CN110261778B - SOC estimation algorithm of lithium ion battery - Google Patents
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- 229910001416 lithium ion Inorganic materials 0.000 title claims abstract description 19
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 title claims abstract description 18
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 15
- 238000000034 method Methods 0.000 claims abstract description 52
- 230000002068 genetic effect Effects 0.000 claims abstract description 11
- 239000011159 matrix material Substances 0.000 claims description 18
- 230000010287 polarization Effects 0.000 claims description 11
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- 238000004364 calculation method Methods 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 5
- 230000008034 disappearance Effects 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
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- 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]
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Abstract
The invention discloses a lithium ion battery SOC estimation algorithm which mainly comprises the steps of establishing a lithium ion battery model, determining the relation of SOC-OCV by using a discharge standing method, estimating initial parameters of the battery model in an off-line state, identifying parameters of the battery model by using a least square method with genetic factors in an on-line state, and observing SOC by using a state observer. The algorithm is combined with a least square method with genetic factors to carry out real-time parameter identification and a state observer to carry out SOC observation, the implementation is simple, the practicability is high, the problems that a traditional Kalman filter is large in calculation amount and difficult to apply practically are solved by using the state observer, the accuracy of the lithium ion battery estimation algorithm is guaranteed by the state observer, and the estimation precision is high.
Description
Technical Field
The invention relates to the field of battery state of charge (SOC) estimation, in particular to an SOC estimation algorithm of a lithium ion battery.
Background
The system comprises a power battery, a controller and a controller, wherein the power battery is used as a main energy source of an electric automobile, and the SOC of the power battery is one of the most important and basic parameters in an energy management system; reasonable energy distribution can be performed only by accurate SOC value estimation, so that limited energy is utilized more effectively; the remaining mileage of the vehicle can be correctly predicted. The SOC is defined as the state of charge of the battery, which is used to indicate the remaining amount of the battery. Accurate battery state of charge is a prerequisite and prerequisite for system management of electric vehicles. The battery is a complex nonlinear system, and when the battery is used for an electric vehicle, accurate noise statistics is difficult to obtain due to the fact that electronic equipment is numerous and noise interference is complex; in addition, the parameter change randomness of the external environment and the internal environment causes the mathematical model of the system to be inaccurate and generates model errors, so the anti-interference capability and the self-adaptive capability of the battery charge state estimation must be researched, and the effectiveness of the estimation robustness on the battery charge state is improved.
In the existing SOC estimation method, an ampere-hour measurement method based on a current integration method is easy to form an accumulated error; an open-circuit voltage method and an electromotive force method based on battery terminal voltage measurement require a battery to stand for a long time, and an SOC value cannot be estimated in real time; a neural network method based on a large amount of sample data and a neural network model needs to provide a reliable training method based on a large amount of data samples; the Kalman filtering method based on the battery state space model and the recursion equation has large calculation amount and difficult practical application.
Disclosure of Invention
The purpose of the invention is as follows: the lithium ion battery SOC estimation algorithm solves the problems that a traditional lithium ion battery SOC estimation algorithm is large in calculation amount, low in estimation precision and difficult to achieve.
The technical scheme is as follows: a lithium ion battery SOC estimation algorithm mainly comprises the following steps:
step 1: establishing a Thevenin lithium ion battery model;
step 2: determining the relation of SOC-OCV by using an intermittent discharge standing method;
and step 3: estimating initial parameters of a battery model in an off-line state;
and 4, step 4: identifying parameters of the battery model by a least square method with genetic factors in an online state;
and 5: the SOC is observed using a state observer.
According to one aspect of the invention, a second-order Thevenin lithium ion battery model is established, open-circuit voltage E (t) is used for representing a voltage source, R represents ohmic resistance of the battery, and a second-order resistance-capacitance loop is used for simulating the polarization process of the battery.
According to one aspect of the present invention, the SOC-OCV relationship is determined using an intermittent discharge rest method, first fully charging the battery to 100% SOC, second discharging the battery using a negative pulse every 10% SOC, then resting for 1h to eliminate polarization reaction, and finally averaging at rest to obtain an SOC-OCV curve, the pulse discharge current being set to C/2, and the discharge time width corresponding to a certain amount of charge, i.e., 10% SOC.
According to one aspect of the invention, the initial parameters of the battery model are estimated in an off-line state, and according to the process that the voltage drop generated on the internal ohmic resistance of the battery disappears after the discharge is finished, the ohmic internal resistance of the battery can be obtained:
t is a time variable, order
,
For the time constants of the two RC parallel circuits, the voltage output of the voltage change process caused by the disappearance of the polarization reaction of the cell is:
e is voltage source, I is output current, and Matlab can be used for fitting and identifying double exponential coefficientR s 、R p 、C S 、C PThe value of (c).
According to one aspect of the invention, the battery model parameters are identified by a least square method with genetic factors in an online state, and the functional relation of an equivalent circuit model is as follows:
e (t) is a voltage source, U (t) is an open-circuit voltage, i is an output current, t is a time variable, and the following equation can be obtained through recursive operation of a least square method with genetic factors:
in the least square method, in the recursion operation process, more and more old data can cause the recursion result not to well reflect the characteristics of new data, in order to avoid the situation, a forgetting factor lambda is introduced, k is the iteration frequency,
the parameter matrix estimated for the system, Φ is the measurement matrix, P is the covariance matrix,Kis a gain feedback matrix，yAnd outputting the real output value of the system.
According to one aspect of the invention, the SOC is observed using a state observer whose state equations and output equations are as follows:
wherein,
=[u p u s SOC] T ，u=I ，A= 
，B=[1/C p 1/C s -1/Q n ] T ， 
)=E(soc)-u p -u s ，D=R 0 ，Kin order to obtain a gain feedback matrix, the gain feedback matrix,
in order to output the observed value, the observation value is output,yfor the true output value of the system, QnIs the common ratio of the equal ratio number series.
Has the advantages that: the method can identify the parameters of the battery model by a least square method with genetic factors, strengthen the information quantity provided by new data, gradually weaken old data and prevent data saturation; meanwhile, the state observer is easy to realize, the calculated amount is smaller than that of a Kalman filter, and the estimation precision is high.
Drawings
Fig. 1 is a schematic structural diagram of the novel road damage detection system of the present invention.
Fig. 2 is a second-order thevenin lithium ion battery model diagram.
Fig. 3 is a diagram of an intermittent discharge current.
Fig. 4 is a graph of intermittent discharge voltage.
Fig. 5 is a schematic diagram of a terminal voltage response curve of a lithium ion battery at a discharge end.
Fig. 6 is a diagram showing results of SOC observation experiments.
Detailed Description
The algorithm of the present invention is further described with reference to the accompanying drawings.
The detailed steps of the SOC estimation algorithm are illustrated in fig. 1.
As shown in fig. 2, a second-order thevenin lithium ion battery model is established, open-circuit voltage e (t) is used for representing a voltage source, R represents ohmic resistance of the battery, and a second-order resistance-capacitance loop is used for simulating the polarization process of the battery.
As shown in fig. 3 and 4, the abscissa is time, the ordinate of fig. 3 is discharge current, the ordinate of fig. 4 is open-circuit voltage, and the SOC-OCV relationship was determined by the intermittent discharge static method, first, the battery was fully charged to 100% SOC, and second, negative pulses were used every 10% SOC. The cell was discharged by current, then left to stand for 1h to eliminate polarization reaction, and finally averaged at rest to obtain the SOC-OCV curve. The pulse discharge current is set to C/2, and the discharge time width thereof corresponds to a certain amount of charge, i.e., 10% SOC.
As shown in figure 5 of the drawings,(V1-V0) The process that the voltage drop generated on the internal ohmic resistance of the battery disappears after the discharge is finished, so that the ohmic internal resistance of the battery can be obtained:
Suppose the battery is discharged for a period of time during (t0-tr) and then the remaining time is in a static state, where t0To the discharge start time, tdAt the time of discharge stop, trFor the rest stop time, the RC network voltage during this process is:
t is a time variable, order
,
Is the time constant of two RC parallel circuits, (V)3-V1) The phase voltage change is caused by the disappearance of the polarization reaction of the cell, where the process voltage output is:
e is a voltage source, and E is a voltage source,
for outputting current, Matlab can be used for fitting and identifying double-exponential coefficientR s 、R p 、C S 、C PThe value of (c).
As shown in fig. 2, the equivalent circuit model is obtained as a functional relationship:
e (t) is a voltage source, U (t) is an open-circuit voltage value, i is an output current, t is a time variable, and an equation can be obtained through recursive operation of a least square method with genetic factors, wherein the equation is as follows:
in the recursive operation process of the least square method, more and more old data can cause the recursive result to not well reflect the characteristic of new data, and in order to avoid the situation, a forgetting factor is introducedλAnd k is the number of iterations,
the parameter matrix estimated for the system, Φ is the measurement matrix, P is the covariance matrix,Kis a gain feedback matrix，yAnd outputting the real output value of the system.
The state equation and the output equation of the state observer are as follows:
wherein,
=[u p u s SOC] T ，u=I，A= 
，B=[1/C p 1/C s -1/Q n ] T ，
) =E(soc)-u p -u s ，D=R 0 ，Kin order to obtain a gain feedback matrix, the gain feedback matrix,
in order to output the observed value, the observation value is output,yfor the true output value of the system, QnIs the common ratio of the equal ratio number series.
The experimental result graph is shown in fig. 6, and the result shows that the following is good and the SOC estimation deviation is small.
In summary, the present invention has the following advantages: the algorithm of the invention combines the least square method with genetic factors to identify the parameters of the battery model, strengthen the information quantity provided by new data, weaken old data gradually and prevent data saturation; meanwhile, the state observer is used for observing the SOC, the realization is easy, the calculated amount is smaller than that of a Kalman filter, and the estimation precision is high.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. The invention is not described in detail in order to avoid unnecessary repetition.
Claims (3)
1. A lithium ion battery SOC estimation algorithm is characterized by mainly comprising the following steps:
step 1: establishing a Thevenin lithium ion battery model;
step 2: determining the relation of SOC-OCV by using an intermittent discharge standing method;
and step 3: estimating initial parameters of a battery model in an off-line state;
and 4, step 4: identifying parameters of the battery model by a least square method with genetic factors in an online state;
and 5: observing the SOC by using a state observer;
in the step 2, an intermittent discharge standing method is adopted for the relation of SOC-OCV, firstly, the battery is fully charged to 100% SOC, secondly, negative pulses are used at every 10% SOC, the battery is discharged by current, then the battery is static for 1h to eliminate polarization reaction, finally, the average value during standing is obtained to obtain an SOC-OCV curve, the pulse discharge current is set to be C/2, and the discharge time width corresponds to a certain amount of charge, namely 10% SOC;
step 3, calculating initial parameters of a battery model by using a voltage response curve after the battery intermittent discharge is finished in an off-line state, simulating the polarization process of the battery by adopting a mode of superposing two resistance-capacitance links, and setting a parameter Rs、CsSetting a parameter R for the resistance and capacitance of a resistance-capacitance linkp、CpFor the resistance and capacitance of another resistance-capacitance link, Matlab can be used for fitting and identifying double-exponential coefficientR s 、R p 、C s 、C pValue of (A), CsAnd RsThe formed RC parallel circuit has a small time constant and is used for simulating the process of rapid voltage change of the battery when the current suddenly changes, CpAnd RpThe time constant of the parallel circuit is large, and is used for simulating the process of voltage slow change, assuming that the battery is discharged for a period of time in the process, and then the rest time is in a standing state, wherein the RC network voltage is as follows:
wherein t is0In order to start the time of the discharge,tdat the time of discharge stop, trRespectively standing for a stop time, t is a time variable, and order
,
And as time constants of the two RC parallel circuits, the voltage output in the process that the polarization reaction of the battery disappears is as follows:
wherein E is a voltage source, and E is a voltage source,
is an output current;
in the step 5, the state observer is used for observing the SOC value, and a state equation and an output equation of the state observer are as follows:
wherein,
=[u p u s SOC] T ，u=I，A=
,B=[1/C p 1/C s -1/Q n ] T ， 
)= E(soc)-u p -u s ，D=R 0 ，Kin order to obtain a gain feedback matrix, the gain feedback matrix,
in order to output the observed value, the observation value is output,yfor the true output value of the system, QnIs the common ratio of the equal ratio number series.
2. The lithium ion battery SOC estimation algorithm of claim 1, wherein the lithium ion battery model in step 1 is a second-order Withanan model, the open-circuit voltage E (t) is used to represent the voltage source, R is the ohmic resistance of the battery, and a second-order RC loop is used to simulate the polarization process of the battery.
3. The estimation algorithm of SOC of li-ion battery as claimed in claim 1, wherein in step 4, the identification of battery model parameters is performed by using a least square method with genetic factors in an online state, and the equivalent circuit model has the following functional relationship:
e (t) is a voltage source, U (t) is an open-circuit voltage value, i is an output current, t is a time variable, and an equation can be obtained through recursive operation of a least square method with genetic factors, wherein the equation is as follows:
in the least square method, in the recursion operation process, more and more old data can cause the recursion result not to well reflect the characteristics of new data, in order to avoid the situation, a forgetting factor lambda is introduced, k is the iteration frequency,
the parameter matrix estimated for the system, Φ is the measurement matrix, P is the covariance matrix,Kis a gain feedback matrix，yAnd outputting the real output value of the system.
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| CN112147514B (en) * | 2020-09-25 | 2023-08-11 | 河南理工大学 | RLS-based adaptive equivalent circuit model of lithium battery under full operating conditions |
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| CN113176505B (en) * | 2021-04-30 | 2022-10-04 | 重庆长安新能源汽车科技有限公司 | On-line estimation method and device for state of charge and state of health of vehicle-mounted power battery and storage medium |
| CN114295982B (en) * | 2021-10-28 | 2025-02-11 | 上海空间电源研究所 | Lithium-ion battery parameter and open circuit voltage identification method, system and medium |
| CN116381513B (en) * | 2023-06-07 | 2023-07-28 | 广东电网有限责任公司东莞供电局 | Lithium ion battery model parameter robust identification method considering measured value abnormality |
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