain an estimated value of the SOC;
and 4, step 4: judging whether a time window without data acquisition exists, if so, acquiring a next time window in a sliding manner, taking the acquired time window as a current time window, and executing the step 1; otherwise, executing step 5;
and 5: and completing on-line modeling and state of charge estimation of a battery model of the lithium ion battery.
The time window used in the invention is a fixed time window, taking 1-time data acquisition in 1 second as an example, and 500 data acquisition points are taken as the width of the time window, but the time window is not limited to the fixed time window under the condition of ensuring the validity of the established model.
The invention has the beneficial effects that: the method is used for simultaneously acquiring the battery parameters and the SOC and realizing accurate, rapid and online identification of the battery parameters and accurate estimation of the SOC; the method can be used for online measuring the model parameters and the charge state of the lithium ion battery at any time with high precision, and is easy to realize.
On the basis of the technical scheme, the invention can be further improved as follows.
Further, the step 2 specifically includes the following steps:
step 2.1: performing value domain division according to different voltage value data to obtain a plurality of sectional intervals, and establishing an autoregressive moving average model for each sectional interval by using battery end voltage value data and battery end current value data obtained by online measurement;
step 2.2: and converting the autoregressive moving average model into a battery model described by a corresponding state space, and identifying parameters of the battery model.
Further, the step 3 specifically includes the following steps:
step 3.1: the nonlinear relation between the open-circuit voltage OCV and the state of charge SOC contained in the battery model is subjected to piecewise linearization by using a threshold model, and can be mapped into the piecewise linearization relation between the battery terminal voltage and the state of charge SOC;
step 3.2: and constructing a state observer according to the linear relation in the battery model, and estimating the state of charge (SOC) serving as a state variable to obtain an estimated value of the SOC.
Further, the key of piecewise linearization of the nonlinear relationship between the open-circuit voltage OCV and the state of charge SOC included in the battery model by using the threshold model is to determine the battery model parameters according to the linearized model parameters of the open-circuit voltage OCV and the state of charge SOC of the lithium ion battery.
Further, the determination of the next time window may scale the length of the time window according to the nonlinear strength.
The technical scheme for solving the technical problems is as follows: a combined estimation system for on-line modeling and state of charge of a battery comprises an acquisition module, a battery model module, a state variable estimation module and a judgment module;
the acquisition module is used for acquiring battery end voltage value data and battery end current value data in a current time window;
the battery model module is used for carrying out value domain division according to different voltage value data to obtain a plurality of segmented intervals, establishing an autoregressive moving average model for each segmented interval, converting the autoregressive moving average model into a battery model and identifying parameters of the battery model;
the estimation module is used for constructing a state observer and estimating the state of charge (SOC) serving as a state variable to obtain an estimated value of the SOC;
the judging module is used for judging whether a time window without data acquisition exists, if so, the next time window is obtained in a sliding mode, the obtained time window is used as the current time window, and the voltage and current data of the next group of batteries are acquired to participate in calculation; otherwise, completing the on-line modeling and the state of charge estimation of the battery model of the lithium ion battery.
The invention has the beneficial effects that: the method is used for simultaneously acquiring the battery parameters and the SOC and realizing accurate, rapid and online identification of the battery parameters and accurate estimation of the SOC; the method can be used for online measuring the model parameters and the charge state of the lithium ion battery at any time with high precision, and is easy to realize.
On the basis of the technical scheme, the invention can be further improved as follows.
Further, the battery model module comprises a modeling module and a model conversion module;
the modeling module is used for carrying out value domain division according to different voltage value data to obtain a plurality of sectional intervals, and an autoregressive moving average model is established for each sectional interval by using the battery end voltage value data and the battery end current value data obtained by online measurement;
the model conversion module converts the autoregressive moving average model into a corresponding battery model described by a state space, and identifies parameters of the battery model.
Further, the state variable estimation module comprises a linearization module and an estimation value module;
the linearization module is used for carrying out piecewise linearization on the nonlinear relation between the open-circuit voltage OCV and the state of charge SOC contained in the battery model by utilizing the threshold model, and can be mapped into the piecewise linearization relation between the battery terminal voltage and the state of charge SOC;
and the estimation value module is used for constructing a state observer according to the linear relation in the battery model, estimating the state of charge (SOC) serving as a state variable and obtaining an estimation value of the SOC.
Further, the key of piecewise linearization of the nonlinear relationship between the open-circuit voltage OCV and the state of charge SOC included in the battery model by using the threshold model is to determine the battery model parameters according to the linearized model parameters of the open-circuit voltage OCV and the state of charge SOC of the lithium ion battery.
Further, the determination of the next time window may scale the length of the time window according to the nonlinear strength.
Drawings
FIG. 1 is a flow chart of a method for jointly estimating the on-line modeling and the state of charge of a battery according to the present invention;
FIG. 2 is a schematic diagram of a lithium ion power battery modeling and SOC joint estimation method according to the present invention;
FIG. 3 is a diagram of an equivalent circuit of a battery model according to the present invention;
FIG. 4 shows the SOC estimation result under the FUDS condition according to the present invention;
FIG. 5 shows SOC estimation errors under FUDS operating conditions according to the present invention;
fig. 6 is a structural block diagram of a joint estimation system for on-line modeling and state of charge of a battery according to the present invention.
In the drawings, the components represented by the respective reference numerals are listed below:
1. the device comprises an acquisition module, a battery model module, a state variable estimation module, a judgment module, a modeling module, a model conversion module, a linearization module, a model conversion module, a state variable estimation module and an estimation value estimation module, wherein the acquisition module 2, the battery.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1, the method for jointly estimating the on-line modeling and the state of charge of the battery according to the present invention specifically includes the following steps:
step 1: acquiring battery end voltage value data and battery end current value data in a current time window;
step 2: performing value domain division according to different voltage value data to obtain a plurality of segmented intervals, establishing an autoregressive moving average model for each segmented interval, converting the autoregressive moving average model into a battery model, and identifying parameters of the battery model;
and step 3: constructing a state observer, estimating the state of charge (SOC) serving as a state variable to obtain an estimated value of the SOC;
and 4, step 4: judging whether a time window without data acquisition exists, if so, acquiring a next time window in a sliding manner, taking the acquired time window as a current time window, and executing the step 1; otherwise, executing step 5;
and 5: and completing on-line modeling and state of charge estimation of a battery model of the lithium ion battery.
Fig. 2 is a schematic diagram of a lithium ion power battery modeling and SOC joint estimation method. As shown in fig. 2, the joint estimation method for on-line modeling and state of charge of a battery specifically includes the following steps:
step 1: and (3) carrying out piecewise linearization on the nonlinear relation between the open-circuit voltage OCV and the state of charge SOC contained in the battery model by using a threshold model, and mapping the nonlinear relation into the piecewise linearization relation between the battery terminal voltage and the state of charge SOC. The relation function between the Open Circuit Voltage (OCV) of the lithium ion power battery and the state of charge (SOC) of the lithium ion battery is as follows: vocF (·) is a nonlinear relationship between the open-circuit voltage OCV and the state of charge SOC of the battery. It can be piecewise linear approximated by a threshold model, i.e.,
λ1,…,λkis a constant coefficient of r1,…,rk-1A constant represents the threshold value, and k is the number of segmented regions. The piecewise linear relationship of mapped battery terminal voltage to state of charge SOC may be expressed as,
v and I respectively represent the measured cellsTerminal voltage and current data, Vp1Is a first set of polarization voltages, V, in a lithium ion batteryp2For a second set of polarization voltages, R, in the lithium ion battery0Is the load resistance in the lithium ion battery in response to current changes.
Step 2: and in a selected time window, performing value domain division on the battery terminal voltage obtained by online measurement, and establishing an ARMA model in each subsection interval by only using the battery terminal voltage and current data obtained by online measurement. Can be expressed as a number of times,
Vtand ItRespectively representing the measured battery terminal voltage and current data time series; phi is aj,iAnd thetal,iCoefficients of the ARMA model built in the ith segmentation region are respectively expressed, wherein j and l are respectively expressed as the order of the model, and i is 1, … k; phi is a0,1,…,φ0,kConstant terms of the established ARMA model; e.g. of the typet,kIndicating the prediction error.
And step 3: and converting the ARMA model into a battery model described by a corresponding state space, and identifying parameters of the battery model. The present embodiment utilizes a battery model equivalent circuit as shown in fig. 3. Establishing a state space equation equivalent to the ARMA model of each segment area, establishing a coefficient solution equation, and expressing a state space expression capable of establishing an electrical circuit model of the battery for the ith segment area as
state X ═ SOCV of lithium ion batteryp1Vp2]T. The following solving equation exists between the ARMA model and the continuous system
And 4, step 4: a state observer is constructed to estimate the state of charge as a state variable. Equation of state of lithium ion
Wherein L is ═ 2 [, ]L1L2L3]T，L1Gain coefficient of error feedback quantity of first order derivative of charge state of lithium ion battery; l is2The gain coefficient is the error feedback quantity of the first-order derivative of the polarization voltage of the first group of the lithium ion batteries; l is3The gain coefficient is the error feedback quantity of the first derivative of the second group of polarization voltage of the lithium ion battery; v is terminal voltage of lithium ion batteryAn actual value;is an estimate of the terminal voltage of the lithium ion battery. The gain coefficients can be solved using a pole placement method or a linear quadratic method.
And 5: and sliding a time window, and collecting the voltage and current data of the next group of batteries to participate in calculation.
Selecting 1 segment of LiMn2O4The nominal voltage of the battery cell is 3.6V, and the nominal capacity of the battery cell is 15 Ah. Under the FUDS working condition, the battery model parameters are obtained by the method, the SOC estimation result is obtained and shown in figure 4, and the estimation error is shown in figure 5.
As shown in fig. 6, the system for jointly estimating the on-line modeling and the state of charge of the battery according to the present invention includes an acquisition module 1, a battery model module 2, a state variable estimation module 3, and a determination module 4;
the acquisition module 1 is used for acquiring battery end voltage value data and battery end current value data in a current time window;
the battery model module 2 is used for carrying out value domain division according to different voltage value data to obtain a plurality of segmented intervals, establishing an autoregressive moving average model for each segmented interval, converting the autoregressive moving average model into a battery model and identifying parameters of the battery model;
the estimation module 3 is used for constructing a state observer and estimating the state of charge (SOC) serving as a state variable to obtain an estimation value of the state of charge (SOC);
the judging module 4 is used for judging whether a time window without data acquisition exists, if so, acquiring a next time window in a sliding manner, taking the acquired time window as a current time window, and acquiring the voltage and current data of the next group of batteries to participate in calculation; otherwise, completing the on-line modeling and the state of charge estimation of the battery model of the lithium ion battery.
The battery model module 2 comprises a modeling module 21 and a model conversion module 22;
the modeling module 21 is configured to perform value domain division according to different voltage value data to obtain a plurality of segment intervals, and establish an autoregressive moving average model for each segment interval by using battery end voltage value data and battery end current value data obtained by online measurement;
the model conversion module 22 converts the autoregressive moving average model into a corresponding battery model described in a state space, and identifies battery model parameters.
The state variable estimation module 3 comprises a linearization module 31 and an estimation value module 32;
the linearization module 31 is configured to perform piecewise linearization on the nonlinear relationship between the open-circuit voltage OCV and the state of charge SOC included in the battery model by using a threshold model, and may map the nonlinear relationship between the battery terminal voltage and the state of charge SOC into a piecewise linearization relationship;
the estimation value module 32 is configured to construct a state observer according to a linear relationship in the battery model, and estimate the state of charge SOC serving as a state variable to obtain an estimation value of the state of charge SOC.
The key of piecewise linearization of the nonlinear relation between the open-circuit voltage OCV and the state of charge SOC contained in the battery model by using the threshold model is to determine the battery model parameters according to the open-circuit voltage OCV and the linearized model parameters of the state of charge SOC of the lithium ion battery.
The determination of the next time window can be performed by scaling the length of the time window according to the nonlinear strength.
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, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A joint estimation method for battery online modeling and state of charge is characterized by comprising the following steps:
step 1: acquiring battery end voltage value data and battery end current value data in a current time window;
step 2: performing value domain division according to different voltage value data to obtain a plurality of segmented intervals, establishing an autoregressive moving average model for each segmented interval, converting the autoregressive moving average model into a battery model, and identifying parameters of the battery model;
and step 3: constructing a state observer, estimating the state of charge (SOC) serving as a state variable to obtain an estimated value of the SOC;
and 4, step 4: judging whether a time window without data acquisition exists, if so, acquiring a next time window in a sliding manner, taking the acquired time window as a current time window, and executing the step 1; otherwise, executing step 5;
and 5: and completing on-line modeling and state of charge estimation of a battery model of the lithium ion battery.
2. The method for jointly estimating the on-line modeling and the state of charge of the battery according to claim 1, wherein the step 2 specifically comprises the following steps:
step 2.1: performing value domain division according to different voltage value data to obtain a plurality of sectional intervals, and establishing an autoregressive moving average model for each sectional interval by using battery end voltage value data and battery end current value data obtained by online measurement;
step 2.2: and converting the autoregressive moving average model into a battery model described by a corresponding state space, and identifying parameters of the battery model.
3. The method for jointly estimating the on-line modeling and the state of charge of the battery according to claim 1 or 2, wherein the step 3 specifically comprises the following steps:
step 3.1: the nonlinear relation between the open-circuit voltage OCV and the state of charge SOC contained in the battery model is subjected to piecewise linearization by using a threshold model, and can be mapped into the piecewise linearization relation between the battery terminal voltage and the state of charge SOC;
step 3.2: and constructing a state observer according to the linear relation in the battery model, and estimating the state of charge (SOC) serving as a state variable to obtain an estimated value of the SOC.
4. The method according to claim 3, wherein the key to piecewise linearize the nonlinear relationship between the open-circuit voltage OCV and the state of charge SOC contained in the battery model by using the threshold model is to determine the battery model parameters according to the linearized model parameters of the open-circuit voltage OCV and the state of charge SOC of the lithium ion battery.
5. The method of claim 1, wherein the determination of the next time window is scaled according to the degree of nonlinearity.
6. A combined estimation system for on-line modeling and state of charge of a battery is characterized by comprising an acquisition module, a battery model module, a state variable estimation module and a judgment module;
the acquisition module is used for acquiring battery end voltage value data and battery end current value data in a current time window;
the battery model module is used for carrying out value domain division according to different voltage value data to obtain a plurality of segmented intervals, establishing an autoregressive moving average model for each segmented interval, converting the autoregressive moving average model into a battery model and identifying parameters of the battery model;
the estimation module is used for constructing a state observer and estimating the state of charge (SOC) serving as a state variable to obtain an estimated value of the SOC;
the judging module is used for judging whether a time window without data acquisition exists, if so, the next time window is obtained in a sliding mode, the obtained time window is used as the current time window, and the voltage and current data of the next group of batteries are acquired to participate in calculation; otherwise, completing the on-line modeling and the state of charge estimation of the battery model of the lithium ion battery.
7. The system of claim 6, wherein the battery model module comprises a modeling module and a model conversion module;
the modeling module is used for carrying out value domain division according to different voltage value data to obtain a plurality of sectional intervals, and an autoregressive moving average model is established for each sectional interval by using the battery end voltage value data and the battery end current value data obtained by online measurement;
the model conversion module converts the autoregressive moving average model into a corresponding battery model described by a state space, and identifies parameters of the battery model.
8. The system for the joint estimation of the on-line modeling and the state of charge of the battery according to claim 6 or 7, wherein the state variable estimation module comprises a linearization module and an estimation value module;
the linearization module is used for carrying out piecewise linearization on the nonlinear relation between the open-circuit voltage OCV and the state of charge SOC contained in the battery model by utilizing the threshold model, and can be mapped into the piecewise linearization relation between the battery terminal voltage and the state of charge SOC;
and the estimation value module is used for constructing a state observer according to the linear relation in the battery model, estimating the state of charge (SOC) serving as a state variable and obtaining an estimation value of the SOC.
9. The system of claim 8, wherein the key to piecewise linearize the nonlinear relationship between the open circuit voltage OCV and the state of charge SOC included in the battery model using the threshold model is to determine the battery model parameters according to the linearized model parameters of the open circuit voltage OCV and the state of charge SOC of the lithium ion battery.
10. The system of claim 6, wherein the determination of the next time window is scaled according to the degree of non-linearity.
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