CN106707178B - Method for estimating SOC of battery by multi-gain observer based on classifier decision - Google Patents
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- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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Abstract
The invention discloses a classifier decision-based method for estimating the SOC of a battery by a multi-gain observer, which comprises the following steps of: s1, establishing a mathematical model of the battery open-circuit voltage and the battery SOC; s2, estimating the SOC of the battery by using a Luenberger observer with different gains and an extended Kalman filtering algorithm, and recording an error signal between the output of the extended Kalman filtering algorithm and a real measurement value; s3, designing a classifier according to the error signal; and S4, estimating the SOC of the battery by using a Luenberger observer, wherein the gain of the Luenberger observer is determined by the classifier. The method for estimating the SOC of the battery by the multi-gain observer based on the classifier decision can effectively estimate the SOC of the battery, has high precision and small dependence on a battery model, and can avoid a data saturation phenomenon; the algorithm of the method has strong error correction capability in the operation process. The method for estimating the SOC of the battery by the multi-gain observer based on the classifier decision can be widely applied to the field of battery SOC estimation.
Description
Technical Field
The invention relates to the field of battery SOC estimation, in particular to a method for estimating the SOC of a battery by a multi-gain observer based on classifier decision.
Background
Lithium batteries are highly nonlinear systems that involve a strong coupling of complex physical and electrochemical changes, which are difficult to model. How to realize accurate state estimation with quick error correction capability on the state of charge (SOC) of a battery under the condition that a model is not very accurate has important significance on accurately predicting the driving mileage of a vehicle and improving the reliability of vehicle operation.
At present, methods for estimating SOC in electric vehicles mainly include: an ampere-hour integration method, an open-circuit voltage method, a lunberg observer, a kalman filter algorithm, a particle filter algorithm, and the like. Ampere-hour integration is simple and feasible, but the initial error cannot be determined, and accumulated errors exist along with the time; the open-circuit voltage method needs to keep the battery still for a long time and is only suitable for laboratories; the Lonberg observer has a simple structure but slow convergence, and the algorithm depends on a model and has poor performance; the Kalman filtering method has moderate calculation amount but serious dependence on the model and can not overcome the problem of data saturation; particle filtering methods have high accuracy but very complex algorithms. The algorithm cannot distinguish the error source of the model output and the actual measured value in the battery charging and discharging process, the precision depends on the model, and the error correction capability in the operation process is poor.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a method for estimating the SOC of a battery by a classifier decision-based multi-gain observer, which has high estimation accuracy of the SOC of the battery and small dependence on a battery model.
The technical scheme adopted by the invention is as follows: a method for estimating the SOC of a battery by a multi-gain observer based on classifier decision comprises the following steps:
s1, establishing a mathematical model of the battery open-circuit voltage and the battery SOC as a battery model of the following steps S2-S4;
s2, estimating the SOC of the battery model by using an extended Kalman filtering algorithm, and recording an error signal between an SOC estimation value output by the battery model and a real SOC measurement value output by the battery;
s3, designing a classifier according to the error signal;
and S4, estimating the SOC of the battery by using a Luenberger observer, wherein the gain of the Luenberger observer is determined by the classifier.
Further, the step S3 includes the following steps:
s31, obtaining a criterion signal of the classifier according to the error signal;
and S32, designing a classifier according to the criterion signal.
Further, the criterion signal comprises an absolute value E1 of the low-pass filtered error signal and an absolute value E2 of an accumulated sum of the low-pass filtered error signal with an accumulation effect of a forgetting factor.
Further, the step S32 includes the following steps:
s321, extracting an absolute value E1 and an absolute value E2 generated when the SOC is estimated by the extended Kalman filter algorithm, and calculating a mean value mu and a standard deviation sigma of the mean value mu and the standard deviation sigma; extracting an absolute value E1 and an absolute value E2 of partial data of which the SOC error is smaller than a first preset value and larger than a second preset value when the SOC is estimated by an extended Kalman filtering algorithm, and respectively calculating the geometric centers of the partial data in an E2-E1 plane to obtain a first geometric center C1 and a second geometric center C2, wherein the first preset value is smaller than the second preset value;
s322, taking mu +/-6 sigma as a boundary condition, and taking a perpendicular bisector of the first geometric center C1 and the second geometric center C2 as a boundary line;
s323, if the absolute value E1 and the absolute value E2 in actual measurement exceed the boundary at the same time, adopting an aggressive observation strategy; if only one of the absolute value E1 and the absolute value E2 exceeds the boundary, adopting a normal observation strategy; if the absolute value E1 and the absolute value E2 in actual measurement are both in the boundary, judging whether the distance from the currently measured coordinates (E1 and E2) to the first geometric center C1 is smaller than the distance from the coordinates (E1 and E2) to the second geometric center C2, and if so, adopting a soft observation strategy; otherwise, an open loop estimation strategy is adopted.
Further, the filter coefficient of the low-pass filtering is 0.98.
Further, the forgetting factor is 0.97.
Further, the first predetermined value is 1% and the second predetermined value is 2%.
Further, the mathematical model comprises an electrochemical combinatorial model.
The invention has the beneficial effects that: the invention relates to a method for estimating the SOC of a battery by a multi-gain observer based on classifier decision, which comprises the steps of classifying error signals in the SOC estimation process of the battery, and designing a classifier to adopt different observation strategies for different types of errors; when the SOC is estimated by using the Luenberger observer, the classifier classifies the error signals and corrects the SOC parameters according to an observation strategy corresponding to the class of the error signals; therefore, the SOC of the battery can be effectively estimated, the accuracy is high, the dependence on a battery model is small, the data saturation phenomenon can be avoided, and the convergence speed of the observer is accelerated; the algorithm of the method has strong error correction capability in the running process, and can greatly improve the reliability of the SOC estimation algorithm in the severe environment with unexpected noise.
Drawings
The following further describes embodiments of the present invention with reference to the accompanying drawings:
FIG. 1 is a flow chart of the steps of a method of estimating battery SOC by a multi-gain observer based on classifier decision-making according to the present invention;
FIG. 2 is a response diagram of a Runberg observer estimation algorithm under different observation strategies in the classifier decision-based multi-gain observer battery SOC estimation method of the present invention;
FIG. 3 is a diagram illustrating the result of estimating the SOC of the battery by using the extended Kalman filter in the method for estimating the SOC of the battery by the multi-gain observer based on the classifier decision;
FIG. 4 is an architecture diagram of a method for estimating battery SOC by a multi-gain observer based on classifier decision-making according to the present invention;
FIG. 5 is a schematic diagram of a classifier in a method for estimating SOC of a battery by a multi-gain observer based on classifier decision according to the present invention;
FIG. 6 is a schematic diagram of the estimation performance of the multi-gain observer for estimating the SOC of the battery under normal conditions according to the present invention;
FIG. 7 is a diagram illustrating the estimation performance of the multi-gain observer for estimating the SOC of the battery in the presence of initial errors and unexpected noise according to the method for estimating the SOC of the battery based on the classifier decision.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a flowchart illustrating steps of a method for estimating SOC of a battery by a multi-gain observer based on classifier decision according to the present invention, including the following steps:
s1, establishing a mathematical model of the battery open-circuit voltage and the battery SOC as a battery model of the following steps S2-S4;
s2, estimating the SOC of the battery model by using a standard extended Kalman filtering algorithm, and recording an error signal between an SOC estimation value output by the battery model and a real SOC measurement value output by the battery;
s3, designing a classifier according to the error signal to decide feedback gain in real time;
and S4, calculating the residual capacity of the battery by utilizing a conventional estimating algorithm of the Reynberger observer to estimate the SOC of the battery, wherein the gain of the Reynberger observer is determined by the classifier.
The invention relates to a method for estimating the SOC of a battery by a multi-gain observer based on classifier decision, which comprises the steps of firstly, offline classifying error signals in the SOC estimation process of the battery, and designing a classifier to adopt different observation strategies for different types of errors; when the SOC is estimated on line by using a Luenberger observer, the classifier classifies the error signals and corrects the SOC parameters according to an observation strategy corresponding to the class of the error signals; therefore, the SOC of the battery can be effectively estimated, the accuracy is high, the dependence on a battery model is small, the data saturation phenomenon can be avoided, and the convergence speed of the observer is accelerated; the algorithm of the method has strong error correction capability in the running process, and can greatly improve the reliability of the SOC estimation algorithm in the severe environment with unexpected noise.
In this embodiment, a relation between the battery terminal voltage and the SOC is obtained by an offline measurement means to establish a mathematical model, where the relation may be related to variables including, but not limited to, current, temperature, aging, and the like; preferably, the mathematical model employs an electrochemical combinatorial model. In addition, when estimating the battery SOC by using the different gains of the luneberg observer and the extended kalman filter algorithm offline, referring to fig. 2 and fig. 3, fig. 2 is a response schematic diagram of the luneberg observer estimation algorithm under different observation strategies in the method for estimating the battery SOC by using the multi-gain observer based on the classifier decision of the present invention, fig. 3 is a result schematic diagram of estimating the battery SOC by using the extended kalman filter in the method for estimating the battery SOC by using the multi-gain observer based on the classifier decision of the present invention, wherein a certain error exists between the SOC estimated by using the extended kalman filter algorithm (EKF) and the reference SOC.
As a further improvement of the technical solution, referring to fig. 4, fig. 4 is an architecture diagram of the method for estimating the SOC of the battery by using the multi-gain observer based on the classifier decision, the SOC of the battery is estimated offline by using the luneberg observer with different gains and the extended kalman filter algorithm, the sensor 1 detects the current input to the battery model by using the current sensor, the sensor 2 detects the output voltage of the battery by using the voltage sensor to correct the output value of the battery model, and an error signal between the output of the extended kalman filter algorithm and a real measurement value is obtained and recorded; step S3 includes the following steps:
s31, obtaining a criterion signal of the classifier according to the error signal; the criterion signal comprises an absolute value E1 of the error signal subjected to low-pass filtering and an absolute value E2 of the accumulated sum of the error signal subjected to low-pass filtering under the accumulation action of a forgetting factor, wherein in the embodiment, the filtering coefficient of the low-pass filtering is 0.98, and the forgetting factor is 0.97;
and S32, designing a classifier according to the criterion signal.
As a further improvement of the technical solution, referring to fig. 4 and 5, fig. 4 is an architecture diagram of a method for estimating the SOC of the battery by the multi-gain observer based on the classifier decision, fig. 5 is a schematic diagram of the classifier in the method for estimating the SOC of the battery by the multi-gain observer based on the classifier decision, and step S32 includes the following steps:
s321, extracting an absolute value E1 and an absolute value E2 generated when the SOC is estimated by the extended Kalman filter algorithm, and calculating a mean value mu and a standard deviation sigma of the mean value mu and the standard deviation sigma; extracting an absolute value E1 and an absolute value E2 of partial data of which the SOC error is smaller than a first preset value and larger than a second preset value when the SOC is estimated by the extended Kalman filter, and respectively calculating the geometric centers of the partial data in an E2-E1 plane to obtain a first geometric center C1 and a second geometric center C2, wherein the first preset value is smaller than the second preset value; in this embodiment, the first predetermined value is 1%, and the second predetermined value is 2%;
s322, taking mu +/-6 sigma as a boundary condition, and taking a perpendicular bisector of the first geometric center C1 and the second geometric center C2 as a boundary line to obtain four partitions which are respectively an L1 partition, an L2 partition, an L3 partition and an L4 partition;
s323, if the absolute value E1 and the absolute value E2 exceed the boundary at the same time during actual measurement, namely are in the L1 partition, the Lonberg observer adopts an aggressive observation strategy; if only one of the absolute value E1 and the absolute value E2 exceeds the boundary, namely is in the L2 partition, the Lonberg observer adopts a normal observation strategy, namely a commonly adopted observation strategy; if the absolute value E1 and the absolute value E2 in actual measurement are both within the boundary, judging whether the distance from the currently measured coordinates (E1 and E2) to the first geometric center C1 is smaller than the distance from the coordinates (E1 and E2) to the second geometric center C2, if so, namely, in the L3 partition, adopting a soft observation strategy by the Luenberg observer; otherwise, in the L4 partition, the lunberger observer employs an open-loop estimation strategy.
After the classifier is established offline, data such as voltage and current of the battery are acquired online, deviation between the output of the battery model and a real measured value is acquired, and the classifier is used for selecting the gain of the observer online to correct the SOC of the battery. Referring to fig. 6 and 7, fig. 6 is a schematic diagram of the estimation performance of the method for estimating the SOC of the battery by the multi-gain observer based on the classifier decision under the normal condition, and fig. 7 is a schematic diagram of the estimation performance of the method for estimating the SOC of the battery by the multi-gain observer based on the classifier decision under the condition of the initial error and the unexpected noise.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. A method for estimating the SOC of a battery by a multi-gain observer based on classifier decision is characterized by comprising the following steps:
s1, establishing a mathematical model of the battery open-circuit voltage and the battery SOC as a battery model of the following steps S2-S4;
s2, estimating the SOC of the battery model by using an extended Kalman filtering algorithm, and recording an error signal between an SOC estimation value output by the battery model and a real SOC measurement value output by the battery;
s3, designing a classifier according to the error signal;
and S4, estimating the SOC of the battery by using a Luenberger observer, wherein the gain of the Luenberger observer is determined by the classifier.
2. The method for estimating the SOC of the battery by the multi-gain observer based on classifier decision as claimed in claim 1, wherein the step S3 comprises the steps of:
s31, obtaining a criterion signal of the classifier according to the error signal;
and S32, designing a classifier according to the criterion signal.
3. The method for classifier decision-based multi-gain observer estimation of battery SOC as claimed in claim 2, characterized in that the criterion signal comprises the absolute value E1 of the low-pass filtered error signal and the absolute value E2 of the accumulated sum of the low-pass filtered error signal with accumulation of forgetting factor.
4. The method for estimating the SOC of the battery by the multi-gain observer based on classifier decision as claimed in claim 3, wherein the step S32 includes the steps of:
s321, extracting an absolute value E1 and an absolute value E2 generated when the SOC is estimated by the extended Kalman filter algorithm, and calculating a mean value mu and a standard deviation sigma of the mean value mu and the standard deviation sigma; extracting an absolute value E1 and an absolute value E2 of partial data of which the SOC error is smaller than a first preset value and larger than a second preset value when the SOC is estimated by an extended Kalman filtering algorithm, and respectively calculating the geometric centers of the partial data in an E2-E1 plane to obtain a first geometric center C1 and a second geometric center C2, wherein the first preset value is smaller than the second preset value;
s322, taking mu +/-6 sigma as a boundary condition, and taking a perpendicular bisector of the first geometric center C1 and the second geometric center C2 as a boundary line;
s323, if the absolute value E1 and the absolute value E2 of the error signal of the output of the battery model and the real measurement value during actual measurement exceed the boundary at the same time, adopting an aggressive observation strategy; if only one of the absolute value E1 and the absolute value E2 exceeds the boundary, adopting a normal observation strategy; if the absolute value E1 and the absolute value E2 in actual measurement are both in the boundary, judging whether the distance from the currently measured coordinates (E1 and E2) to the first geometric center C1 is smaller than the distance from the coordinates (E1 and E2) to the second geometric center C2, and if so, adopting a soft observation strategy; otherwise, an open loop estimation strategy is adopted.
5. The method for classifier decision-based multi-gain observer estimation of battery SOC of claim 3, wherein the low pass filter has a filter coefficient of 0.98.
6. The method for classifier decision-based multi-gain observer estimation of battery SOC of claim 3, wherein the forgetting factor is 0.97.
7. The method of estimating battery SOC based on the multi-gain observer of classifier decision as claimed in claim 4, wherein the first predetermined value is 1% and the second predetermined value is 2%.
8. The classifier decision-based multi-gain observer method of estimating battery SOC of any of claims 1-7, wherein the mathematical model comprises an electrochemical combinatorial model.
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