CN111679199B - Lithium ion battery SOC estimation method and device - Google Patents

Lithium ion battery SOC estimation method and device Download PDF

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CN111679199B
CN111679199B CN202010438210.4A CN202010438210A CN111679199B CN 111679199 B CN111679199 B CN 111679199B CN 202010438210 A CN202010438210 A CN 202010438210A CN 111679199 B CN111679199 B CN 111679199B
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CN111679199A (en
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陈峥
舒星
申江卫
颜文胜
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Kunming University of Science and Technology
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367Software therefor, e.g. for battery testing using modelling or look-up tables
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/385Arrangements for measuring battery or accumulator variables
    • G01R31/387Determining ampere-hour charge capacity or SoC
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/385Arrangements for measuring battery or accumulator variables
    • G01R31/387Determining ampere-hour charge capacity or SoC
    • G01R31/388Determining ampere-hour charge capacity or SoC involving voltage measurements
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/389Measuring internal impedance, internal conductance or related variables

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Abstract

The invention is suitable for the technical field of lithium ion batteries, and provides a method and a device for improving the SOC estimation precision of a lithium ion battery, wherein the method comprises the following steps: and acquiring the working state of the lithium ion battery at the moment k, and calculating to obtain the maximum discharge capacity of the battery by acquiring the characteristic parameters when the lithium ion battery is in a charging state. And after the lithium ion battery is converted into a discharge state, acquiring current and voltage in the working process of the lithium ion battery, performing online identification, acquiring identification parameters of the lithium ion battery at the moment, calculating the terminal voltage, the current and the identification parameters of the lithium ion battery at the moment k and the acquired maximum discharge capacity, and acquiring the SOC of the lithium ion battery at the moment k. The method and the device can accurately estimate the residual electric quantity of the lithium ion battery after the environmental temperature changes and the battery ages, improve the efficiency and the accuracy of estimating the residual electric quantity of the lithium ion battery, and have strong anti-jamming capability.

Description

Lithium ion battery SOC estimation method and device
Technical Field
The invention belongs to the technical field of lithium ion batteries, and particularly relates to a lithium ion battery SOC estimation method and device.
Background
At present, the method for estimating the remaining capacity of the battery usually adopts an ampere-hour integration method and an open-circuit voltage method for calculation, the method can be influenced by an initial SOC and a current acquisition error to cause inaccurate estimation, the estimation error is gradually increased along with the aging of the battery and the temperature change of the battery, and the precision is difficult to ensure.
In order to reduce the SOC estimation error caused by the current sampling error, researchers develop an estimation algorithm based on a battery model, the algorithm can effectively reduce the influence caused by current noise, but the algorithm needs to rely on the identification precision of model parameters, and after the temperature change and the battery aging, the model parameters change greatly, so that ideal estimation precision cannot be obtained.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for estimating an SOC of a lithium ion battery, so as to solve the problems in the prior art.
A first aspect of an embodiment of the present invention provides a method for estimating an SOC of a lithium ion battery, including:
step 1: according to the battery running current voltage, running an improved least square method with forgetting factors to obtain the ohmic internal resistance, the polarization internal resistance and the open-circuit voltage of the lithium ion battery in real time;
step 2: preprocessing the charging voltage data and the capacity value sequence to obtain charging time (t) in a specific voltage range as a parameter of the step 3;
and step 3: running a least square support vector machine (LS-SVM) method to search for an incidence relation, and training the capacity value sequence and the specific voltage range charging time (t) in the step 2 to obtain a trained aging model;
and 4, step 4: charging data in normal service life is obtained through a battery management system, and data processing is carried out to obtain a charging time sequence (T) in a specific voltage range;
and 5: inputting the charging time sequence (T) with the specific voltage range in the step (4) into the aging model in the step (3) for prediction to obtain a target battery capacity value;
step 6: and (5) calculating in real time to obtain the SOC of the lithium ion battery by adopting a self-adaptive H infinite filter algorithm according to the ohmic internal resistance, the polarization capacitance and the open-circuit voltage which are obtained in the step (1) and the capacity value obtained in the step (5).
Preferably, in step 1, the obtaining of the ohmic resistance, the polarization internal resistance, the polarization capacitance, and the open-circuit voltage of the lithium ion battery in real time according to the improved least square algorithm with forgetting factor includes:
reading the terminal voltage and the charge and discharge current of the lithium ion battery;
establishing a first-order equivalent circuit model of the lithium ion battery;
and calculating the current and the terminal voltage according to an improved least square algorithm with a forgetting factor based on the first-order equivalent circuit model of the lithium ion battery, and acquiring the ohmic resistance, the polarization internal resistance, the polarization capacitance and the open-circuit voltage of the lithium ion battery in real time.
Preferably, in step 2, the series of charging voltage data and capacity values for different cycle lives is determined.
Preferably, in step 2, the method for preprocessing the charging voltage data and the capacity value sequence under different cycle lives is to record the charging time from the voltage threshold value 1 to the voltage threshold value 2 of the battery under different aging conditions, so as to obtain the charging time sequence (T) in the specific voltage range.
Preferably, in step 3, the aging model inputs the single-cycle specific voltage range charging time (t) and outputs the current battery capacity value.
Preferably, the calculating ohmic internal resistance, polarization capacitance, open-circuit voltage, capacity, current and terminal voltage of the lithium ion battery according to the adaptive H-infinity filtering algorithm to obtain the SOC of the lithium ion battery in real time includes:
and calculating ohmic internal resistance, polarization capacitance, open-circuit voltage, capacity, current and terminal voltage of the lithium ion battery according to a self-adaptive H infinite filtering algorithm based on the first-order equivalent circuit model of the lithium ion battery, and acquiring the SOC of the lithium ion battery in real time.
A second aspect of the embodiments of the present invention provides a lithium ion battery SOC estimation device, including:
the current and voltage real-time acquisition module is used for acquiring the current and the voltage of the lithium ion battery in real time;
the parameter calculation module is used for calculating the first-order equivalent circuit model of the lithium ion battery and the current and the voltage of the lithium ion battery acquired at the moment k to acquire identification parameters of the lithium ion battery; wherein the identification parameters comprise ohmic resistance, polarization capacitance and open circuit voltage;
the charging time acquisition module in the specific voltage range is used for acquiring the charging time in the specific voltage range corresponding to the lithium ion battery;
the capacity value calculation module is used for acquiring a corresponding capacity value according to the trained aging model and the current specific voltage range charging time;
and the SOC calculation model is used for calculating the open-circuit voltage, the terminal voltage, the current, the identification parameters and the maximum discharge capacity of the lithium ion battery at the current moment to obtain the SOC of the lithium ion battery at the current moment.
Compared with the prior art, the invention has the following advantages:
1. aiming at the problem of inaccurate estimation of the SOC of the battery, the lithium ion battery is taken as a research object, and the battery parameters are identified on line by adopting an improved least square method with forgetting factors, so that the influence of temperature, aging and current on the battery parameters is avoided, and the model precision is improved.
2. In order to solve the problem that the capacity is reduced due to battery aging, and further SOC estimation is inaccurate, the invention provides a least square support vector machine method for estimating the battery capacity, and then the battery capacity is fed back to an SOC estimation module, so that SOC estimation errors caused by battery capacity attenuation are effectively avoided.
3. The method adopts the self-adaptive H infinite filtering algorithm to estimate the SOC of the battery, effectively reduces the influence of model parameters on SOC errors, does not depend on current sampling errors, and further improves the SOC estimation precision.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a schematic flow diagram of an implementation of the present invention;
FIG. 2 is a schematic diagram of a first order equivalent circuit model of a battery embodying the present invention;
FIG. 3 is a schematic diagram of a terminal device embodying the present invention;
FIG. 4 is a schematic diagram of a second preset algorithm implemented in the present invention to obtain the maximum discharge capacity of the battery;
FIG. 5 is a schematic diagram of a third pre-set algorithm implemented by the present invention to obtain battery SOC;
fig. 6 is a schematic diagram of a lithium ion battery SOC estimation device implemented by the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood by those skilled in the art, the technical solutions in the embodiments of the present invention will be clearly described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "comprises" and "comprising," and any variations thereof, in the description and claims of this invention and the above-described drawings are intended to cover non-exclusive inclusions. For example, a process, method, or system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus. Furthermore, the terms "first," "second," and "third," etc. are used to distinguish between different objects and are not used to describe a particular order.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
Example one
As shown in fig. 1, the present embodiment provides a method for estimating SOC of a lithium ion battery, which may be applied to a terminal device such as a battery management system of an electric vehicle. The SOC estimation method for a lithium ion battery provided in this embodiment includes:
s101, acquiring the ohmic resistance, the polarization internal resistance, the polarization capacitance and the open-circuit voltage of the lithium ion battery in real time according to a first preset algorithm.
In the specific application, the charging and discharging current and the terminal voltage of the lithium ion battery are obtained in real time, the charging and discharging current and the terminal voltage of the lithium ion battery are calculated through a first preset algorithm, and the ohmic resistance, the polarization capacitance and the open-circuit voltage of the lithium ion battery are obtained. The first preset algorithm is a parameter identification algorithm based on an improved least square method with a forgetting factor.
And S102, acquiring the maximum discharge capacity of the lithium ion battery in real time according to a second preset algorithm.
In specific application, the charging time of the lithium ion battery in the specific voltage range is obtained in real time, and the charging time of the lithium ion battery in the specific voltage range is calculated according to a second preset algorithm to obtain the maximum discharge capacity of the lithium ion battery. Wherein the second predetermined algorithm includes, but is not limited to, a least squares support vector machine based algorithm.
S103, calculating the charge-discharge current, the terminal voltage, the ohmic internal resistance, the polarization capacitance and the maximum discharge capacity of the lithium ion battery according to a third preset algorithm, and acquiring the SOC of the lithium ion battery in real time.
In specific application, the charge-discharge current, the terminal voltage, the ohmic internal resistance, the polarization capacitance and the maximum discharge capacity of the lithium ion battery are calculated according to a third preset algorithm, and the SOC of the lithium ion battery is obtained in real time. Wherein the third preset algorithm includes, but is not limited to, an adaptive H-infinity based filtering algorithm
In the embodiment, the charging time corresponding to different maximum discharging capacities of the battery in different cycle lives is obtained through experiments, and the maximum discharging capacity of the lithium ion battery is updated in real time by establishing a charging time and maximum discharging capacity model based on a least square support vector machine algorithm. Meanwhile, the internal identification parameters (namely open-circuit voltage, internal resistance, polarization resistance and polarization capacitance) of the lithium ion battery are updated in real time through a least square algorithm with forgetting factors, and then the residual electric quantity of the lithium ion battery is calculated in real time through the updated identification parameters and the maximum discharge capacity by adopting a self-adaptive H-expanding infinite filtering algorithm, so that the SOC estimation precision of the lithium ion battery is improved.
Example two
As shown in fig. 2, this embodiment is a further description of the method steps in the first embodiment. In this embodiment, step S101 includes:
and S1011, reading the terminal voltage of the lithium ion battery.
And S1012, establishing a first-order equivalent circuit model of the lithium ion battery.
In a specific application, a first-order equivalent circuit model of any single battery in the lithium ion battery pack is shown in fig. 3. Wherein E is terminal voltage, VOCVIs an open circuit voltage, R0Is ohmic internal resistance, R1C1For describing polarization characteristics, polarization resistance R, during charging and discharging of the battery1Voltage at both ends is V1And I is charge-discharge current. R0、R1、C1Is the parameter to be identified. According to the circuit principle, let tau be R1C1Then the electrical characteristics of the lithium ion battery can be described as:
Figure GDA0002614391750000061
E=Voc(s,T)-V1-R0It+v
discretizing the above formula can obtain:
Figure GDA0002614391750000062
further obtaining:
Figure GDA0002614391750000063
by elimination of V1,kTo obtain
Figure GDA0002614391750000064
S1013, calculating the terminal voltage according to a first preset algorithm based on the first-order equivalent circuit model of the lithium ion battery, and acquiring ohmic resistance, polarization internal resistance, polarization capacitance and open-circuit voltage of the lithium ion battery in real time; the first preset algorithm is a parameter identification algorithm of a least square method based on a forgetting factor.
In specific application, the difference equation of the first-order RC model of the parameter identification algorithm based on the least square method is:
Ek=a1VOCV,k+a2Ek-1+a3Ik+a4Ik-1
aifor the coefficients related to the model parameters, further we obtain:
Figure GDA0002614391750000065
Φkis a system data matrix, θkIs a parameter matrix, ykIs the system output, wherein:
Figure GDA0002614391750000071
therefore, each parameter in the first-order equivalent circuit model can be calculated by the following formula:
Figure GDA0002614391750000072
the calculation flow of the recursive least square method with forgetting factors is as follows:
Figure GDA0002614391750000073
wherein, λ is forgetting factor, KFF-RLS,kFor algorithmic gain, PFF-RLS,k-1An error covariance matrix of the state estimate;
it is noted that the least squares with forgetting factor, although it is possible to identify the battery parameters under fast current excitation conditions, it still faces the challenge of accurately identifying the parameters under current excitation conditions with no or small variations. However, in practical applications, long periods of quiescent and constant current charging are unavoidable. Therefore, the current profile should be more diversified to sufficiently trigger the extreme performance of the battery. To this end, the present patent contemplates an additional iterative process to solve this problem. Under the condition of small current or CC, the least square method with forgetting factor is temporarily stopped, and new parameters are obtained through the weighted average of the parameters of the historical model. The iterative process is designed as:
Figure GDA0002614391750000074
the ohmic internal resistance, the polarization capacitance and the open-circuit voltage of the lithium ion battery can be identified and obtained according to the formula.
In the embodiment, the terminal voltage is calculated according to the first preset algorithm through the first-order equivalent circuit model based on the lithium ion battery, and the ohmic resistance, the polarization internal resistance, the polarization capacitance and the open-circuit voltage of the lithium ion battery are obtained in real time, so that the authenticity and the timeliness of data are ensured, and the accuracy of SOC estimation on the lithium ion battery is further improved.
EXAMPLE III
As shown in fig. 4, this embodiment is a further description of the method steps in the first embodiment. In this embodiment, step S102 includes:
and S1021, analyzing historical charging and discharging data of the lithium ion battery reaching the service life. The data comes from laboratory charge-discharge tests, the test process adopts constant current and constant voltage to fully charge the battery, the battery is stood for more than 1 hour, then the battery is discharged to the cut-off voltage by constant current, the battery is stood for more than 1 hour, the process is circulated, and the experimental current, voltage and discharge capacity are recorded.
According to charging data under different cycle lives, extracting charging time t from a voltage threshold V1 to a voltage threshold V2 under different cycle lives as least square support vector machine input, outputting a training aging model by taking the maximum discharge capacity under each cycle life as least square support vector machine output, wherein the process of the least square support vector machine is as follows:
for the following equations:
Figure GDA0002614391750000081
in the formula, xi ═ xi [ xi ]1 ξ2 … ξl]For the offset vector, C is the weight, the optimal hyperplane can be identified. Furthermore, the following constraints should also be satisfied:
Figure GDA0002614391750000082
the following lagrangian function is defined and the maximum condition of the function is solved and minimized.
Figure GDA0002614391750000083
Wherein alpha isiIs a lagrange multiplier. The optimization conditions can be summarized as follows:
Figure GDA0002614391750000084
this gives:
Figure GDA0002614391750000091
by solving the above equation one can obtain:
Figure GDA0002614391750000092
in the formula, K (x, x)i) In the patent of the invention, a radial basis kernel function is adopted to solve the model as a kernel function, and the expression is as follows:
Figure GDA0002614391750000093
and integrating the charging time t from the voltage threshold V1 to the voltage threshold V2 according to the condition that the battery with the non-end of life is in use, inputting the charging time t into aging for estimation, and obtaining the target estimation parameter, namely the maximum discharge capacity of the battery.
When the more the historical data of the battery is involved in training, the more accurate the estimated value of the maximum discharge capacity of the battery is.
Example four
As shown in fig. 5, this embodiment is a further description of the method steps in the first embodiment. In this embodiment, step S104 includes:
s1041, based on the lithium ion battery first-order equivalent current model, according to ohmic resistance, polarization internal resistance, polarization capacitance and open-circuit voltage which are obtained by identification of a first preset algorithm, according to battery capacity estimated by a second preset algorithm, and by utilizing a third preset algorithm, obtaining the residual electric quantity of the lithium ion battery at the k moment; wherein the third preset algorithm is based on a self-adaptive H infinite filtering algorithm.
In specific application, based on the first-order equivalent current model of the lithium ion battery, according to ohmic resistance, polarization internal resistance, polarization capacitance and open-circuit voltage which are obtained by identification of a first preset algorithm, according to battery capacity estimated by a second preset algorithm, the residual electric quantity of the lithium ion battery at the k moment is obtained by utilizing a third preset algorithm; wherein the third preset algorithm is based on a self-adaptive H infinite filtering algorithm.
The parameter identification process based on the adaptive H-infinity filtering is as follows:
Figure GDA0002614391750000094
wherein:
Figure GDA0002614391750000101
setting initial values of filter correlation, including the time when t is 0State vector
Figure GDA0002614391750000102
State estimation error covariance P+ h,0Initial value Q of covariance matrix of system noise and measurement noiseh,0And Rh,0And a symmetric positive matrix Sh,0h,0
State prior estimation:
Figure GDA0002614391750000103
a priori estimation of error covariance:
Figure GDA0002614391750000104
updating a symmetric positive definite matrix:
Figure GDA0002614391750000105
and (3) information updating:
Figure GDA0002614391750000106
self-adaptive measurement noise matrix:
Figure GDA0002614391750000107
updating H infinite gain:
Figure GDA0002614391750000108
adaptive process noise matrix update:
Figure GDA0002614391750000109
updating the state quantity:
Figure GDA00026143917500001010
updating the covariance matrix:
Figure GDA00026143917500001011
through the steps, the residual capacity of the lithium ion battery at the moment k can be dynamically estimated.
In the embodiment, the residual capacity of the battery is estimated by the self-adaptive H-infinity filtering algorithm according to the open-circuit voltage, the ohmic resistance, the polarization internal resistance, the polarization capacitance and the battery capacity of the lithium ion battery at the current moment, so that the accuracy of estimating the residual capacity of the lithium ion battery after the temperature change of the battery and the aging of the battery is improved.
EXAMPLE five
As shown in fig. 6, the present embodiment provides a lithium ion battery SOC estimation apparatus 100 for performing the method steps in the first embodiment. The present embodiment provides a lithium ion battery SOC estimation device 100, including:
a current and voltage real-time acquisition module 101, configured to acquire current and voltage of a lithium ion battery in real time;
the parameter calculation module 102 is configured to calculate, in real time according to a first preset algorithm, ohmic resistance, polarization internal resistance, polarization capacitance, and open-circuit voltage of the lithium ion battery; the first preset algorithm is an improved least square algorithm with a forgetting factor.
A specific voltage charging time obtaining module 103, configured to obtain a specific voltage charging time of the lithium ion battery;
a capacity value calculation module 104, configured to obtain a maximum discharge capacity of the lithium ion battery according to a second preset algorithm; and the second preset algorithm is based on a least square support vector machine algorithm.
The SOC calculation module 105 is configured to calculate, based on the first-order equivalent circuit model of the lithium ion battery, the current, the terminal voltage, the maximum discharge capacity, and the identification parameter of the lithium ion battery at the time k according to a third preset algorithm, and obtain an SOC of the lithium ion battery at the time k; wherein the third preset algorithm is based on a self-adaptive H infinite filtering algorithm.

Claims (4)

1. A method for estimating SOC of a lithium ion battery is characterized by comprising the following steps:
step 1: according to the current and terminal voltage acquired during the operation of the battery, the ohmic internal resistance, the polarization capacitance and the open-circuit voltage of the lithium ion battery are acquired in real time by adopting an improved least square algorithm with forgetting factors, and the method comprises the following steps:
reading the terminal voltage and the charge and discharge current of the lithium ion battery;
establishing a first-order equivalent circuit model of the lithium ion battery;
based on the first-order equivalent circuit model of the lithium ion battery and current and voltage values acquired in real time, acquiring ohmic internal resistance, polarization capacitance and open-circuit voltage of the lithium ion battery in real time according to an improved least square method with forgetting factors;
the improved least square algorithm with the forgetting factor relates to an additional iterative process, under the condition of low current or CC constant current charging, the least square method with the forgetting factor is temporarily stopped, and new parameters are obtained through weighted average of parameters of a historical model;
step 2: preprocessing the charging voltage data and the capacity value sequence according to the charging voltage data and the capacity value sequence of the lithium ion battery under different cycle lives to obtain charging time (t) in a specific voltage range as a parameter in the step 3; the method for preprocessing the charging voltage data and the capacity value sequence comprises the steps of recording the charging time from a voltage threshold value 1 to a voltage threshold value 2 of a battery under different aging conditions to obtain a charging time sequence (T) in a specific voltage range;
and step 3: running a least square support vector machine (LS-SVM) method of the radial basis kernel function to find an incidence relation, and training the capacity value sequence and the charging time (t) in the specific voltage range in the step 2 to obtain a trained aging model;
and 4, step 4: charging data in normal service life is obtained through a battery management system, and data processing is carried out to obtain a charging time sequence (T) in a specific voltage range;
and 5: inputting the charging time sequence (T) with the specific voltage range in the step (4) into the aging model in the step (3) for prediction to obtain a target battery capacity value;
step 6: and (5) calculating in real time to obtain the SOC of the lithium ion battery by adopting a self-adaptive H infinite filter algorithm according to the ohmic internal resistance, the polarization capacitance and the open-circuit voltage which are obtained in the step (1) and the capacity value obtained in the step (5).
2. The method for estimating the SOC of the lithium ion battery according to claim 1, wherein in step 3, the input of the aging model is a single-cycle specific voltage range charging time, and the output is a current battery capacity value.
3. The method of claim 1, wherein the calculating of the ohmic internal resistance, polarization capacitance, open circuit voltage, capacity, current and terminal voltage of the lithium ion battery according to an adaptive H-infinity filtering algorithm is performed to obtain the SOC of the lithium ion battery in real time.
4. An apparatus for lithium ion battery SOC estimation comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 3 when executing the computer program.
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