CN112379268A - Lithium battery SOC estimation method and device based on SVM _ EKF algorithm and storage medium - Google Patents
Lithium battery SOC estimation method and device based on SVM _ EKF algorithm and storage medium Download PDFInfo
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- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 90
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 89
- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000012549 training Methods 0.000 claims description 18
- 238000001914 filtration Methods 0.000 claims description 9
- 238000012360 testing method Methods 0.000 claims description 8
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- 238000010801 machine learning Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
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- 150000002641 lithium Chemical class 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- 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]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- 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]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
- G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- 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]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
- G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements
Abstract
The embodiment of the invention provides a lithium battery SOC joint estimation method and system based on an EKF-SVM algorithm and a storage medium, belonging to the technical field of estimation of lithium battery SOC. The estimation method comprises the following steps: acquiring the current terminal voltage and current of the lithium battery; generating a terminal voltage observation output error and an optimal estimation value according to the terminal voltage and the current by adopting an EKF algorithm; inputting the terminal voltage, the current, the terminal voltage observation output error and the optimal estimation value into a trained SVM model to obtain the difference value between the true value and the optimal estimation value of the SOC of the lithium battery and the current maximum available capacity of the lithium battery; estimating a first SOC value of the lithium battery according to the difference value and the optimal estimation value; and estimating the true value of the SOC of the lithium battery according to the current maximum available capacity and the first SOC value of the lithium battery. The estimation method, the estimation device and the storage medium can overcome the technical defects of the EKF algorithm in the prior art when the EKF algorithm is applied to the technical field of lithium battery SOC estimation.
Description
Technical Field
The invention relates to the technical field of estimation of lithium battery SOC, in particular to a lithium battery SOC estimation method and device based on an SVM _ EKF algorithm and a storage medium.
Background
At present, a Kalman (KF) algorithm is widely applied to an estimation method for a state of charge (SOC) of a lithium ion battery. The main idea of the Kalman filtering algorithm for SOC estimation is to filter noise from signals affected by environmental noise and reduce the influence of the noise as much as possible, thereby realizing the optimal estimation on the power battery SOC in the sense of minimum variance. The initial KF algorithm is only suitable for a linear system, and the Extended Kalman Filter (EKF) proposed extends its application range to a nonlinear system, but the taylor expansion operation in the EKF algorithm linearizes the established battery model and introduces a truncation error, and the EKF is susceptible to environmental noise, thereby seriously affecting the accuracy and stability of battery state estimation. The Adaptive Extended Kalman Filter (AEKF) and the H-infinity Filter (HIF) developed on the basis solve the defect of reduced filtering precision to a certain extent on the basis of the EKF, but the rapid increase of the calculated amount greatly reduces the practicability and stability of the algorithm.
Disclosure of Invention
The embodiment of the invention aims to provide a lithium battery SOC estimation method, a lithium battery SOC estimation device and a storage medium based on an SVM _ EKF algorithm.
In order to achieve the above object, an embodiment of the present invention provides a method for estimating SOC of a lithium battery based on SVM _ EKF algorithm, where the method includes:
acquiring the current terminal voltage and current of the lithium battery;
generating a terminal voltage observation output error and an optimal estimation value according to the terminal voltage and the current by adopting an EKF algorithm;
inputting the terminal voltage, the current, the terminal voltage observation output error and the optimal estimation value into a trained SVM model to obtain the difference value between the true value and the optimal estimation value of the SOC of the lithium battery and the current maximum available capacity of the lithium battery;
estimating a first SOC value of the lithium battery according to the difference value and the optimal estimation value;
and estimating the true value of the SOC of the lithium battery according to the current maximum available capacity of the lithium battery and the first SOC value.
Optionally, the generating of the terminal voltage observation output error and the optimal estimation value according to the terminal voltage and the current by using the EKF algorithm specifically includes:
and performing 1000 times of filtering operation on the terminal voltage and the current by adopting the EKF algorithm to obtain a terminal voltage observation output error and an optimal estimation value.
Optionally, the estimation method further comprises:
acquiring historical terminal voltage and historical current of a lithium battery;
inputting the historical terminal voltage and the historical current into an EKF algorithm to obtain a corresponding terminal voltage observation output error and an optimal estimation value;
and combining the historical terminal voltage, the historical current, the terminal voltage observation output error, the optimal estimation value, the difference value between the true value and the optimal estimation value of the SOC corresponding to the historical terminal voltage and the historical current and the current maximum available capacity of the lithium battery to form a data set for training the SVM model.
Optionally, the estimation method further comprises:
dividing the data set into a training set and a test set according to the percentage of 7: 3;
training the SVM model by adopting the training set, wherein the training times are less than or equal to 3000;
testing the SVM model by using the test set, wherein the prediction error is less than 10-5Determining that training of the SVM model is completed.
Optionally, the estimating the true value of the SOC of the lithium battery according to the current maximum available capacity of the lithium battery and the first SOC value specifically includes:
estimating said true value according to equation (1),
wherein s (t) is the true value at time t, s (0) is the initial SOC value of the lithium battery, QNAnd I (t) is the discharge current of the lithium battery at the moment t, and eta is the coulombic efficiency.
On the other hand, the invention also provides a lithium battery SOC estimation device based on the SVM _ EKF algorithm, and the estimation device comprises:
the current and voltage acquisition equipment is used for acquiring the terminal voltage and the current of the lithium battery;
a processor to:
receiving the terminal voltage and current;
generating a terminal voltage observation output error and an optimal estimation value according to the terminal voltage and the current by adopting an EKF algorithm;
inputting the terminal voltage, the current, the terminal voltage observation output error and the optimal estimation value into a trained SVM model to obtain the difference value between the true value and the optimal estimation value of the SOC of the lithium battery and the current maximum available capacity of the lithium battery;
estimating a first SOC value of the lithium battery according to the difference value and the optimal estimation value;
and estimating the true value of the SOC of the lithium battery according to the current maximum available capacity of the lithium battery and the first SOC value.
Optionally, the estimating means is further configured to:
and performing 1000 times of filtering operation on the terminal voltage and the current by adopting the EKF algorithm to obtain a terminal voltage observation output error and an optimal estimation value.
Optionally, the estimating means is further configured to:
estimating said true value according to equation (1),
wherein s (t) is the true value at time t, s (0) is the initial SOC value of the lithium battery, QNAnd I (t) is the discharge current of the lithium battery at the moment t, and eta is the coulombic efficiency.
In yet another aspect, the present invention also provides a storage medium storing instructions for being read by a machine to cause the machine to perform the estimation method as described above.
According to the technical scheme, the method, the device and the storage medium for estimating the SOC of the lithium battery based on the SVM _ EKF algorithm preprocess the terminal voltage and the current of the lithium battery based on the EKF algorithm, output errors and optimal estimated values are observed based on the output terminal voltage, the original terminal voltage and the original current are used as the input of an SVM model, and finally the accurate estimation of the SOC of the lithium battery is realized while the interference of white noise such as a sensor is avoided by combining the actual value and the difference value of the optimal estimated values of the SOC output by the SVM model, the current maximum available capacity and the optimal estimated values of the lithium battery.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the embodiments of the invention without limiting the embodiments of the invention. In the drawings:
FIG. 1 is a flow chart of a lithium battery SOC joint estimation method based on SVM _ EKF algorithm according to an embodiment of the invention;
FIG. 2 is a flow diagram of an EKF algorithm in accordance with an embodiment of the present invention;
FIG. 3 is a flow diagram of a manner of acquiring a data set according to one embodiment of the invention;
FIG. 4 is a schematic diagram of a flow of a lithium battery SOC joint estimation method based on an SVM _ EKF algorithm according to an embodiment of the invention; and
fig. 5 is a block diagram of a structure of a lithium battery SOC joint estimation device based on an SVM _ EKF algorithm according to an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating embodiments of the invention, are given by way of illustration and explanation only, not limitation.
In the embodiments of the present invention, unless otherwise specified, the use of directional terms such as "upper, lower, top, and bottom" is generally used with respect to the orientation shown in the drawings or the positional relationship of the components with respect to each other in the vertical, or gravitational direction.
In addition, if there is a description of "first", "second", etc. in the embodiments of the present invention, the description of "first", "second", etc. is for descriptive purposes only and is not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between the various embodiments can be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not be within the protection scope of the present invention.
Fig. 1 is a flowchart illustrating a method for jointly estimating SOC of a lithium battery based on SVM _ EKF algorithm according to an embodiment of the present invention. In fig. 1, the estimation method may include:
in step S10, the terminal voltage and current of the present lithium battery are acquired.
In step S11, an EKF algorithm is used to generate a terminal voltage observation output error and an optimum estimated value from the terminal voltage and the current.
In step S12, the terminal voltage, the current, the error of the terminal voltage observed output, and the optimal estimated value are input into the trained SVM model to obtain the difference between the true value and the optimal estimated value of the SOC of the lithium battery and the current maximum available capacity of the lithium battery.
In step S13, a first SOC value of the lithium battery is estimated based on the difference and the optimal estimation value.
In step S14, the true value of the SOC of the lithium battery is estimated according to the current maximum available capacity of the lithium battery and the first SOC value. Specifically, the true value is estimated according to the formula (1),
wherein s (t) is the real value at the time t, s (0) is the initial SOC value of the lithium battery, and QNDt is the sampling period, i (t) is the discharge current of the lithium battery at time t, and η is the coulombic efficiency, and is usually 1.
In the prior art, an EKF algorithm mainly aims at general nonlinear system state quantity, a nonlinear system state function and an observation function are expanded into Taylor series, a second order and higher order terms are omitted, an approximate linearization model is obtained, and then a Kalman filtering method is adopted to complete processing such as filtering estimation of a target. Specifically, the flow of the conventional EKF algorithm is shown in fig. 2. After the EKF algorithm processing is performed as shown in fig. 2, due to the influence of white gaussian noise of a sensor and the like, a large deviation exists between the output optimal estimation value and the SOC of the lithium battery, so that the SOC value is not estimated accurately. However, although the SOC value is estimated by the AEKF algorithm or the HIF algorithm which is conventional in the related art, the deviation can be reduced to some extent, but the calculation amount of the algorithm is also increased sharply.
For the above reasons, the inventors have devised an estimation method as shown in fig. 1. In the method, first, the terminal voltage and current of the lithium battery are inputted into an EKF algorithm to obtain an observed output error of the terminal voltage and an optimum estimated value (step S11). It is noted that, in the estimation method provided in the present application, considering that the terminal voltage and current of the battery are continuous data that vary with time, during the EKF algorithm processing, the terminal voltage and current may be subjected to, for example, 1000 filtering operations to obtain the observed output error of the terminal voltage and an optimal estimation value.
And then, based on the obtained terminal voltage observation output error and the optimal estimation value, combining the terminal voltage and the current of the lithium battery, and further processing by adopting a four-input two-output SVM model. The SVM algorithm is a machine learning algorithm developed on the basis of the principle of structural risk minimization, and the problems of over-learning, quick dimension increase, easy falling into local optimal solution and the like in the traditional neural network are solved by seeking balance between the complexity and the learning capacity of a model by solving the convex quadratic programming problem, and the SVM algorithm has good robustness and better accuracy. When the inventor designs a product, the inventor finds that the characteristics of the SVM model can just overcome the defect of Gaussian white noise which cannot be overcome by the EKF algorithm, so that the reliability of the lithium battery model and the application range of SOC estimation are effectively improved. Therefore, the inventor inputs the terminal voltage, the current and the observation output error of the terminal voltage and the optimal estimation value into the trained SVM model. It should be noted that the structure of the trained SVM model can be in various forms known to those skilled in the art. However, in the technical scheme of the application, the terminal voltage observation output error and the maximum available capacity of the lithium battery are obtained finally in consideration of the requirement. Thus, the SVM model may be a structure including four inputs and two outputs.
In addition, the specific process for training the SVM model may take various forms known to those skilled in the art. In this embodiment, it is contemplated that the data set to be used for training should remain formally consistent with the actual data employed. Thus, the manner of acquisition of the data set may be as shown in fig. 3. In fig. 3, the obtaining manner may include:
in step S20, obtaining a history terminal voltage and a history current of the lithium battery;
in step S21, inputting the historical terminal voltage and the historical current into an EKF algorithm to obtain a corresponding terminal voltage observation output error and an optimal estimation value;
in step S22, the historical terminal voltage, the historical current, the terminal voltage observation output error, the optimal estimation value, the difference between the true value and the optimal estimation value of the SOC corresponding to the historical terminal voltage and the historical current, and the current maximum available capacity of the lithium battery are combined to form a data set for training the SVM model.
The process for the partitioning of the test set and the training set may also be in a variety of forms known to those skilled in the art. In a preferred example of the present invention, the data set may be partitioned into a training set and a test set at a percentage of 7: 3.
In addition, the number of times of training for the SVM model may be less than or equal to 3000 times. The prediction error may be less than 10-5。
A schematic diagram of the workflow of the overall method may be as shown in fig. 4.
On the other hand, the invention also provides a lithium battery SOC estimation device based on the SVM _ EKF algorithm, as shown in FIG. 4. In fig. 4, the estimation means may include a current voltage collecting device 10 and a processor 20. The current and voltage collecting device 10 may be used to collect terminal voltage and current of the lithium battery. The processor 20 may be configured to perform the methods as described above.
In yet another aspect, the present disclosure also provides a storage medium that may store instructions that may be read by a machine to cause the machine to perform the estimation method as described above.
Through the technical scheme, the lithium battery SOC estimation method, the device and the storage medium based on the SVM _ EKF algorithm, which are provided by the invention, preprocess the terminal voltage and the current of the lithium battery by the EKF algorithm, observe the output error and the optimal estimation value based on the output terminal voltage and use the original terminal voltage and the current as the input of the SVM model, finally combine the difference value between the real value and the optimal estimation value of the SOC of the lithium battery output by the SVM model, the current maximum available capacity and the optimal estimation value of the lithium battery, carry out machine learning on the filter data of the EKF algorithm by introducing the SVM model, optimize the EKF algorithm by using the error compensation value output by the SVM model, further correct the SOC error caused by the battery capacity attenuation by the maximum available capacity updated in real time, improve the estimation precision and the reliability of the single EKF algorithm in the aspect of estimating the SOC of the lithium battery, ensure the higher convergence and the robustness of the algorithm, and the high-precision estimation of the SOC of the lithium battery is effectively realized.
Although the embodiments of the present invention have been described in detail with reference to the accompanying drawings, the embodiments of the present invention are not limited to the details of the above embodiments, and various simple modifications can be made to the technical solution of the embodiments of the present invention within the technical idea of the embodiments of the present invention, and the simple modifications all belong to the protection scope of the embodiments of the present invention.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. In order to avoid unnecessary repetition, the embodiments of the present invention will not be described separately for the various possible combinations.
Those skilled in the art can understand that all or part of the steps in the method for implementing the above embodiments may be implemented by a program instructing related hardware, where the program is stored in a storage medium and includes several instructions to enable a single chip, a chip, or a processor (processor) to execute all or part of the steps in the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In addition, various different embodiments of the present invention may be arbitrarily combined with each other, and the embodiments of the present invention should be considered as disclosed in the disclosure of the embodiments of the present invention as long as the embodiments do not depart from the spirit of the embodiments of the present invention.
Claims (9)
1. A lithium battery SOC estimation method based on SVM _ EKF algorithm is characterized by comprising the following steps:
acquiring the current terminal voltage and current of the lithium battery;
generating a terminal voltage observation output error and an optimal estimation value according to the terminal voltage and the current by adopting an EKF algorithm;
inputting the terminal voltage, the current, the terminal voltage observation output error and the optimal estimation value into a trained SVM model to obtain the difference value between the true value and the optimal estimation value of the SOC of the lithium battery and the current maximum available capacity of the lithium battery;
estimating a first SOC value of the lithium battery according to the difference value and the optimal estimation value;
and estimating the true value of the SOC of the lithium battery according to the current maximum available capacity of the lithium battery and the first SOC value.
2. The estimation method according to claim 1, wherein said generating a terminal voltage observed output error and an optimal estimation value from said terminal voltage and current using an EKF algorithm specifically comprises:
and performing 1000 times of filtering operation on the terminal voltage and the current by adopting the EKF algorithm to obtain a terminal voltage observation output error and an optimal estimation value.
3. The estimation method according to claim 1, characterized in that the estimation method further comprises:
acquiring historical terminal voltage and historical current of a lithium battery;
inputting the historical terminal voltage and the historical current into an EKF algorithm to obtain a corresponding terminal voltage observation output error and an optimal estimation value;
and combining the historical terminal voltage, the historical current, the terminal voltage observation output error, the optimal estimation value, the difference value between the true value and the optimal estimation value of the SOC corresponding to the historical terminal voltage and the historical current and the current maximum available capacity of the lithium battery to form a data set for training the SVM model.
4. The estimation method according to claim 3, characterized in that the estimation method further comprises:
dividing the data set into a training set and a test set according to the percentage of 7: 3;
training the SVM model by adopting the training set, wherein the training times are less than or equal to 3000;
testing the SVM model by using the test set, wherein the prediction error is less than 10-5Determining that training of the SVM model is completed.
5. The estimation method according to claim 1, wherein the estimating of the true value of the SOC of the lithium battery based on the current maximum available capacity of the lithium battery and the first SOC value specifically comprises:
estimating said true value according to equation (1),
wherein s (t) is the true value at time t, s (0) is the initial SOC value of the lithium battery, QNAnd I (t) is the discharge current of the lithium battery at the moment t, and eta is the coulombic efficiency.
6. A lithium battery SOC estimation device based on SVM _ EKF algorithm is characterized by comprising:
the current and voltage acquisition equipment is used for acquiring the terminal voltage and the current of the lithium battery;
a processor to:
receiving the terminal voltage and current;
generating a terminal voltage observation output error and an optimal estimation value according to the terminal voltage and the current by adopting an EKF algorithm;
inputting the terminal voltage, the current, the terminal voltage observation output error and the optimal estimation value into a trained SVM model to obtain the difference value between the true value and the optimal estimation value of the SOC of the lithium battery and the current maximum available capacity of the lithium battery;
estimating a first SOC value of the lithium battery according to the difference value and the optimal estimation value;
and estimating the true value of the SOC of the lithium battery according to the current maximum available capacity of the lithium battery and the first SOC value.
7. The estimation apparatus according to claim 6, wherein the estimation apparatus is further configured to:
and performing 1000 times of filtering operation on the terminal voltage and the current by adopting the EKF algorithm to obtain a terminal voltage observation output error and an optimal estimation value.
8. The estimation apparatus according to claim 6, wherein the estimation apparatus is further configured to:
estimating said true value according to equation (1),
wherein s (t) is the true value at time t, s (0) is the initial SOC value of the lithium battery, QNAnd I (t) is the discharge current of the lithium battery at the moment t, and eta is the coulombic efficiency.
9. A storage medium storing instructions for reading by a machine to cause the machine to perform an estimation method according to any one of claims 1 to 5.
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