CN111914485A - Adaptive power battery SOC estimation method and system based on fractional order technology - Google Patents
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Abstract
The utility model provides a self-adaptive power battery SOC estimation method and system based on fractional order technology, comprising: establishing a fractional order equivalent circuit model of the power battery and discretizing the fractional order equivalent circuit model; obtaining parameters of each element in the model by minimizing the mean square error of the output value and the measured value of the model; and aiming at the obtained model, a particle filter algorithm is utilized, the variance in the filter algorithm is dynamically adjusted according to the initially set variance range by calculating the accumulated updating amount of the state and the error of the output voltage, and the state of the power battery is estimated. And accurate estimation of the SOC is realized by using a variance adaptive updating algorithm. And finally, the algorithm of the scheme is verified through dynamic working conditions, and the result shows that the algorithm has good convergence performance, high estimation precision and practical application value.
Description
Technical Field
The disclosure belongs to the technical field of power battery application, and particularly relates to a fractional order technology-based adaptive power battery SOC estimation method and system.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The electric automobile has the advantages of high energy utilization rate, low emission, low pollution and the like, and is widely concerned by the industry. A Battery Management System (BMS) is one of the core components of the electric automobile and has a basic effect on ensuring the reliable and safe operation of the electric automobile. Power battery state of charge (SOC) estimation is a major and difficult point of power battery management systems. The rapid and accurate SOC estimation is beneficial to protecting the battery, preventing overcharge and overdischarge and improving the utilization rate of the battery, and has important significance for promoting the development of electric automobiles.
At present, widely used power battery SOC estimation methods can be mainly classified into three categories: open-loop methods represented by coulomb counting and open-circuit voltage methods, model-based methods incorporating observer or filter theory, and data-driven methods based on machine learning algorithms. To date, coulometry is still the most traditional method of counting, which is most widely used, because it is simple and easy to implement. However, this method is very sensitive to initial estimation and has cumulative errors, so it is often necessary to adjust it using a relationship between Open Circuit Voltage (OCV) and state of charge (soc) measured in advance.
In general, simple open-loop methods do not cope well with aging and measurement disturbances, and accurate SOC estimation is difficult to achieve. The data driving method represented by fuzzy logic, artificial neural network and support vector machine can well deal with the nonlinear characteristic of the battery system, but the performance of the method depends on the quantity and quality of the training data set, so that the method is difficult to cope with various complex practical working conditions. The model-based method can well perform self-correction and realize accurate SOC estimation. However, the accuracy of the model is required to be high in the method, and the performance of the method needs to be guaranteed by the accurate model.
Disclosure of Invention
In order to overcome the defects of the prior art, the adaptive power battery SOC estimation method based on the fractional order technology is provided, and compared with a coulomb counting method, the strategy of estimating OCV and indirectly obtaining SOC through a monotonic relationship between OCV and SOC has better consistency.
In order to achieve the above object, one or more embodiments of the present disclosure provide the following technical solutions:
on one hand, the invention discloses a self-adaptive power battery SOC estimation method based on a fractional order technology, which comprises the following steps:
establishing a fractional order equivalent circuit model of the power battery and discretizing the fractional order equivalent circuit model;
obtaining parameters of each element in the model by minimizing the mean square error of the output value and the measured value of the model;
and aiming at the obtained model, a particle filter algorithm is utilized, the variance in the filter algorithm is dynamically adjusted according to the initially set variance range by calculating the accumulated updating amount of the state and the error of the output voltage, and the state of the power battery is estimated.
According to a further technical scheme, the fractional order equivalent circuit model of the power battery comprises ohmic internal resistance of the power battery, a constant phase element with fractional order characteristics and R which are connected in parallelpFor describing the polarization effect inside the battery, and for describing the open circuit voltage of the battery, which varies with the SOC of the battery. RpA resistive element being a parallel part of the equivalent circuit, connected in parallel with the constant phase element, for describing the electricityDynamic characteristics of the cell during charging and discharging.
In a further technical scheme, a state equation of the established equivalent circuit model is obtained based on kirchhoff's law and an element characteristic equation.
According to the further technical scheme, the power battery is subjected to charge and discharge tests to obtain the open-circuit voltage of the battery in different SOC states, and the relation between the open-circuit voltage OCV and the SOC is obtained by utilizing polynomial fitting.
According to the further technical scheme, based on the relation between the open-circuit voltage OCV and the SOC, the state equation of the equivalent circuit model is discretized, and the discrete equation of the model is obtained.
According to the further technical scheme, a pulse charging and discharging test strategy is adopted, the battery is placed statically after the discharging or charging of fixed electric quantity is finished each time to obtain the open-circuit voltage of the battery under different SOC levels, and finally the average value of the voltage after the standing in the charging and discharging process is taken as the open-circuit voltage of the battery to eliminate the hysteresis effect of the charged open-circuit voltage.
Further technical scheme is that [ R ] in the model is used0,Rp,Cp,α]As the parameters to be identified, the model parameters are obtained by minimizing the mean square error of the output value and the measured value of the model.
In another aspect, an adaptive power battery SOC estimation system based on fractional order technique is disclosed, comprising:
a model building module configured to: establishing a fractional order equivalent circuit model of the power battery and discretizing the fractional order equivalent circuit model;
a model parameter obtaining module configured to: obtaining parameters of each element in the model by minimizing the mean square error of the output value and the measured value of the model;
a state estimation module of the power battery configured to: and aiming at the obtained model, a particle filter algorithm is utilized, the variance in the filter algorithm is dynamically adjusted according to the initially set variance range by calculating the accumulated updating amount of the state and the error of the output voltage, and the state of the power battery is estimated.
The above one or more technical solutions have the following beneficial effects:
aiming at the problems that the traditional integer order model of the power battery is insufficient in precision and the traditional filtering algorithm estimation is difficult to converge and poor in robustness, the technical scheme of the disclosure provides a power battery SOC estimation method based on open-circuit voltage and completely applicable to a nonlinear model on the basis of a fractional order equivalent circuit, and further realizes accurate estimation of SOC by using a variance adaptive updating algorithm. And finally, the algorithm of the scheme is verified through dynamic working conditions, and the result shows that the algorithm has good convergence performance, high estimation precision and practical application value.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
FIG. 1 is a schematic block diagram of a method for estimating SOC according to an embodiment of the present invention;
FIG. 2 is a diagram of an equivalent circuit of fractional order incorporating a CPE component according to the present invention;
FIG. 3 is a graph comparing the power open circuit voltage fitting results with the measured values;
FIG. 4 is a graph of input current versus output voltage for the DST operating condition;
FIG. 5 is a flow chart of a particle swarm optimization algorithm for obtaining model parameters;
FIG. 6 is a diagram illustrating the estimation result of the present scheme under the FUDS condition.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
Compared with a traditional equivalent circuit model which utilizes ideal circuit elements to simulate the external output characteristics of the power battery, the Fractional Order Model (FOM) can better fit the overall performance of the battery from the frequency domain perspective by introducing a Constant Phase Element (CPE) with the fractional order characteristics. Research in materials science has shown that electrochemical reactions inside batteries have properties such as anomalous diffusion, memory, and hysteresis, which can be more succinctly described using a fractional order model.
The activation polarization and concentration difference polarization degrees of the battery under different discharge multiplying powers are different, so that the recovery effect of the battery after discharge cutoff is different. In order to better cope with the change of the available electric quantity of the battery under different discharge rates, the strategy of estimating the OCV and indirectly obtaining the SOC through the monotonic relationship between the OCV and the SOC has better consistency compared with the coulomb counting method.
The general idea proposed by the present disclosure:
the method mainly utilizes a fractional order equivalent circuit containing a CPE element to obtain a state space model of the lithium ion battery, combines the results obtained by a pulse discharge test and a DST working condition test, and utilizes a particle swarm optimization algorithm and a polynomial fitting technology to obtain the relationship between the OCV and the SOC and the parameters of each element in the model. In addition, the state of the battery is estimated by the model through a particle filtering algorithm, and the variance in the filtering algorithm is dynamically adjusted by referring to the accumulated state updating data, so that the robustness and the stability of state estimation are improved.
The technical scheme disclosed by the invention uses a particle filtering algorithm, has better advantages compared with the existing extended Kalman filtering algorithm or least square algorithm, and has better adaptability to a nonlinear system.
The model used in the technical scheme of the disclosure is a first-order model, and the SOC is indirectly estimated by estimating the open-circuit voltage. And an adaptive variance update algorithm based on historical updates is used.
The embodiment discloses a fractional order technology-based adaptive power battery SOC estimation method, which comprises the following steps:
1) establishing a fractional order equivalent circuit model of the power battery, and discretizing the fractional order equivalent circuit model by utilizing the GL definition of the fractional order calculus and the OCV-SOC relation; GL definition, formula (4);
2) designing an experiment to obtain related data, establishing a battery data set by utilizing a pulse discharge test, a dynamic working condition test and the like, and verifying the fractional order characteristic of the battery;
3) obtaining an OCV-SOC relation by utilizing a polynomial fitting technology, and obtaining parameters of the model by utilizing a particle swarm optimization algorithm;
4) and the SOC accurate estimation based on the OCV is realized by utilizing a particle filter algorithm based on a Monte Carlo method and a resampling method. In addition, a self-adaptive variance updating algorithm is designed, the state variance is dynamically adjusted in the estimation process, and the robustness and the convergence speed of the algorithm are improved.
The battery state estimation needs a model, the particle swarm optimization algorithm is a swarm optimization algorithm, and model parameters of the equivalent circuit, namely R, can be obtained by utilizing the swarm optimization algorithm0 RpC α can be determined. The element parameters in equation (7) can be determined, and then the state estimation can be realized by using the particle filter algorithm (the importance resampling and the monte carlo method are introduced in the particle filter algorithm). In particle filtering, the effect of the algorithm is given by w in equation (7)k vkThe influence is large, and the self-adaptive variance updating is to continuously adjust the two parameters according to the error and the historical data in the estimation process.
The method indirectly obtains the SOC by estimating the open-circuit voltage, and can better realize the accurate estimation of the SOC under various complex working conditions by utilizing a self-adaptive particle filter algorithm.
Further, the power battery equivalent circuit model mentioned in step (1) includes a Constant Phase Element (CPE) with a fractional order characteristic. The equivalent impedance of a CPE element can be expressed as
Wherein alpha is the fractional order of the element, C is the fractional capacitance constant, and S is the operator of Laplace transform.
A fractional order equivalent circuit model of the lithium ion power battery is established as shown in figure 2, wherein CPE and polarization resistance R in the equivalent circuit modelpAnd are connected in parallel to describe the polarization effect inside the battery. In addition, the circuit comprises a voltage source for describing the OCV and an ohmic internal resistance R0. The voltage source describes the open circuit voltage of the battery, which varies as the SOC of the battery varies. V in the modeltAnd ItRespectively describing the terminal current and the terminal voltage of the power battery when ItPositive indicates charging. Based on kirchhoff's law and element characteristic equation, the characteristic equation of the established equivalent circuit model can be written as
Suppose focv(SOC) describes the open circuit voltage versus SOC, then open circuit voltage OCV versus ItCan be written as
Taking into account the battery capacity QnIs far greater than It(t), therefore, V can be consideredocSince (t) changes slowly with time, it is considered that
Furthermore, the derivative of the order a of a given function x (t) can be written as GL definition of fractional calculus
Wherein the operator
Representing an arbitrary order integral or derivative of the function with respect to t. T issIs the sampling time of the system [. ]]Is the function of the lower rounding off,
is a binomial expression (1-z)αCan be written as
Wherein the gamma function (-) can be written as
Considering that the SOC estimation method based on the OCV can better overcome the inconsistency brought by different discharge rates, the OCV is used as the state variable.
In the scheme, the SOC is estimated by indirectly utilizing the monotonic relation between the OCV and the SOC after the OCV is estimated, and the discrete equation which can write a model by combining the definition is
Wherein ω iskAnd upsilonkRespectively the state of the model and the measurement noise.
In the step (2), a pulse charge-discharge test strategy is adopted, the battery is placed statically after the discharge or the charge of fixed electric quantity is finished each time to obtain the open-circuit voltage of the battery under different SOC levels, and finally the average value of the voltage after the standing in the charge-discharge process is taken as the open-circuit voltage of the battery to eliminate the hysteresis loop-back effect of the charge-discharge open-circuit voltage. And obtaining a voltage current curve of the battery under the Dynamic working condition by using a Dynamic Stress Test (DST). The test scheme is to split, cut and simplify general operating mode, and combine power distribution statistics to obtain, the simulation of the charge-discharge device of being convenient for. Further, other simulation tests such as FUDS and the like which are more consistent with actual working conditions can be performed.
This example was tested experimentally using a model 18650 LNMC lithium ion battery from Samsung corporation. The specific parameters of the battery are as follows:
table 1: 18650 model LNMC lithium ion battery's parameters
And carrying out pulse charge and discharge test on the battery to obtain the open-circuit voltage of the battery in different SOC states. The battery was fully charged using a standard charging strategy, and then discharged with a current of 1A (i.e., 0.5C rate), giving up 10% of its capacity every 12 minutes of discharge. And (3) standing the battery for 2 hours after each discharge, and taking the voltage of the battery after standing for two hours as the open-circuit voltage of the battery in the SOC state in the discharge process. The open-circuit voltages of the batteries in different SOC states in the charging process can be obtained by the same strategy, and the average value of the voltages in the two processes is finally taken as the open-circuit voltage in the corresponding SOC state in consideration of the hysteresis effect of the open-circuit voltage of the batteries.
In step (3), first a function of OCV with respect to SOC is established, i.e., focv(SOC). In the scheme, a six-order polynomial is selected to describe the relationship between the two
Voc(5OC)＝K1SOC6+K2SOC5+K3SOC4+K4SOC3+K5SOC2+K6SOC+K7, (8)
The parameters can be obtained by a parameter identification tool carried by Matlab. Further, utilizing a particle swarm optimization algorithm to optimize [ R ] in the model0,Rp,Cp,α]As the band identification parameters, the model parameters are obtained by minimizing the mean square error of the output value and the measured value of the model.
The parameters obtained by using the curve fitting toolbox of Matlab are shown in Table 2, and the fitting effect is shown in figure 3.
Table 2: open circuit voltage polynomial fitting parameters
And testing the battery by using a dynamic stress test (DST working condition) of the electric automobile to obtain element parameters in the equivalent circuit model. The working condition is a simplified simulation of the practical application of the battery, and the current and the voltage of the DST working condition test obtained in the experiment are shown in the attached figure 4. Further, the [ R ] in the model is optimized by applying a particle swarm optimization algorithm0,Rp,Cp,α]As the parameters to be identified, the model parameters are obtained by minimizing the mean square error of the output value and the measured value of the model.
The flow chart of the particle swarm optimization algorithm is shown in fig. 5, and the obtained model parameters are shown in table 3.
Table 3: equivalent circuit parameter table
In step (4), a particle filtering algorithm based on a monte carlo sampling process is adopted to realize accurate estimation of the state. In order to improve the robustness and convergence speed of the algorithm, an adaptive variance updating algorithm considering the output error and the state accumulated variation is provided. By the algorithm, accurate and quick SOC real-time estimation can be realized.
Referring again to fig. 1, the specific steps are:
(1) calculating the cumulative update quantity of the state to be estimated at the past m moments, wherein the state is the open-circuit voltage V in the formula (7)ocAnd a polarization voltage Vp。
(2) And preliminarily determining the variance at the next moment according to a preset variance range. (three variance thresholds, i.e., maximum variance value Q, are given at the beginning of the algorithmc,maxMean variance value Qc,midMinimum variance value Qc,min)。
(3) And determining the final variance according to the difference value of the predicted terminal voltage and the actually measured terminal voltage of the model. Wherein ErrormaxThe maximum output voltage error of the model.
In combination with the resulting model, a particle filtering algorithm may be applied to estimate the battery state. The particle filter algorithm takes Bayes inference and importance resampling as a basic framework, applies a Monte Carlo method, and expresses probability by a particle set, thereby being applied to state space models in any forms. Compared with the traditional Kalman filtering and the like, the particle filtering algorithm has better adaptability to the nonlinear fractional order model in the patent. In order to improve the convergence speed and robustness of the algorithm, the dynamic variance updating algorithm is adopted in the particle filter algorithm, and the convergence speed of the algorithm is obviously improved by calculating the accumulated updating quantity of the state and the error of the output voltage and dynamically adjusting the model variance according to the initially set variance range. FIG. 6 shows the effect diagram of the method under the FUDS working condition test, and it can be seen that the method can effectively realize accurate and fast estimation of SOC.
In another embodiment, the present invention is directed to a computing device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the specific steps of the method in the above embodiment.
In another implementation example, the embodiment is also disclosed to provide a computer readable storage medium.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, performs specific steps of the above-described example method.
In another embodiment, an adaptive power battery SOC estimation system based on fractional order technique is disclosed, comprising:
a model building module configured to: establishing a fractional order equivalent circuit model of the power battery and discretizing the fractional order equivalent circuit model;
a model parameter obtaining module configured to: obtaining parameters of each element in the model by minimizing the mean square error of the output value and the measured value of the model;
a state estimation module of the power battery configured to: and aiming at the obtained model, a particle filter algorithm is utilized, the variance in the filter algorithm is dynamically adjusted according to the initially set variance range by calculating the accumulated updating amount of the state and the error of the output voltage, and the state of the power battery is estimated.
The steps involved in the apparatus of the above embodiment correspond to the first embodiment of the method, and the detailed description thereof can be found in the relevant description of the first embodiment. The term "computer-readable storage medium" should be taken to include a single medium or multiple media containing one or more sets of instructions; it should also be understood to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any of the methods of the present disclosure.
Those skilled in the art will appreciate that the modules or steps of the present disclosure described above can be implemented using general purpose computer means, or alternatively, they can be implemented using program code executable by computing means, whereby the modules or steps may be stored in memory means for execution by the computing means, or separately fabricated into individual integrated circuit modules, or multiple modules or steps thereof may be fabricated into a single integrated circuit module. The present disclosure is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.
Claims (10)
1. A self-adaptive power battery SOC estimation method based on a fractional order technology is characterized by comprising the following steps:
establishing a fractional order equivalent circuit model of the power battery and discretizing the fractional order equivalent circuit model;
obtaining parameters of each element in the model by minimizing the mean square error of the output value and the measured value of the model;
and aiming at the obtained model, a particle filter algorithm is utilized, the variance in the filter algorithm is dynamically adjusted according to the initially set variance range by calculating the accumulated updating amount of the state and the error of the output voltage, and the state of the power battery is estimated.
2. The adaptive power battery SOC estimation method based on fractional order technology as claimed in claim 1, wherein the fractional order equivalent circuit model of the power battery comprises sequentially connected ohmic internal resistance of the power battery, a constant phase element with fractional order characteristic and R connected in parallelpFor describing the polarization effect inside the battery, and for describing the open circuit voltage of the battery, which varies with the SOC of the battery.
3. The adaptive power battery SOC estimation method based on the fractional order technology as claimed in claim 1, wherein a characteristic equation of the established equivalent circuit model is obtained based on kirchhoff's law and an element characteristic equation.
4. The adaptive power battery SOC estimation method based on the fractional order technique as claimed in claim 1, wherein the power battery is tested for charge and discharge to obtain the open circuit voltage of the battery under different SOC states, and the relationship between the open circuit voltage OCV and SOC is obtained by polynomial fitting.
5. The adaptive power battery SOC estimation method based on the fractional order technique as claimed in claim 1, wherein the characteristic equation of the equivalent circuit model is discretized based on the relationship between the open circuit voltage OCV and the SOC to obtain a discrete equation of the model.
6. The adaptive power battery SOC estimation method based on fractional order technology as claimed in claim 1, wherein a pulse charge and discharge test strategy is adopted, the battery is left to stand after each time of finishing the discharge or charge of fixed electric quantity to obtain the open circuit voltage of the battery under different SOC levels, and finally the average value of the voltage after standing in the charge and discharge process is taken as the open circuit voltage of the battery to eliminate the hysteresis loop effect of the open circuit voltage.
7. The adaptive power battery SOC estimation method based on fractional order technique as claimed in claim 1, wherein [ R ] in model0,Rp,Cp,α]As the parameters to be identified, the model parameters are obtained by minimizing the mean square error of the output value and the measured value of the model.
8. A self-adaptive power battery SOC estimation system based on fractional order technology is characterized by comprising the following steps:
a model building module configured to: establishing a fractional order equivalent circuit model of the power battery and discretizing the fractional order equivalent circuit model;
a model parameter obtaining module configured to: obtaining parameters of each element in the model by minimizing the mean square error of the output value and the measured value of the model;
a state estimation module of the power battery configured to: and aiming at the obtained model, a particle filter algorithm is utilized, the variance in the filter algorithm is dynamically adjusted according to the initially set variance range by calculating the accumulated updating amount of the state and the error of the output voltage, and the state of the power battery is estimated.
9. A computing device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of any of claims 1-7 when executing the program.
10. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method according to any one of the preceding examples 1 to 7.
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