CN106909716B - Lithium iron phosphate battery modeling and SOC estimation method considering capacity loss - Google Patents

Lithium iron phosphate battery modeling and SOC estimation method considering capacity loss Download PDF

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CN106909716B
CN106909716B CN201710044564.9A CN201710044564A CN106909716B CN 106909716 B CN106909716 B CN 106909716B CN 201710044564 A CN201710044564 A CN 201710044564A CN 106909716 B CN106909716 B CN 106909716B
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iron phosphate
lithium iron
phosphate battery
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李军徽
高凤杰
严干贵
穆钢
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JILIN POWER SUPPLY COMPANY OF STATE GRID JILINSHENG ELECTRIC POWER SUPPLY Co.
Northeast Electric Power University
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Abstract

The invention relates to a method for modeling and SOC estimation of a lithium iron phosphate battery considering capacity loss, which is provided based on the fact that the working state of the conventional lithium iron phosphate battery is influenced by various factors such as temperature, current, cycle frequency, discharge depth and the like, so that the modeling process is very complicated; meanwhile, an Extended Kalman Filter (EKF) method is used for solving the SOC estimation problem of the lithium iron phosphate battery brought by uncertain noise. Has the advantages of simple, scientific and reasonable method, high application value, good effect and the like.

Description

Lithium iron phosphate battery modeling and SOC estimation method considering capacity loss
Technical Field
The invention relates to the technical field of power supply application, in particular to a method for modeling and SOC (state of charge) estimation of a lithium iron phosphate battery considering capacity loss.
Background
Electrochemical energy storage represented by a lithium battery has the advantages of high controllability and high module degree, so that the energy density is high and the conversion efficiency is high on the whole, and the electrochemical energy storage is generally researched and applied in the field of new energy power generation. At present, a plurality of lithium battery energy storage demonstration projects are established in China, such as Zhang Jiakozhou north county in Hebei (a lithium phosphate battery device with 14MW/63WMh and a liquid flow energy storage device with 2MW/8 MWh), Shenzhen Baoqing energy storage power station (a 4MW/16MW lithium iron phosphate battery energy storage system is installed), and the like. According to relevant domestic policies and documents, the domestic energy storage market shows a rapid development trend, the lithium battery energy storage field mainly deals with the lithium battery energy storage technology with high safety, low cost and long service life, and the experimental demonstration of the technology is realized later, so that the development and the application of the lithium battery technology are greatly promoted.
Around the key problems of modeling, control strategy design, capacity configuration and the like of the lithium battery energy storage system, scholars at home and abroad obtain some research achievements. In the aspect of energy storage modeling research, the battery consists of a positive electrode, a negative electrode and an electrolyte, the charging and discharging process is an electrochemical process, and the voltage and the current of the battery are related to factors such as resistance, polarization, temperature and the like of active substances in the battery and are very complex. In the whole life cycle of the working operation of the lithium battery, the capacity of the lithium battery can generate corresponding loss along with the continuous charging and discharging, and the aging phenomenon of the lithium battery can occur. Therefore, the capacity evaluation of the lithium battery is also necessary, and the working state of the battery can be timely adjusted by workers. And at present, the price of battery energy storage is generally expensive, and how to establish an effective battery model for analyzing the technical-economic characteristics of an energy storage system when the battery model is applied to new energy is a very critical problem.
Disclosure of Invention
The invention aims to solve the technical problem of providing a lithium iron phosphate battery modeling and SOC state estimation method considering battery capacity loss, which is characterized by comprising the following steps of:
1) mathematical model of lithium iron phosphate battery equivalent circuit
Adopting a Thevenin equivalent circuit model, and establishing a model state equation as a formula (1) according to a second-order RC equivalent circuit model and by kirchhoff's theorem:
Figure GDA0002254928570000011
in the formula: u shapebFor the load terminal voltage of lithium iron phosphate batteries, CuseIs the effective capacity of the iron phosphate battery, i.e. the available capacity of the lithium iron phosphate battery, IbIs the operating current of the lithium iron phosphate battery, SOC is the state of charge, U, of the lithium iron phosphate batteryocThe open-circuit voltage of the lithium iron phosphate battery is a nonlinear function of SOC and is represented by a controllable voltage source RsThe ohmic resistance of the lithium iron phosphate battery, two RC links, R1、C1And R2、C2Respectively represents the electrochemical polarization and concentration polarization processes in the operation of the lithium iron phosphate battery, U1，U2Indicating its corresponding voltage valueη is the charge-discharge efficiency of the lithium iron phosphate battery;
the equivalent circuit model of the lithium iron phosphate battery shows that the left and right circuit networks are coupled through SOC (state of charge), SOC is an important factor for connecting the two parts, and the state equation (1) of the model shows that the output voltage of the lithium iron phosphate battery is determined by the open-circuit voltage and the polarization voltage of the lithium iron phosphate battery, wherein the polarization voltage of the lithium iron phosphate battery is directly related to the corresponding resistance, capacitance and current value, and the real-time available capacity (C) of the lithium iron phosphate battery is accurately determineduse) Estimating SOC, an open-circuit voltage value, a resistance value and a capacitance value is basic work of iron phosphate lithium battery modeling;
2) identification of relevant parameters of lithium iron phosphate battery model
Because the working state of the lithium iron phosphate battery is influenced by factors such as discharge depth, cycle times, capacity attenuation and the like, and the equivalent circuit model parameters of the lithium iron phosphate battery change along with the changes of loads and external environments, in order to obtain a more reliable model, the lithium iron phosphate battery needs to be tested under the condition of multiple factors during off-line modeling, and a parameter data relational expression is established;
SOC is the most important influence factor of all parameters of a resistance-capacitance model, the determination of the functional relation between impedance parameters and SOC is the most basic and important part of resistance-capacitance modeling work under the standard running state condition of the lithium iron phosphate battery, and the lithium iron phosphate battery U in the normal working environmentocThe corresponding relation with SOC is stable and is slightly influenced by temperature, therefore, UocIs uniquely determined by SOC, and the relation is obtained by fitting a function;
resistance-capacitance parameters in the model can be obtained by the following method, under different SOC, the initial value can be set to be 0.2, the step length is 0.05, no-load loading discharge and charge experiments are carried out on the resistance-capacitance parameters, when the lithium iron phosphate battery carries out discharge experiments by no-load state actions, the voltage of the lithium iron phosphate battery can generate a period of abrupt drop, the change of the polarization voltage of the lithium iron phosphate battery is very small and ignored at the moment, and the main reason for causing the change is the ohmic resistance R of the lithium iron phosphate batterysVoltage drop caused by this data change to ohm inside the batteryThe internal resistance is estimated, and the terminal voltage of the battery enters an exponential-like change period because of the polarization voltage U on the RC circuit of the battery1，U2The slow decrease results in a period of time that is considered to correspond to a zero state, described by equation (2):
Figure GDA0002254928570000021
wherein U isbRepresents the terminal voltage, U, of a lithium iron phosphate batteryARepresenting the terminal voltage a, b of the lithium iron phosphate battery at the point A, which are parameters to be fitted, and fitting the formula (2) to obtain corresponding a, b, tau1，τ2And estimating and calculating the resistance and the capacitance of the RC circuit by using the value, wherein the value is specifically represented by formula (3):
Figure GDA0002254928570000031
Ibrepresents the operating current, tau, of a lithium iron phosphate battery1，τ2Is a parameter to be fitted, two RC links, R1、C1And R2、C2Respectively representing electrochemical polarization and concentration polarization processes in the operation of the lithium iron phosphate battery;
accordingly, the corresponding resistance and capacitance in the charging process are estimated by using the formula (4), the capacitance and the resistance of the lithium iron phosphate battery under different SOC are obtained by analogy, the R, C values under different states are obtained by carrying out spline interpolation on the capacitance and the resistance,
Figure GDA0002254928570000032
3) evaluation of available capacity of lithium iron phosphate battery
The service life of the lithium iron phosphate battery is limited, and with the continuous action of charging and discharging in the life cycle of the lithium iron phosphate battery, the loss of lithium ions and the decline of active materials in the lithium iron phosphate battery can cause the irreversible capacity loss in the lithium iron phosphate battery and directly influence the service life of the lithium iron phosphate battery, so that the real-time capacity evaluation of the lithium iron phosphate battery is carried out, the real-time state of the lithium iron phosphate battery can be correctly known, and the method has a positive effect on the estimation of the state of the lithium iron phosphate battery at a certain,
according to the maximum charge-discharge cycle number N corresponding to the operation of the lithium iron phosphate battery at delta SOC ═ xm|ΔSOC＝xFitting the relation of the data, wherein the fitting function is a formula (5), and calculating the maximum charge-discharge cycle number of the lithium iron phosphate battery under a certain charge-discharge cycle depth in the life cycle of the lithium iron phosphate battery according to the formula (5)
The fitting function of the maximum charge-discharge cycle times of the lithium iron phosphate battery when the lithium iron phosphate battery operates at different delta SOC is as follows (5):
Figure GDA0002254928570000033
wherein: Δ SOC ═ x, Nm|ΔSOC＝xRepresents the maximum number of charge-discharge cycles;
evaluating the available capacity in the life cycle of the lithium iron phosphate battery to obtain that the maximum charging and discharging cycle times of the lithium iron phosphate battery corresponding to the life cycles of different delta SOCs are different, the charging and discharging cycle times of the lithium iron phosphate battery under the shallow charging and shallow discharging environment are more, and the cycle charging and discharging cycle times of each delta SOC in the charging and discharging process of the lithium iron phosphate battery are converted according to the formula (6) corresponding to the cycle times under the full charging and discharging;
Figure GDA0002254928570000034
in the formula: n is a radical ofm(x) The maximum cycle number of the lithium iron phosphate battery is when the charging and discharging depth of the lithium iron phosphate battery is equal to x, wherein x belongs to (0, 1); n is a radical ofm(1) α (x) represents the equivalent cycle depth for the maximum number of cycles for a lithium iron phosphate battery when the lithium iron phosphate battery has a charge-discharge depth equal to 1.
Setting n times of charge and discharge at a certain time, and the depth of charge and discharge is x0、x1、…、xnAnd adding the equivalent charge-discharge coefficients under different charge-discharge depths to obtain the lithium iron phosphate batteryThe equivalent number of charge and discharge times is as follows (7):
Figure GDA0002254928570000041
wherein Nm' represents an equivalent charge-discharge coefficient;
the state of health (SOH) of a lithium iron phosphate battery, also referred to as the state of life of the lithium iron phosphate battery, is defined as the ratio of the capacity of the lithium iron phosphate battery discharged from a full charge state to a cut-off voltage at a certain rate to its nominal capacity, reflecting the life status of the lithium iron phosphate battery, and is defined as formula (8):
Figure GDA0002254928570000042
in the formula, CCapicityIndicates the nominal capacity, C, of the lithium iron phosphate batteryuseRepresenting the available capacity of the lithium iron phosphate battery;
the available capacity of the lithium iron phosphate battery at the time t is measured by equation (9):
Figure GDA0002254928570000043
gamma is a constant, which means the percentage of the maximum value of the capacity loss allowed by the normal work of the lithium iron phosphate battery, namely the maximum value of SOH, is 0.3, the SOH reflects the health state of the lithium iron phosphate battery, represents the aging degree of the lithium iron phosphate battery, the change range is 0-100%, when the SOH is reduced to 20-30%, the function of the lithium iron phosphate battery basically fails, and the basic charge and discharge tasks cannot be completed;
4) state estimation of SOC using EKF algorithm
From steps 1) -3), SOC is an important parameter in the running process of the lithium iron phosphate battery, and the state of charge estimation of the lithium iron phosphate battery is the guarantee of safe and reliable running of a lithium iron phosphate battery pack in an energy storage device, so that the real-time SOC of the lithium iron phosphate battery is accurately estimated, and the real-time control strategy of the lithium iron phosphate battery is conveniently adjusted;
the Kalman filtering algorithm is composed of a state equation, an output equation and statistical characteristics of system process noise and observation noise, states or parameters needing to be estimated are obtained according to the state equation and the output equation of the system, the SOC of the lithium iron phosphate battery can be optimally estimated in the minimum variance, prediction and estimation of the lithium iron phosphate battery at a certain future moment are facilitated, the Kalman filtering algorithm is a state equation utilizing a linear system, the lithium iron phosphate battery is a nonlinear model, the nonlinear model of the lithium iron phosphate battery is subjected to Kalman filtering algorithm (EKF) expansion, and the real-time SOC state quantity of the battery is estimated by adopting EKF:
based on the equivalent mathematical model of the lithium iron phosphate battery, establishing a Kalman filtering state equation and an output equation of the lithium iron phosphate battery:
equation of state (10):
Figure GDA0002254928570000051
output equation (11):
ub(k)＝uoc(k)-i(k)×Rs(k)-u1(k)-u2(k)+v(k) (11)
corresponding to the general form (12) of the Kalman filtering equation of state, respectively
Figure GDA0002254928570000052
In the formula: u shapebFor the load terminal voltage of lithium iron phosphate batteries, CuseIs the effective capacity of the lithium iron phosphate battery, i.e. the available capacity of the lithium iron phosphate battery, IbIs the operating current of the lithium iron phosphate battery, SOC represents the state of charge of the lithium iron phosphate battery, UocThe open-circuit voltage of the lithium iron phosphate battery is a nonlinear function of SOC and is represented by a controllable voltage source RsThe ohmic resistance of the lithium iron phosphate battery, two RC links, R1、C1And R2、C2Respectively representing operation of lithium iron phosphate batteriesElectrochemical and concentration polarization process, U1，U2Representing the corresponding voltage value, η representing the charging and discharging efficiency of the lithium iron phosphate battery, w (k) representing the system error, v (k) representing the empirical error;
carrying out SOC estimation in real time:
where k | k-1 represents the result of the last state prediction, k-1| k-1 represents the optimal result at the last time, P (k), Q (k), R (k) corresponds to the covariance of X (k), w (k), v (k),
the Kalman filtering start must select a good initial value, which includes three state parameters, SOC (k), U1(k)，U2(k) SOC (k) is obtained from the last time of last operation as an initial value, while the lithium iron phosphate battery has little effect immediately after operation, and the polarization voltage is considered to be 0, and for the covariance q (k), r (k), defined as:
Figure GDA0002254928570000061
further comprises the following steps:
Figure GDA0002254928570000062
Figure GDA0002254928570000063
R(0)＝0.001。
the invention relates to a lithium iron phosphate battery modeling and SOC estimation method considering capacity loss, which is provided based on the fact that the working state of the existing lithium iron phosphate battery is influenced by various factors such as temperature, current, cycle frequency, discharge depth and the like, so that the modeling process is very complicated; meanwhile, an Extended Kalman Filter (EKF) method is used for solving the SOC estimation problem of the lithium iron phosphate battery brought by uncertain noise. Has the advantages of simple, scientific and reasonable method, high application value, good effect and the like.
Drawings
FIG. 1 is a model of an equivalent circuit of a lithium iron phosphate battery;
FIG. 2 is a curve of a constant-current no-load loading discharge and charge experiment of a lithium iron phosphate battery;
FIG. 3 is a graph of the relationship between different Δ SOC and the maximum number of charge and discharge cycles;
FIG. 4 is a flow chart of a Kalman filtering algorithm;
FIG. 5 is a graph showing the relationship between the open-circuit voltage and the SOC of a lithium iron phosphate battery;
FIG. 6 discharge curves after different cycle numbers;
FIG. 71000 cycles later without considering the variation after capacity loss and verification;
FIG. 8 is a graph of partial current versus time;
fig. 9 is a graph of terminal voltage versus time for a lithium iron phosphate battery;
figure 10 lithium iron phosphate battery SOC versus time curves.
Detailed Description
The lithium iron phosphate battery modeling and SOC estimation method taking capacity loss into consideration according to the present invention will be further described with reference to the drawings and examples.
The invention relates to a lithium iron phosphate battery modeling and SOC state estimation method considering battery capacity loss, which comprises the following steps of:
1) mathematical model of lithium iron phosphate battery equivalent circuit
Because the energy storage of the lithium iron phosphate battery is an electrochemical reaction process, the lithium iron phosphate battery is difficult to describe in detail by adopting a conventional physical model, the modeling of an energy storage system is based on an application scene, a simple model cannot reflect the characteristics of the lithium iron phosphate battery, an excessively complex model can greatly increase the complexity of solving and applying a control strategy, and a more modeling method used in the existing system carries out equivalent circuit modeling according to the dynamic characteristics and the external special effect performance in the lithium iron phosphate battery.
External characteristic equivalent circuit modeling is a simple and effective method for modeling electrochemical cells. The equivalent circuit model simulates the transient-stable characteristic of the battery to the outside by forming a circuit network through electric elements such as a voltage source, a capacitor, a resistor, an inductor and the like, has the advantages of simple modeling method, easy parameter identification, high precision, convenient fusion of various factors, easy mathematical analysis, strong general applicability and the like, and is the most widely applied modeling method in the field of electrical engineering.
As shown in fig. 1, a davinan equivalent circuit model is adopted, and according to a second-order RC equivalent circuit model, a model state equation is established by kirchhoff's theorem as formula (1):
Figure GDA0002254928570000071
in the formula: u shapebFor the load terminal voltage of lithium iron phosphate batteries, CuseIs the effective capacity of the iron phosphate battery, i.e. the available capacity of the lithium iron phosphate battery, IbIs the operating current of the lithium iron phosphate battery, SOC is the state of charge, U, of the lithium iron phosphate batteryocThe open-circuit voltage of the lithium iron phosphate battery is a nonlinear function of SOC and is represented by a controllable voltage source RsThe ohmic resistance of the lithium iron phosphate battery, two RC links, R1、C1And R2、C2Respectively represents the electrochemical polarization and concentration polarization processes in the operation of the lithium iron phosphate battery, U1，U2The corresponding voltage value is shown, and η is the charge-discharge efficiency of the lithium iron phosphate battery;
the equivalent circuit model of the lithium iron phosphate battery shows that the left and right circuit networks are coupled through SOC (state of charge), SOC is an important factor for connecting the two parts, and the state equation (1) of the model shows that the output voltage of the lithium iron phosphate battery is determined by the open-circuit voltage and the polarization voltage of the lithium iron phosphate battery, wherein the polarization voltage of the lithium iron phosphate battery is directly related to the corresponding resistance, capacitance and current value, and the real-time available capacity (C) of the lithium iron phosphate battery is accurately determineduse) Estimating SOC, an open-circuit voltage value, a resistance value and a capacitance value is basic work of iron phosphate lithium battery modeling;
2) identification of relevant parameters of lithium iron phosphate battery model
Because the working state of the lithium iron phosphate battery is influenced by factors such as discharge depth, cycle times, capacity attenuation and the like, and the equivalent circuit model parameters of the lithium iron phosphate battery change along with the changes of loads and external environments, in order to obtain a more reliable model, the lithium iron phosphate battery needs to be tested under the condition of multiple factors during off-line modeling, and a parameter data relational expression is established;
SOC is the most important influence factor of all parameters of a resistance-capacitance model, the determination of the functional relation between impedance parameters and SOC is the most basic and important part of resistance-capacitance modeling work under the standard running state condition of the lithium iron phosphate battery, and the lithium iron phosphate battery U in the normal working environmentocThe corresponding relation with SOC is stable and is slightly influenced by temperature, therefore, UocIs uniquely determined by SOC, and the relation is obtained by fitting a function;
as shown in fig. 2, the resistance-capacitance parameters in the model can be obtained by setting the initial value to 0.2 and the step length to 0.05 under different SOCs, and performing no-load charging and discharging experiments on the parameters, wherein when the lithium iron phosphate battery performs the discharging experiments in the no-load state, the voltage of the lithium iron phosphate battery generates a steep drop period (OA period), the change of the polarization voltage of the lithium iron phosphate battery is very small and negligible, and the main cause of the change is the ohmic resistance R of the lithium iron phosphate batterysThe voltage drop caused by the voltage drop is used for estimating the internal ohmic internal resistance of the battery according to the data change, and then the terminal voltage of the battery enters an exponential-like change period (AB period) because of the polarization voltage U on the RC circuit of the battery1，U2The slow decrease results in a period of time that is considered to correspond to a zero state, described by equation (2):
Figure GDA0002254928570000081
wherein U isbRepresents the terminal voltage, U, of a lithium iron phosphate batteryADenotes the terminal voltage of lithium iron phosphate battery at point A, a, b, τ1，τ2Is to be fitted toParameter, fitting formula (2) to obtain corresponding a, b, tau1，τ2And estimating and calculating the resistance and the capacitance of the RC circuit by using the value, wherein the value is specifically represented by formula (3):
Figure GDA0002254928570000091
Ibrepresenting the running current of the lithium iron phosphate battery, two RC links, R1、C1And R2、C2Respectively representing electrochemical polarization and concentration polarization processes in the operation of the lithium iron phosphate battery;
accordingly, the corresponding resistance and capacitance in the charging process are estimated by using the formula (4), the capacitance and the resistance of the lithium iron phosphate battery under different SOC are obtained by analogy, the R, C values under different states are obtained by carrying out spline interpolation on the capacitance and the resistance,
Figure GDA0002254928570000092
3) evaluation of available capacity of lithium iron phosphate battery
The service life of the lithium iron phosphate battery is limited, and with the continuous action of charging and discharging in the life cycle of the lithium iron phosphate battery, the loss of lithium ions and the decline of active materials in the lithium iron phosphate battery can cause the irreversible capacity loss in the lithium iron phosphate battery and directly influence the service life of the lithium iron phosphate battery, so that the real-time capacity evaluation of the lithium iron phosphate battery is carried out, the real-time state of the lithium iron phosphate battery can be correctly known, and the method has a positive effect on the estimation of the state of the lithium iron phosphate battery at a certain,
according to the maximum charge-discharge cycle number N corresponding to the operation of the lithium iron phosphate battery at delta SOC ═ xm|ΔSOC＝xFitting the relation of the data, wherein the fitting function is a formula (5), and calculating the maximum charge-discharge cycle number of the lithium iron phosphate battery under a certain charge-discharge cycle depth in the life cycle of the lithium iron phosphate battery according to the formula (5)
As shown in fig. 3, the fitting function of the maximum number of charge and discharge cycles of the lithium iron phosphate battery when operating at different Δ SOCs is as follows:
Figure GDA0002254928570000093
wherein: Δ SOC ═ x, Nm|ΔSOC＝xRepresents the maximum number of charge-discharge cycles;
according to fig. 3, the available capacity in the life cycle of the lithium iron phosphate battery is evaluated to obtain that the maximum charging and discharging cycle times of the lithium iron phosphate battery corresponding to the life cycles of different delta SOCs are different, the charging and discharging cycle times of the lithium iron phosphate battery under the shallow charging and shallow discharging environment are more, and the cycle charging and discharging cycle times under each delta SOC in the charging and discharging process of the lithium iron phosphate battery are converted according to the formula (6) corresponding to the cycle times under the full charging and discharging;
Figure GDA0002254928570000101
in the formula: n is a radical ofBESS(x)The maximum cycle number of the lithium iron phosphate battery is when the charging and discharging depth of the lithium iron phosphate battery is equal to x (x belongs to (0, 1)); n is a radical ofBESS(1)α (x) represents the equivalent cycle depth for the maximum number of cycles for a lithium iron phosphate battery when the lithium iron phosphate battery has a charge-discharge depth equal to 1.
Setting n times of charge and discharge at a certain time, and the depth of charge and discharge is x0、x1、…、xnAnd accumulating the equivalent charge and discharge coefficients under different charge and discharge depths to obtain the equivalent charge and discharge frequency of the lithium iron phosphate battery as the following formula (7):
Figure GDA0002254928570000102
wherein Nm' represents an equivalent charge-discharge coefficient;
the state of health (SOH) of a lithium iron phosphate battery, also referred to as the state of life of the lithium iron phosphate battery, is defined as the ratio of the capacity of the lithium iron phosphate battery discharged from a full charge state to a cut-off voltage at a certain rate to its nominal capacity, reflecting the life status of the lithium iron phosphate battery, and is defined as formula (8):
Figure GDA0002254928570000103
in the formula, CCapicityIndicates the nominal capacity, C, of the lithium iron phosphate batteryuseRepresenting the available capacity of the lithium iron phosphate battery;
the available capacity of the lithium iron phosphate battery at the time t is measured by equation (9):
Figure GDA0002254928570000104
gamma is a constant, which means the percentage of the maximum value of the capacity loss allowed by the normal work of the lithium iron phosphate battery, namely the maximum value of SOH, is 0.3, the SOH reflects the health state of the lithium iron phosphate battery, represents the aging degree of the lithium iron phosphate battery, the change range is 0-100%, when the SOH is reduced to 20-30%, the function of the lithium iron phosphate battery basically fails, and the basic charge and discharge tasks cannot be completed;
4) state estimation of SOC using EKF algorithm
From steps 1) -3), SOC is an important parameter in the running process of the lithium iron phosphate battery, and the state of charge estimation of the lithium iron phosphate battery is the guarantee of safe and reliable running of a lithium iron phosphate battery pack in an energy storage device, so that the real-time SOC of the lithium iron phosphate battery is accurately estimated, and the real-time control strategy of the lithium iron phosphate battery is conveniently adjusted;
the Kalman filtering algorithm is composed of a state equation, an output equation and statistical characteristics of system process noise and observation noise, states or parameters needing to be estimated are obtained according to the state equation and the output equation of the system, the SOC of the lithium iron phosphate battery can be optimally estimated in the minimum variance, prediction and estimation of the lithium iron phosphate battery at a certain future moment are facilitated, the Kalman filtering algorithm is a state equation utilizing a linear system, the lithium iron phosphate battery is a nonlinear model, the nonlinear model of the lithium iron phosphate battery is subjected to Kalman filtering algorithm (EKF) expansion, and the real-time SOC state quantity of the battery is estimated by adopting EKF:
based on the equivalent mathematical model of the lithium iron phosphate battery, establishing a Kalman filtering state equation and an output equation of the lithium iron phosphate battery:
equation of state (10):
Figure GDA0002254928570000111
output equation (11):
ub(k)＝uoc(k)-i(k)×Rs(k)-u1(k)-u2(k)+v(k) (11)
corresponding to the general form (12) of the Kalman filtering equation of state, respectively
Figure GDA0002254928570000112
In the formula: u shapebFor the load terminal voltage of lithium iron phosphate batteries, CuseIs the effective capacity of the lithium iron phosphate battery, i.e. the available capacity of the lithium iron phosphate battery, IbIs the operating current of the lithium iron phosphate battery, SOC represents the state of charge of the lithium iron phosphate battery, UocThe open-circuit voltage of the lithium iron phosphate battery is a nonlinear function of SOC and is represented by a controllable voltage source RsThe ohmic resistance of the lithium iron phosphate battery, two RC links, R1、C1And R2、C2Respectively represents the electrochemical polarization and concentration polarization processes in the operation of the lithium iron phosphate battery, U1，U2Representing the corresponding voltage value, η representing the charging and discharging efficiency of the lithium iron phosphate battery, w (k) representing the system error, v (k) representing the empirical error;
according to a block diagram 4, estimating the SOC in real time:
where k | k-1 represents the result of the last state prediction, k-1| k-1 represents the optimal result at the last time, P (k), Q (k), R (k) corresponds to the covariance of X (k), w (k), v (k),
the Kalman filtering start must select a good initial value, which includes three state parameters, SOC (k), U1(k)，U2(k)，SOc (k) as an initial value, the SOC obtained from the last moment of last operation, while the lithium iron phosphate battery has little effect right after operation, considering that the polarization voltage is 0, and for the covariance q (k), r (k), defined as:
Figure GDA0002254928570000121
further comprises the following steps:
Figure GDA0002254928570000122
Figure GDA0002254928570000123
R(0)＝0.001。
the method comprises the steps of adopting a domestic lithium iron phosphate battery with the rated capacity of 40AH and the rated voltage of 3.2V to carry out experiment data acquisition, identifying relevant parameters of a model by combining the contents of the previous sections, modeling in Matlab/Simulink, and comparing output changes of the lithium iron phosphate battery after 500-1000 cycle times. And the SOC is estimated by EKF under a certain current and voltage working condition, and the validity of the SOC is verified.
The measured relation between the open-circuit voltage of the lithium iron phosphate battery and the SOC is shown in fig. 5, the curve in fig. 5 shows that an obvious nonlinear relation exists between the open-circuit voltage of the lithium iron phosphate battery and the SOC, the fitting curve can well reflect the change relation between the open-circuit voltage and the SOC, the fitting function is as shown in formula (13), and the open-circuit voltage of the lithium iron phosphate battery under different SOC levels is estimated according to the fitting function:
UOC＝-0.7644e-26.6346×SOC+3.2344+0.4834×SOC-1.2057×SOC2+0.9641×SOC3
according to the following method, discharge experiments are carried out on different new and old lithium iron phosphate batteries under the same experimental conditions.
According to the fact that the lithium iron phosphate battery runs in a state that delta SOC is x and the maximum charge-discharge cycle number Nm corresponding to the delta SOC is the number of the cells in the furnaceΔSOC＝xExperimental data of (a) to (b), on whichFitting the relation, wherein the fitting function is as shown in formula (5), and the maximum charge-discharge cycle number under a certain charge-discharge cycle depth in the life cycle of the lithium iron phosphate battery can be calculated according to the formula (5)
The maximum charge-discharge cycle number of the lithium iron phosphate battery when the lithium iron phosphate battery operates at different delta SOC is shown in figure 3, and the fitting function of the lithium iron phosphate battery is as follows (5):
according to the invention, the available capacity in the life cycle of the lithium iron phosphate battery is evaluated according to the graph 3, and it can be seen that the maximum charge-discharge cycle times of the life cycle corresponding to different delta SOCs are different, and the charge-discharge times of the lithium iron phosphate battery under the shallow charging and shallow discharging environment are more. The invention converts the cycle charge and discharge times under each delta SOC to the cycle times under full charge and discharge in the process of charging and discharging the lithium iron phosphate battery according to the formula 6.
Setting n times of charge and discharge at a certain time, and the depth of charge and discharge is x0、x1、…、xnAnd accumulating the equivalent charge and discharge coefficients under different charge and discharge depths to obtain the equivalent charge and discharge times of the lithium iron phosphate battery according to the formula (7).
Fig. 6 is a discharge curve of a lithium iron phosphate battery after 500 cycles, 1000 cycles, and 1500 cycles. The curve shows that after the lithium iron phosphate battery is subjected to different cycle times, under the same discharge rate and after a period of time, the voltage is obviously different, the terminal voltage is reduced in different ranges, and the discharge cut-off voltage is reached within the shortest time after 1000 times.
Further comparison did not take into account the difference in the change in terminal voltage of the lithium iron phosphate batteries at different SOC levels after the lithium iron phosphate batteries were verified, as shown in fig. 7.
The comparison curve shows that the lithium iron phosphate battery does not consider capacity loss and considers that the estimation of the front period and the rear period of the SOC generates a non-negligible error after the verification, and the error becomes larger along with the increase of the cycle number. The output voltage of the lithium iron phosphate battery under different SOC levels can also show obvious difference, which can seriously affect the accuracy of modeling and the estimation of the output voltage of the lithium iron phosphate battery.
Statistically, the error rates of the two compared to the actual curve are shown in Table 1.
TABLE 11000 Voltage error between different SOC intervals after cycle number
Figure GDA0002254928570000131
It can be seen from the table that the calculation error difference before and after the capacity verification is large, the error difference between different SOC intervals is large, and the error is larger under the low SOC level and the high SOC level and cannot be ignored. If the capacity of the lithium iron phosphate battery does not account for the capacity loss, the calculation error is about 0-11.2% after 1000 times of cycle times, the error is about 0-3.8% at the later stage of capacity correction, and the calculation error is greatly reduced. Obviously, the output accuracy of the model is higher after the capacity loss is considered, so that the estimation of the capacity of the current lithium iron phosphate battery is very necessary, and the improvement of the modeling accuracy of the lithium iron phosphate battery and the accurate estimation of the running state of the lithium iron phosphate battery are facilitated.
Because when the lithium iron phosphate battery energy storage system is applied to the new energy field, the charge-discharge characteristic that shows "random", the electric current of lithium iron phosphate battery is not single unchangeable, and the change of in-process electric current can be very violent, utilizes traditional ampere-hour measurement method to lead to the error very big under this condition, utilizes this problem of solution that the extended Kalman filtering algorithm can be fine, uses the model of building as the basis, according to having surveyed the electric voltage, can converge to the more accurate SOC of battery fast.
If the partial current working condition of the lithium iron phosphate battery is shown in fig. 8, and the voltage of the lithium iron phosphate battery is shown in fig. 9, the EKF can obtain the SOC change, specifically, the formula (10), the formula (11) and the formula (12):
and establishing a Kalman filtering state equation and an output equation of the lithium iron phosphate battery based on the equivalent mathematical model of the lithium iron phosphate battery.
Next, the SOC is estimated in real time according to the block diagram 4, which is shown in fig. 10.
The terms, diagrams, tables and the like in the embodiments of the present invention are used for further description, are not exhaustive, and do not limit the scope of the claims, and those skilled in the art can conceive of other substantially equivalent alternatives without inventive step in light of the teachings of the embodiments of the present invention, which are within the scope of the present invention.

Claims (1)

1. A lithium iron phosphate battery modeling and SOC state estimation method considering battery capacity loss is characterized by comprising the following steps:
1) mathematical model of lithium iron phosphate battery equivalent circuit
Adopting a Thevenin equivalent circuit model, and establishing a model state equation as a formula (1) according to a second-order RC equivalent circuit model and by kirchhoff's theorem:
Figure FDA0002254928560000011
in the formula: u shapebFor the load terminal voltage of lithium iron phosphate batteries, CuseIs the effective capacity of the iron phosphate battery, i.e. the available capacity of the lithium iron phosphate battery, IbIs the operating current of the lithium iron phosphate battery, SOC is the state of charge, U, of the lithium iron phosphate batteryocThe open-circuit voltage of the lithium iron phosphate battery is a nonlinear function of SOC and is represented by a controllable voltage source RsThe ohmic resistance of the lithium iron phosphate battery, two RC links, R1、C1And R2、C2Respectively represents the electrochemical polarization and concentration polarization processes in the operation of the lithium iron phosphate battery, U1，U2The corresponding voltage value is shown, and η is the charge-discharge efficiency of the lithium iron phosphate battery;
the equivalent circuit model of the lithium iron phosphate battery shows that the left and right circuit networks are coupled through SOC (state of charge), SOC is an important factor for connecting the two parts, and the state equation (1) of the model shows that the output voltage of the lithium iron phosphate battery is determined by the open-circuit voltage and the polarization voltage of the lithium iron phosphate battery, wherein the polarization voltage of the lithium iron phosphate battery is directly related to the corresponding resistance, capacitance and current value, and the real-time available capacity (C) of the lithium iron phosphate battery is accurately determineduse) The estimation of SOC, open-circuit voltage value, resistance value and capacitance value is the modeling of the lithium iron phosphate batteryBasic work of (2);
2) identification of relevant parameters of lithium iron phosphate battery model
Because the working state of the lithium iron phosphate battery is influenced by factors such as discharge depth, cycle times, capacity attenuation and the like, and the equivalent circuit model parameters of the lithium iron phosphate battery change along with the changes of loads and external environments, in order to obtain a more reliable model, the lithium iron phosphate battery needs to be tested under the condition of multiple factors during off-line modeling, and a parameter data relational expression is established;
SOC is the most important influence factor of all parameters of a resistance-capacitance model, the determination of the functional relation between impedance parameters and SOC is the most basic and important part of resistance-capacitance modeling work under the standard running state condition of the lithium iron phosphate battery, and the lithium iron phosphate battery U in the normal working environmentocThe corresponding relation with SOC is stable and is slightly influenced by temperature, therefore, UocIs uniquely determined by SOC, and the relation is obtained by fitting a function;
resistance-capacitance parameters in the model can be obtained by the following method, under different SOC, the initial value can be set to be 0.2, the step length is 0.05, no-load loading discharge and charge experiments are carried out on the resistance-capacitance parameters, when the lithium iron phosphate battery carries out discharge experiments by no-load state actions, the voltage of the lithium iron phosphate battery can generate a period of abrupt drop, the change of the polarization voltage of the lithium iron phosphate battery is very small and ignored at the moment, and the main reason for causing the change is the ohmic resistance R of the lithium iron phosphate batterysThe voltage drop caused by the voltage drop is used for estimating the internal ohmic internal resistance of the battery according to the data change, and then the terminal voltage of the battery enters an exponential-like change period because of the polarization voltage U on the RC circuit of the battery1，U2The slow decrease results in a period of time that is considered to correspond to a zero state, described by equation (2):
Figure FDA0002254928560000021
wherein U isbRepresents the terminal voltage, U, of a lithium iron phosphate batteryARepresenting the terminal voltage of the lithium iron phosphate battery at the point A, a and b are parameters to be fitted, and the formula is shown in a pair(2) Fitting to obtain corresponding a, b, tau1，τ2And estimating and calculating the resistance and the capacitance of the RC circuit by using the value, wherein the value is specifically represented by formula (3):
Figure FDA0002254928560000022
Ibrepresents the operating current, tau, of a lithium iron phosphate battery1，τ2Is a parameter to be fitted, two RC links, R1、C1And R2、C2Respectively representing electrochemical polarization and concentration polarization processes in the operation of the lithium iron phosphate battery;
accordingly, the corresponding resistance and capacitance in the charging process are estimated by using the formula (4), the capacitance and the resistance of the lithium iron phosphate battery under different SOC are obtained by analogy, the R, C values under different states are obtained by carrying out spline interpolation on the capacitance and the resistance,
Figure FDA0002254928560000023
3) evaluation of available capacity of lithium iron phosphate battery
The service life of the lithium iron phosphate battery is limited, and with the continuous action of charging and discharging in the life cycle of the lithium iron phosphate battery, the loss of lithium ions and the decline of active materials in the lithium iron phosphate battery can cause the irreversible capacity loss in the lithium iron phosphate battery and directly influence the service life of the lithium iron phosphate battery, so that the real-time capacity evaluation of the lithium iron phosphate battery is carried out, the real-time state of the lithium iron phosphate battery can be correctly known, and the method has a positive effect on the estimation of the state of the lithium iron phosphate battery at a certain,
according to the maximum charge-discharge cycle number N corresponding to the operation of the lithium iron phosphate battery at delta SOC ═ xm|ΔSOC＝xFitting the relation of the data, wherein the fitting function is a formula (5), and calculating the maximum charge-discharge cycle number of the lithium iron phosphate battery under a certain charge-discharge cycle depth in the life cycle of the lithium iron phosphate battery according to the formula (5)
The fitting function of the maximum charge-discharge cycle times of the lithium iron phosphate battery when the lithium iron phosphate battery operates at different delta SOC is as follows (5):
Figure FDA0002254928560000031
wherein: Δ SOC ═ x, Nm|ΔSOC＝xRepresents the maximum number of charge-discharge cycles;
evaluating the available capacity in the life cycle of the lithium iron phosphate battery to obtain that the maximum charging and discharging cycle times of the lithium iron phosphate battery corresponding to the life cycles of different delta SOCs are different, the charging and discharging cycle times of the lithium iron phosphate battery under the shallow charging and shallow discharging environment are more, and the cycle charging and discharging cycle times of each delta SOC in the charging and discharging process of the lithium iron phosphate battery are converted according to the formula (6) corresponding to the cycle times under the full charging and discharging;
Figure FDA0002254928560000032
in the formula: n is a radical ofm(x) The maximum cycle number of the lithium iron phosphate battery is when the charging and discharging depth of the lithium iron phosphate battery is equal to x, wherein x belongs to (0, 1); n is a radical ofm(1) α (x) represents the equivalent cycle depth, which is the maximum cycle number of the lithium iron phosphate battery when the charging and discharging depth of the lithium iron phosphate battery is equal to 1;
setting n times of charge and discharge at a certain time, and the depth of charge and discharge is x0、x1、…、xnAnd accumulating the equivalent charge and discharge coefficients under different charge and discharge depths to obtain the equivalent charge and discharge frequency of the lithium iron phosphate battery as the following formula (7):
Figure FDA0002254928560000033
wherein Nm' represents an equivalent charge-discharge coefficient;
the state of health (SOH) of a lithium iron phosphate battery, also referred to as the state of life of the lithium iron phosphate battery, is defined as the ratio of the capacity of the lithium iron phosphate battery discharged from a full charge state to a cut-off voltage at a certain rate to its nominal capacity, reflecting the life status of the lithium iron phosphate battery, and is defined as formula (8):
Figure FDA0002254928560000034
in the formula, CCapicityIndicates the nominal capacity, C, of the lithium iron phosphate batteryuseRepresenting the available capacity of the lithium iron phosphate battery;
the available capacity of the lithium iron phosphate battery at the time t is measured by equation (9):
Figure FDA0002254928560000035
gamma is a constant, which means the percentage of the maximum value of the capacity loss allowed by the normal work of the lithium iron phosphate battery, namely the maximum value of SOH, is 0.3, the SOH reflects the health state of the lithium iron phosphate battery, represents the aging degree of the lithium iron phosphate battery, the change range is 0-100%, when the SOH is reduced to 20-30%, the function of the lithium iron phosphate battery basically fails, and the basic charge and discharge tasks cannot be completed;
4) state estimation of SOC using EKF algorithm
From steps 1) -3), SOC is an important parameter in the running process of the lithium iron phosphate battery, and the state of charge estimation of the lithium iron phosphate battery is the guarantee of safe and reliable running of a lithium iron phosphate battery pack in an energy storage device, so that the real-time SOC of the lithium iron phosphate battery is accurately estimated, and the real-time control strategy of the lithium iron phosphate battery is conveniently adjusted;
the Kalman filtering algorithm is composed of a state equation, an output equation and statistical characteristics of system process noise and observation noise, states or parameters needing to be estimated are obtained according to the state equation and the output equation of the system, the SOC of the lithium iron phosphate battery can be optimally estimated in the minimum variance, prediction and estimation of the lithium iron phosphate battery at a certain future moment are facilitated, the Kalman filtering algorithm is a state equation utilizing a linear system, the lithium iron phosphate battery is a nonlinear model, the nonlinear model of the lithium iron phosphate battery is subjected to Kalman filtering algorithm (EKF) expansion, and the real-time SOC state quantity of the battery is estimated by adopting EKF:
based on the equivalent mathematical model of the lithium iron phosphate battery, establishing a Kalman filtering state equation and an output equation of the lithium iron phosphate battery:
equation of state (10):
Figure FDA0002254928560000041
output equation (11):
ub(k)＝uoc(k)-i(k)×Rs(k)-u1(k)-u2(k)+v(k) (11)
corresponding to the general form (12) of the Kalman filtering equation of state, respectively
Figure FDA0002254928560000051
In the formula: u shapebFor the load terminal voltage of lithium iron phosphate batteries, CuseIs the effective capacity of the lithium iron phosphate battery, i.e. the available capacity of the lithium iron phosphate battery, IbIs the operating current of the lithium iron phosphate battery, SOC represents the state of charge of the lithium iron phosphate battery, UocThe open-circuit voltage of the lithium iron phosphate battery is a nonlinear function of SOC and is represented by a controllable voltage source RsThe ohmic resistance of the lithium iron phosphate battery, two RC links, R1、C1And R2、C2Respectively represents the electrochemical polarization and concentration polarization processes in the operation of the lithium iron phosphate battery, U1，U2Representing the corresponding voltage value, η representing the charging and discharging efficiency of the lithium iron phosphate battery, w (k) representing the system error, v (k) representing the empirical error;
carrying out SOC estimation in real time:
where k | k-1 represents the result of the last state prediction, k-1| k-1 represents the optimal result at the last time, P (k), Q (k), R (k) corresponds to the covariance of X (k), w (k), v (k),
the Kalman filtering start must select a good initial value, which includes three state parameters, SOC (k), U1(k)，U2(k) SOC (k) is obtained from the last time of last operation as an initial value, while the lithium iron phosphate battery has little effect immediately after operation, and the polarization voltage is considered to be 0, and for the covariance q (k), r (k), defined as:
Figure FDA0002254928560000052
further comprises the following steps:
Figure FDA0002254928560000053
R(0)＝0.001。
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102315422A (en) * 2011-09-13 2012-01-11 东莞新能源科技有限公司 Lithium ion battery
CN104122504A (en) * 2014-08-11 2014-10-29 电子科技大学 Method for estimating SOC of battery
US9366732B2 (en) * 2009-09-04 2016-06-14 Board Of Regents, The University Of Texas System Estimation of state-of-health in batteries
CN105676133A (en) * 2014-10-31 2016-06-15 通用汽车环球科技运作有限责任公司 Battery system pack life estimation systems and methods

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9366732B2 (en) * 2009-09-04 2016-06-14 Board Of Regents, The University Of Texas System Estimation of state-of-health in batteries
CN102315422A (en) * 2011-09-13 2012-01-11 东莞新能源科技有限公司 Lithium ion battery
CN104122504A (en) * 2014-08-11 2014-10-29 电子科技大学 Method for estimating SOC of battery
CN105676133A (en) * 2014-10-31 2016-06-15 通用汽车环球科技运作有限责任公司 Battery system pack life estimation systems and methods

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
双锂电池–电容器混合储能系统控制策略设计;李军徽 等;《高电压技术》;20151031;第41卷(第10期);3224-3232 *

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