CN113917346A - Lithium battery SOC estimation method considering current and voltage deviation - Google Patents

Lithium battery SOC estimation method considering current and voltage deviation Download PDF

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CN113917346A
CN113917346A CN202111097847.2A CN202111097847A CN113917346A CN 113917346 A CN113917346 A CN 113917346A CN 202111097847 A CN202111097847 A CN 202111097847A CN 113917346 A CN113917346 A CN 113917346A
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voltage
time
soc
deviation
current
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郑伟光
李燕青
覃记荣
郭葵
梁立东
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Guilin University of Electronic Technology
Dongfeng Liuzhou Motor Co Ltd
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Guilin University of Electronic Technology
Dongfeng Liuzhou Motor Co Ltd
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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
    • 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]
    • G01R31/3644—Constructional arrangements
    • G01R31/3648—Constructional arrangements comprising digital calculation means, e.g. for performing an algorithm
    • 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
    • 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]
    • G01R31/389—Measuring internal impedance, internal conductance or related variables

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Abstract

The invention discloses a lithium battery SOC estimation method considering current and voltage deviations, which comprises the steps of establishing an equivalent circuit continuous time model based on a second-order equivalent circuit model and a kirchhoff voltage law; discretizing the equivalent circuit continuous time model and establishing an equivalent circuit discrete time model; performing parameter identification and SOC estimation on the equivalent circuit discrete time model based on an extended Kalman filtering algorithm by combining voltage deviation and current deviation; compared with an ampere-hour integration method and a double-expansion Kalman filtering algorithm without considering current and voltage deviations, the method takes the influences of factors such as current deviations and noises into consideration, effectively avoids the dependence on the initial value of the SOC, and has higher estimation precision and better robustness.

Description

Lithium battery SOC estimation method considering current and voltage deviation
Technical Field
The invention relates to the technical field of battery SOC estimation, in particular to a lithium battery SOC estimation method considering current and voltage deviations.
Background
The increasing demand of electric vehicles has prompted technological progress in the battery technology field, and state of charge (SOC) estimation is an important module of an electric vehicle battery management system, and SOC is not a directly measurable quantity and must be estimated for application in a control or battery management system.
Conventional SOC estimation methods include ampere-hour integration, open-circuit voltage, empirical formula and mathematical model, and neural network. The ampere-hour integration method needs to know the initial SOC, and is easy to introduce an accumulated error when the current measurement precision is insufficient, and the open loop calculation mode determines that the initial SOC error and the current measurement accumulated error cannot be corrected; the open-circuit voltage method has high estimation precision when the open-circuit voltage is accurately measured or calculated, but the accurate open-circuit voltage measurement requires the battery to stand for several hours, so the method is difficult to be used for SOC online estimation under actual working conditions; empirical formulas and mathematical model methods are rules summarized under constant-current charging and discharging conditions, and cannot be well suitable for complex discharging working conditions; the neural network method requires a large amount of data for training, and the estimation result is greatly influenced by the training data and the training mode.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the invention provides the lithium battery SOC estimation method considering the current and voltage deviation, and the defect that the battery SOC estimation is inaccurate due to measurement errors of the voltage and the current in a mathematical model estimation method is overcome.
In order to solve the technical problems, the invention provides the following technical scheme: establishing an equivalent circuit continuous time model based on a second-order equivalent circuit model and a kirchhoff voltage law; discretizing the equivalent circuit continuous time model to establish an equivalent circuit discrete time model; and combining the voltage deviation and the current deviation, and performing parameter identification and SOC estimation on the equivalent circuit discrete time model based on an extended Kalman filtering algorithm.
As a preferable aspect of the method for estimating the SOC of the lithium battery considering the current and voltage deviations, the method includes: the second-order equivalent circuit model comprises the step of establishing the second-order equivalent circuit model according to the internal structure and the internal reaction of the circuit, namely, the battery model is equivalent to an internal resistance and an RC ring.
As a preferable aspect of the method for estimating the SOC of the lithium battery considering the current and voltage deviations, the method includes: the function expression of the equivalent circuit continuous time model is as follows:
Figure RE-GDA0003373879830000021
Figure RE-GDA0003373879830000022
wherein SOC is electric quantity; vTIs the equivalent circuit terminal voltage; vOCIs an open circuit voltage; r0Is a series resistor; vC1Is R1C1The terminal voltage of (a);
Figure RE-GDA0003373879830000023
is R2C2The terminal voltage of (a); r1、R2Is the polarization internal resistance; c1、C2Is a polarization capacitor; t is time, CbatIs a capacitor.
As a preferable aspect of the method for estimating the SOC of the lithium battery considering the current and voltage deviations, the method includes: the equivalent circuit discrete time model comprises discretizing a matrix and a vector of the equivalent circuit continuous time model to obtain the following discrete state output equation:
Figure RE-GDA0003373879830000024
therein, SOCk+1The electric quantity at the moment k + 1;
Figure RE-GDA0003373879830000025
is k +1 time C1The terminal voltage of (a);
Figure RE-GDA0003373879830000026
is k +1 time C2The terminal voltage of (a); vb，k+1: voltage deviation at the k +1 moment; t is the run time of one cycle and I is the current in the circuit.
As a preferable aspect of the method for estimating the SOC of the lithium battery considering the current and voltage deviations, the method includes: the voltage deviation and the current deviation include representing the voltage deviation and the current deviation, respectively, by:
VT＝Vm-Vb
I＝Im-Ib
wherein, VbAnd IbRespectively representing said voltage deviation and current deviation, VmAnd ImRespectively, representing corresponding voltages and currents with deviations.
As a preferable aspect of the method for estimating the SOC of the lithium battery considering the current and voltage deviations, the method includes: obtaining a continuous time state space model of the system with the current sensor deviation and a continuous time state space model of the system with the voltage sensor deviation according to the voltage deviation and the current deviation; the continuous-time state space model of the system with current sensor bias is:
Figure RE-GDA0003373879830000031
Figure RE-GDA0003373879830000032
Figure RE-GDA0003373879830000033
Figure RE-GDA0003373879830000034
Figure RE-GDA0003373879830000035
the continuous-time state space model of the system with voltage sensor bias is:
Figure RE-GDA0003373879830000036
Figure RE-GDA0003373879830000037
Figure RE-GDA0003373879830000038
Figure RE-GDA0003373879830000039
Figure RE-GDA00033738798300000310
as a preferable aspect of the method for estimating the SOC of the lithium battery considering the current and voltage deviations, the method includes: also included is a discrete mathematical formula for a continuous-time state space model of a system with voltage sensor bias as follows:
Figure RE-GDA0003373879830000041
Figure RE-GDA0003373879830000042
therein, SOCk+1The electric quantity at the moment k + 1; vb，kIs the voltage deviation at the kth moment;
Figure RE-GDA0003373879830000043
is k time C1The terminal voltage of (a);
Figure RE-GDA0003373879830000044
is k time C2The terminal voltage of (a); SOCkThe power at time k.
As a preferable aspect of the method for estimating the SOC of the lithium battery considering the current and voltage deviations, the method includes: the parameter identification comprises the step of identifying the series resistor R0Internal polarization resistance R1And R2And a polarization capacitor C1And C2As a parameter of the state vector θ, an expression of the state vector θ is available:
θ＝[R0 C1 R1 C1 R2 Uoc]T
combining the RC equation can obtain the following equation of state and measurement equation:
Figure RE-GDA0003373879830000045
the RC equation is as follows:
Figure RE-GDA0003373879830000046
Figure RE-GDA0003373879830000047
where T is the transpose and θ is a constant C at the k-th timeθ(k)＝[-Ik 0 0 0 0 1]，IkIs the current of the equivalent circuit at the k-th time, rkAnd ekIs the system disturbance error, whose corresponding covariance is QkAnd Rk， YKFor the real-time operating voltage measured by the equivalent circuit, g is the measurement function, xkIs a state variable at time K, ukAs an input quantity, θkIs the value of the state vector θ at time k, rk-1The system disturbance error at the moment k-1 is obtained;
and performing online identification on the series resistance, the polarization internal resistance, the polarization capacitance and the open-circuit voltage parameters of the battery according to the state equation and the measurement equation and based on an extended Kalman filtering algorithm.
As a preferable aspect of the method for estimating the SOC of the lithium battery considering the current and voltage deviations, the method includes: the SOC estimation comprises the following steps: measuring battery terminal voltage UocAccording to the obtained UocObtaining initial value SOC by inquiring the relation table, then obtaining initial value of each parameter according to the initial value SOC and the relation table, and further obtaining initial value theta of state variable0(ii) a Step two: based on the initial value theta of the state variable0Determining a coefficient matrix A from a functional expression of the equivalent circuit continuous-time modelk、BkThen, calculating according to the extended Kalman filter algorithm to obtain the estimated value SOC of the residual electric quantity at the momentk(ii) a Step three: based on the initial value theta of the state variable0And obtaining the parameter value R at the moment by using the extended Kalman filtering algorithm0(k)、C1(k)、R1(k)、C2(k)、R2(k)、Uoc(k)(ii) a Step four: according to the parameter value R0(k)、C1(k)、R1(k)、C2(k)、R2(k)、Uoc(k)Updating the coefficient matrix Ak+1、Bk+1And then obtaining the estimated value SOC of the residual electric quantity at the next moment through the extended Kalman filtering algorithmk+1(ii) a Step five: continuously circulating the second step to the fourth step to obtain a real-time SOC optimal estimation value; wherein R is0(k)R at time k0A resistance; c1(k)Is k time C1The capacitance value of (a); c2(k)Is k time C2The capacitance value of (a); r1(k)Is at time k R1A value of (d); r2(k)Is at time k R2A value of (d); u shapeoc(k)Is time k UocThe voltage value of (2).
The invention has the beneficial effects that: compared with an ampere-hour integration method and a double-expansion Kalman filtering algorithm without considering current and voltage deviations, the method takes the influences of factors such as current deviations and noises into consideration, effectively avoids the dependence on the initial value of the SOC, and has higher estimation precision and better robustness.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced 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. Wherein:
fig. 1 is a schematic structural diagram of a second-order equivalent circuit model of a lithium battery SOC estimation method considering current and voltage deviations according to a first embodiment of the present invention;
fig. 2 is a schematic diagram of SOC estimation results with current bias of dual EKFs according to a second embodiment of the present invention;
fig. 3 is a schematic view of a current deviation curve of a lithium battery SOC estimation method considering current and voltage deviations according to a second embodiment of the present invention;
fig. 4 is a schematic diagram of SOC estimation results of dual EKF with voltage deviation according to a second embodiment of the present invention;
fig. 5 is a schematic voltage deviation curve of a lithium battery SOC estimation method considering current and voltage deviations according to a second embodiment of the present invention;
fig. 6 is a schematic diagram of an SOC estimation result of a dual EKF without a voltage deviation according to the lithium battery SOC estimation method considering current and voltage deviations in the second embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
Referring to fig. 1, a first embodiment of the present invention provides a method for estimating SOC of a lithium battery considering current and voltage deviations, including:
s1: and establishing an equivalent circuit continuous time model based on the second-order equivalent circuit model and the kirchhoff voltage law.
Establishing a second-order equivalent circuit model according to the internal structure and the internal reaction of the circuit, namely, the battery model is equivalent to an internal resistance and an RC ring; the more the RC rings are, the more the dynamic characteristics of the battery can be simulated, and in consideration of practical use, in order to avoid excessive calculation amount, two RC rings are selected in the embodiment, as shown in FIG. 1, wherein V istIs the battery operating voltage; preferably, the dual RC ring model has two RC rings, which better simulates the dynamic performance of the battery relative to a single RC ring model.
By using standard circuit analysis, the dynamic behavior of the system can be extracted; by applying Kirchhoff's Voltage Law (KVL) over the entire loop of the circuit, the following expression of the circuit termination voltage as a function of the circuit internal components can be obtained:
VT＝VOC(soc)-R0I-VC1-VC2
by applying KVL to both RC branches, the following equation is derived
Figure RE-GDA0003373879830000071
Figure RE-GDA0003373879830000072
And combining the expressions to obtain a function expression of the equivalent circuit continuous time model:
Figure RE-GDA0003373879830000073
Figure RE-GDA0003373879830000074
wherein SOC is electric quantity; vTIs the equivalent circuit terminal voltage; vOCIs an open circuit voltage; r0Is a series resistor; vC1Is R1C1The terminal voltage of (a); vC2Is R2C2The terminal voltage of (a); r1、R2Is the polarization internal resistance; c1、C2Is a polarization capacitor; t is time, CbatIs a capacitor.
S2: and discretizing the equivalent circuit continuous time model to establish an equivalent circuit discrete time model.
In order to estimate the SOC by combining with a Kalman filtering algorithm, a continuous time model needs to be discretized, namely a matrix and a vector of the equivalent circuit continuous time model are discretized; to convert a continuous-time model to a discrete-time state model, the present embodiment applies a closed-form discretization formula (shown below) to the matrices and vectors of the continuous-time model to obtain the following discrete-state output equations:
Figure RE-GDA0003373879830000081
therein, SOCk+1The electric quantity at the moment k + 1;
Figure RE-GDA0003373879830000082
is k +1 time C1The terminal voltage of (a);
Figure RE-GDA0003373879830000083
is k +1 time C2The terminal voltage of (a); vb，k+1: voltage deviation at the k +1 moment; t is the run time of one cycle and I is the current in the circuit.
S3: and combining the voltage deviation and the current deviation, and performing parameter identification and SOC estimation on the equivalent circuit discrete time model based on an extended Kalman filtering algorithm.
(1) In setting up an estimation problem, physical phenomena existing under real conditions need to be considered in the model as much as possible, and the present embodiment considers the voltage deviation and the current deviation respectively, specifically, the voltage deviation and the current deviation are represented by the following formulas:
VT＝Vm-Vb
I＝Im-Ib
wherein, VbAnd IbRespectively representing voltage deviation and current deviation, VmAnd ImRespectively, representing corresponding voltages and currents with deviations.
Obtaining a continuous time state space model of the system with the current sensor bias and a continuous time state space model of the system with the voltage sensor bias according to the voltage bias and the current bias;
the continuous-time state space model of the system with current sensor bias is:
Figure RE-GDA0003373879830000084
Figure RE-GDA0003373879830000085
Figure RE-GDA0003373879830000086
Figure RE-GDA0003373879830000087
Figure RE-GDA0003373879830000091
the continuous-time state space model for a system with voltage sensor bias is:
Figure RE-GDA0003373879830000092
Figure RE-GDA0003373879830000093
Figure RE-GDA0003373879830000094
Figure RE-GDA0003373879830000095
Figure RE-GDA0003373879830000096
the discrete mathematical formula for the continuous-time state space model of the system with voltage sensor bias is as follows:
Figure RE-GDA0003373879830000097
Figure RE-GDA0003373879830000098
therein, SOCk+1The electric quantity at the moment k + 1; vb，kIs the voltage deviation at the kth moment;
Figure RE-GDA0003373879830000099
is k time C1The terminal voltage of (a);
Figure RE-GDA00033738798300000910
is k time C2The terminal voltage of (a); SOCkThe power at time k.
(2) And performing parameter identification on the equivalent circuit discrete time model based on an extended Kalman filtering algorithm.
Will be connected with a resistor R in series0Internal polarization resistance R1And R2And a polarization capacitor C1And C2As a parameter of the state vector θ, an expression of the state vector θ is available:
θ＝[R0 C1 R1 C1 R2 Uoc]T
the default theta value is slowly changed in practical application, and the following state equation and measurement equation can be obtained by combining the RC equation:
Figure RE-GDA00033738798300000911
the RC equation is:
Figure RE-GDA0003373879830000101
Figure RE-GDA0003373879830000102
where θ is a constant C at the k-th timeθ(k)＝[-Ik 0 0 0 0 1]，IkIs the current of the equivalent circuit at the k-th time, rkAnd ekIs the system disturbance error, whose corresponding covariance is QkAnd Rk，YKFor the real-time operating voltage measured by the equivalent circuit, g is the measurement function, xkIs a state variable at time K, ukAs an input quantity, θkIs the value of the state vector θ at time k, rk-1Is the systematic disturbance error at time k-1.
According to a state equation and a measurement equation and based on an extended Kalman filtering algorithm, carrying out online identification on parameters of ohmic internal resistance, polarization capacitance and open-circuit voltage of the battery, and specifically comprising the following steps:
firstly, defining initial values of state variables:
covariance: p (1) ═ 0
Parameter covariance: PT (1) ═ 0
Full-charge electric quantity of the battery: x (1) ═ 0.98
VC1Voltage across: x (3) ═ 0
VC2Voltage across: x (4) ═ 0
Secondly, performing primary prediction of state variables:
θk|k-1＝θk-1|k-1
wherein, thetak|k-1Is a predicted value of the state variable;
thirdly, parameter error variance matrix after one-step prediction:
Pk|k-1＝Pk-1|k-1+Qk-1
fourthly, obtaining Kalman gain Kg(k)：
Figure RE-GDA0003373879830000103
Wherein the coefficient matrix HkThe calculation method of (2) is as follows:
Figure RE-GDA0003373879830000104
wherein, Pk|k-1Is a predicted parameter error matrix;
acquiring an optimal estimation value of the parameters:
θk|k＝θk|k-1+Kg(k)(Yk-Yk|k-1)
wherein Y iskFor real-time operating voltage, Y, measured by an equivalent circuitk|k-1To predict the voltage value, θk|kFor optimal estimation of the parameter, Kg(k)Is a kalman gain matrix.
Updating the state quantity estimation error variance matrix to prepare for the next generation of iterative computation:
Pk|k＝(I-Kg(k)Hk)Pk|k-1
and (6) realizing parameter identification through continuous loop iteration from the step two to the step six.
(3) And (5) SOC estimation.
According to the mathematical principle of Kalman filtering, the method is only suitable for a linear system, and the lithium ion battery researched by the invention has the characteristic of high nonlinearity, so that the SOC is estimated by adopting a Double Extended Kalman Filtering (DEKF) algorithm based on the battery parameter online identification method of the extended Kalman filtering algorithm, and the specific steps are as follows:
measuring battery terminal voltage Uoc(0)Obtaining U from off-line experimentsocObtaining initial value SOC through inquiring the relation table, then obtaining each parameter initial value according to the initial value SOC and the relation table, and further obtaining state variable initial value theta0；
Based on initial value theta of state variable0Determining the coefficient matrix A by the function expression of the equivalent circuit continuous time modelk、BkThen, the estimated value SOC of the remaining capacity at the moment is calculatedk；
③ based on the initial value theta of the state variable0Operating the extended Kalman filtering algorithm to obtain the parameter value R at the moment0(k)、C1(k)、R1(k)、C2(k)、R2(k)、Uoc(k)；
Fourthly, according to the parameter value R0(k)、C1(k)、R1(k)、C2(k)、R2(k)、Uoc(k)Updating the coefficient matrix Ak+1、 Bk+1Obtaining the estimated value SOC of the residual electric quantity at the next moment by the extended Kalman filtering algorithmk+1；
And fifthly, continuously and circularly executing the steps from the second step to the fourth step to obtain the real-time SOC optimal estimation value.
Wherein R is0(k)R at time k0A resistance; c1(k)Is k time C1The capacitance value of (a); c2(k)Is k time C2The capacitance value of (a); r1(k)Is at time k R1A value of (d); r2(k)Is at time k R2A value of (d); u shapeoc(k)Is time k UocThe voltage value of (2).
Example 2
In order to verify and explain the technical effect adopted in the method, the embodiment performs model verification on the derived second-order equivalent circuit model, and compares the test results by means of scientific demonstration to verify the real effect of the method.
This verification is accomplished by generating actual SOC and terminal voltage (with process noise) data from a "real" model of a third order ECM, given time and battery current data as inputs; the additional noise is zero-mean gaussian noise with a standard deviation of 0.1% of the maximum value of the corresponding signal; then using the simulated 'real' data for realizing the extended Kalman filtering algorithm, verifying by using a third-order circuit battery model, and simulating the used parameters: rs＝0.024Ω，R1＝0.015Ω，R2＝0.0015Ω，C1＝1000F， C22500F; the results are shown in FIGS. 2 to 6.
Comparing fig. 2 and fig. 4 with fig. 6, it can be seen that the method is more suitable for practical situations and the result is more accurate in the estimation process of the SOC of the lithium battery.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.

Claims (9)

1. A lithium battery SOC estimation method considering current and voltage deviation is characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
establishing an equivalent circuit continuous time model based on a second-order equivalent circuit model and a kirchhoff voltage law;
discretizing the equivalent circuit continuous time model to establish an equivalent circuit discrete time model;
and combining the voltage deviation and the current deviation, and performing parameter identification and SOC estimation on the equivalent circuit discrete time model based on an extended Kalman filtering algorithm.
2. The method for estimating the SOC of a lithium battery considering deviation of current and voltage as set forth in claim 1, wherein: the second-order equivalent circuit model comprises,
and establishing the second-order equivalent circuit model according to the internal structure and the internal reaction of the circuit, namely, the battery model is equivalent to an internal resistance and an RC ring.
3. The method for estimating the SOC of a lithium battery considering a current and a voltage deviation as claimed in claim 1 or 2, wherein: the function expression of the equivalent circuit continuous time model is as follows:
Figure FDA0003269707640000011
Figure FDA0003269707640000012
wherein SOC is electric quantity; vTIs the equivalent circuit terminal voltage; vOCIs an open circuit voltage; r0Is a series resistor; vC1Is R1C1The terminal voltage of (a);
Figure FDA0003269707640000013
is R2C2The terminal voltage of (a); r1、R2Is the polarization internal resistance; c1、C2Is a polarization capacitor; t is time, CbatIs a capacitor.
4. The method for estimating the SOC of a lithium battery considering deviation of current and voltage as set forth in claim 3, wherein: the discrete-time model of the equivalent circuit includes,
discretizing the matrix and the vector of the equivalent circuit continuous time model to obtain the following discrete state output equation:
Figure FDA0003269707640000014
therein, SOCk+1The electric quantity at the moment k + 1;
Figure FDA0003269707640000015
is k +1 time C1The terminal voltage of (a);
Figure FDA0003269707640000016
is k +1 time C2The terminal voltage of (a); vb,k+1: voltage deviation at the k +1 moment; t is the run time of one cycle and I is the current in the circuit.
5. The method for estimating the SOC of a lithium battery considering deviation of current and voltage as set forth in claim 4, wherein: the voltage deviation and the current deviation comprise,
the voltage deviation and the current deviation are respectively expressed by the following formulas:
VT＝Vm-Vb
I＝Im-Ib
wherein, VbAnd IbRespectively representing said voltage deviation and current deviation, VmAnd ImRespectively, representing corresponding voltages and currents with deviations.
6. The method for estimating the SOC of a lithium battery considering deviation of current and voltage as set forth in claim 5, wherein: also comprises the following steps of (1) preparing,
obtaining a continuous time state space model of the system with the current sensor deviation and a continuous time state space model of the system with the voltage sensor deviation according to the voltage deviation and the current deviation;
the continuous-time state space model of the system with current sensor bias is:
Figure FDA0003269707640000021
Figure FDA0003269707640000022
Figure FDA0003269707640000023
Figure FDA0003269707640000024
Figure FDA0003269707640000025
the continuous-time state space model of the system with voltage sensor bias is:
Figure FDA0003269707640000026
Figure FDA0003269707640000027
Figure FDA0003269707640000028
Figure FDA0003269707640000029
Figure FDA0003269707640000031
7. the method for estimating the SOC of a lithium battery considering deviation of current and voltage as set forth in claim 6, wherein: also comprises the following steps of (1) preparing,
the discrete mathematical formula for the continuous-time state space model of the system with voltage sensor bias is as follows:
Figure FDA0003269707640000032
Figure FDA0003269707640000033
therein, SOCk+1The electric quantity at the moment k + 1; vb,kIs the voltage deviation at the kth moment;
Figure FDA0003269707640000034
is k time C1The terminal voltage of (a);
Figure FDA0003269707640000035
is k time C2The terminal voltage of (a); SOCkThe power at time k.
8. The method for estimating the SOC of a lithium battery considering deviation of current and voltage as set forth in claim 7, wherein: the parameter identification includes the identification of the parameter,
the series resistor R0Internal polarization resistance R1And R2And a polarization capacitor C1And C2As a parameter of the state vector θ, an expression of the state vector θ is available:
θ＝[R0 C1 R1 C1 R2 Uoc]T
combining the RC equation can obtain the following equation of state and measurement equation:
Figure FDA0003269707640000036
the RC equation is as follows:
Figure FDA0003269707640000037
Figure FDA0003269707640000038
where T is the transpose and θ is a constant C at the k-th timeθ(k)＝[-Ik 0 0 0 0 1]，IkIs the current of the equivalent circuit at the k-th time, rkAnd ekIs the system disturbance error, whose corresponding covariance is QkAnd Rk，YKFor the real-time operating voltage measured by the equivalent circuit, g is the measurement function, xkAt time KState variable, ukAs an input quantity, θkIs the value of the state vector θ at time k, rk-1The system disturbance error at the moment k-1 is obtained;
and performing online identification on the series resistance, the polarization internal resistance, the polarization capacitance and the open-circuit voltage parameters of the battery according to the state equation and the measurement equation and based on an extended Kalman filtering algorithm.
9. The method for estimating the SOC of a lithium battery considering deviation of current and voltage as set forth in claim 8, wherein: the SOC estimation includes a time-of-flight estimation,
the method comprises the following steps: measuring battery terminal voltage UocAccording to the obtained UocObtaining initial value SOC by inquiring the relation table, then obtaining initial value of each parameter according to the initial value SOC and the relation table, and further obtaining initial value theta of state variable0；
Step two: based on the initial value theta of the state variable0Determining a coefficient matrix A from a functional expression of the equivalent circuit continuous-time modelk、BkThen, calculating according to the extended Kalman filter algorithm to obtain the estimated value SOC of the residual electric quantity at the momentk；
Step three: based on the initial value theta of the state variable0And obtaining the parameter value R at the moment by using the extended Kalman filtering algorithm0(k)、C1(k)、R1(k)、C2(k)、R2(k)、Uoc(k)；
Step four: according to the parameter value R0(k)、C1(k)、R1(k)、C2(k)、R2(k)、Uoc(k)Updating the coefficient matrix Ak+1、Bk+1And then obtaining the estimated value SOC of the residual electric quantity at the next moment through the extended Kalman filtering algorithmk+1；
Step five: continuously circulating the second step to the fourth step to obtain a real-time SOC optimal estimation value;
wherein R is0(k)R at time k0A resistance; c1(k)Is k time C1The capacitance value of (a);C2(k)is k time C2The capacitance value of (a); r1(k)Is at time k R1A value of (d); r2(k)Is at time k R2A value of (d); u shapeoc(k)Is time k UocThe voltage value of (2).
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