CN108008317A - A kind of state-of-charge method of estimation based on battery open circuit voltage curve characteristic - Google Patents

Abstract

The invention relates to a method for estimating the state of charge based on the characteristics of the open-circuit voltage curve of a battery, and belongs to the technical field of batteries. The method includes the following steps: S1: Perform HPPC test on the tested battery to obtain battery characteristic parameters; S2: Based on the OCV curve characteristics of the tested battery, select the SOC interval with a large change rate of OCV with SOC as the reliable interval of the battery model, and other SOC The interval is regarded as the non-battery model reliable interval; S3: In the battery model reliable interval, the SOC of the interval is estimated by the battery model and the EKF algorithm, and in the non-battery model reliable interval, the SOC estimate is obtained by the ampere-hour integration method. The invention has lower algorithm complexity and good feasibility; at the same time, it has the advantage that temperature fluctuation has less influence on SOC estimation accuracy. The method of the invention can further realize the current drift estimation or the capacity error estimation in the ampere-hour integral method of the non-battery model reliable interval.

Description

A State of Charge Estimation Method Based on Battery Open Circuit Voltage Curve Characteristics

technical field

The invention relates to the field of battery technology, in particular to a method for estimating the state of charge based on the characteristics of the open-circuit voltage curve of the battery.

Background technique

The SOC estimation of the battery is an important part of the battery management system. The energy state, power state, and safety state of the battery all depend on the SOC estimation. Accurate SOC estimation is of great significance.

In order to improve the online estimation accuracy and robustness of SOC, the SOC fusion estimation method based on the battery model is often used in engineering. In this method, the model accuracy has a great influence on the final SOC estimation value, especially for some lithium-ion batteries, in some intervals, an OCV error of 1mV will cause an SOC error of 0.3% or even higher. However, through the observation of the SOC-OCV Rate curve, it can be found that the sensitivity of SOC to the change of OCV is low in some intervals, while in other intervals, the sensitivity is higher. That is to say, in intervals where SOC is less sensitive to changes in OCV, the same size of model error will actually cause relatively smaller SOC estimation errors in these intervals.

Studies have shown that the incremental capacity (incremental capacity, IC) curve of the battery is similar to the SOC-OCVRate curve [1]. During the aging process of some lithium-ion batteries, the IC curve is in the valley section (corresponding to the valley of the SOC-OCVRate curve), and the change in the aging process of the battery is much smaller than that of other places [2]. Therefore, the SOC-OCV empirical model during the battery aging process is more reliable in the valley intervals of the SOC-OCV Rate curve of these lithium-ion batteries, and the SOC estimation realized by the fusion estimation method in these intervals is also more accurate.

At present, there are many studies on SOC estimation methods of batteries, but most of them are estimation algorithms for new batteries. There are also some joint estimation algorithms that realize the estimation of battery model parameters and SOC at the same time, but it is difficult to apply in engineering, and for some special working conditions (such as constant current working conditions), there may be instability, or even impossible.

Contents of the invention

In view of this, the purpose of the present invention is to provide a method for estimating the state of charge based on the characteristics of the open-circuit voltage curve of the battery, so as to realize accurate estimation of the state of charge of the battery in its entire life cycle.

To achieve the above object, the present invention provides the following technical solutions:

A method for estimating the state of charge based on the characteristics of the battery open circuit voltage curve, the method comprising the following steps:

S1: Perform a hybrid pulse power characteristic (Hybrid Pulse PowerCharacteristic, HPPC) test on the battery under test to obtain battery characteristic parameters;

S2: Based on the open circuit voltage (OCV) curve characteristics of the tested battery, select the SOC interval with a large change rate of OCV with the state-of-charge (SOC) as the reliable interval of the battery model, and other SOC intervals as the reliable interval of the battery model. Non-battery model reliability interval;

S3: In the reliable interval of the battery model, the SOC of the interval is estimated by the battery model and the extended Kalman filter (EKF) algorithm, and in the non-reliable interval of the battery model, the estimated SOC value is obtained by the ampere-hour integration method.

Further, in step S2, the OCV curve characteristic of the battery is the SOC-OVC Rate curve, and the calculation method is as follows:

Among them, 5e-4 means 5×10 ^-4 V

The reliable interval of the battery model is the SOC interval segment corresponding to the valley of the SOC-OCV ratio curve.

Further, step S3 includes the following steps:

S31: Obtain operating parameters of the target battery, including time, current, voltage and temperature;

S32: Using the ampere-hour integration method to obtain the current SOC estimation value;

S33: Determine whether the estimated SOC value belongs to the reliable interval of the battery model, if so, proceed to step S34; otherwise, skip to step S35;

S34: Using the battery model combined with the EKF algorithm to obtain a new SOC estimation value;

S35: Obtain the final SOC estimation value of the system.

Further, the ampere-hour integration method is:

Among them, SOC ₀ is the initial SOC, which is obtained by looking up the SOC-OCV table in the battery balance state, η is the Coulombic efficiency, C _n is the battery capacity, and I is the measured value of the current passing through the battery.

Further, the battery model is an equivalent circuit model, an electrochemical model, a machine learning model or a mathematical model.

The beneficial effects of the present invention are:

According to the present invention, the rate of change of OCV of some lithium batteries is relatively large in some SOC intervals of the state of charge, and the corresponding relationship between SOC and OCV in these intervals during the aging process of the battery is smaller than that of other intervals. , using an improved fusion algorithm to achieve accurate estimation of the SOC of the battery life cycle.

The advantage of adopting the inventive method is:

1) On the basis of basically not changing the existing SOC estimation algorithm, according to the characteristics of the SOC-OCV empirical model and the accuracy change law at different aging degrees, the accurate estimation of the SOC of the battery life cycle is realized.

2) The algorithm has lower complexity and better feasibility.

3) Temperature fluctuations have less impact on the SOC estimation accuracy.

4) According to the accurate estimation of the SOC of the reliable interval of the battery model, the current drift estimation or capacity error estimation in the ampere-hour integration method of the reliable interval of the non-battery model can be further realized.

Description of drawings

In order to make the purpose, technical scheme and beneficial effect of the present invention clearer, the present invention provides the following drawings for illustration:

Fig. 1 is the flow chart of the method for obtaining the reliable interval of the battery model in the present invention;

Figure 2 is a schematic diagram of SOC-OCV;

Figure 3 is a schematic diagram of SOC-OCV Rate;

Fig. 4 is the flow chart of the fusion algorithm improved in the method of the present invention;

Figure 5 is a second-order RC circuit model.

Detailed ways

The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

In one embodiment of the present invention, the specific method for obtaining the reliable interval of the battery model is shown in Figure 1, which specifically includes the following steps:

Step S1: Select the target battery;

Step S2: Perform HPPC test on the target battery offline to obtain battery characteristic parameters;

Step S3: According to the relationship between SOC-OCV obtained in step S2, as shown in Figure 2, obtain the SOC-OCV Rate curve of the target battery, as shown in Figure 3, the calculation method is as follows:

Among them, 5e-4 means 5×10 ^-4 V

Step S4: Determine which areas are in the valley of the SOC-OCV Rate curve, if it is a valley, go to step S5, otherwise, go to step S6;

Step S5: This interval segment is the reliable interval of the battery model;

Step S6: This interval segment is a non-battery model reliable interval.

In one embodiment of the present invention, the implementation method of estimating SOC based on the characteristics of the battery open circuit voltage curve, as shown in Figure 4, specifically includes the following steps:

Step S3-1: Obtain the operating parameters of the target battery, including time, current, voltage, and temperature;

Step S3-2: using the ampere-hour integration method to obtain the current SOC estimated value;

In one embodiment of the present invention, the ampere-hour integration method is as follows:

In the formula, SOC ₀ is the initial SOC, which is obtained by looking up the SOC-OCV table in the battery balance state; η refers to the Coulombic efficiency; C _n is the battery capacity; I is the measured value of the current passing through the battery, which is negative for charging and positive for discharging .

Step S3-3: Judging whether the estimated SOC value obtained in step S3-2 belongs to the reliable interval of the battery model, if so, go to step S3-4, otherwise, go to step S3-5;

Step S3-4: Using the battery model combined with the EKF algorithm to obtain a new estimated SOC value;

In one embodiment of the present invention, the second-order equivalent circuit model is selected, as shown in Figure 5, in the figure, I is the current through the battery; U _oc and U _B represent open circuit voltage and terminal voltage respectively; R ₀ represents ohms Internal resistance; U _D and U _T are to reflect the characteristics of the internal polarization internal resistance of the battery.

In one embodiment of the present invention, the state equation and output equation in the EKF are as follows:

g(x _k ,u _k )＝U _oc (SOC _k )-I _k R ₀ -U _D,k -U _T,k

In the formula, f(x _k , u _k ) is the state equation, g(x _k , u _k ) is the output equation; u _k is the input quantity, I _k is the current through the battery, U _{D, k} is the polarization internal resistance The voltage on _RD , U _{T, k} represents the voltage on the polarization internal resistance _RT , SOC _k represents the state of charge, U _OC represents the open circuit voltage; R ₀ represents the ohmic internal resistance in the second-order RC model; τ _D and τ _T is the time constant of R _D C _D and R _T C _T respectively; Δt refers to the sampling time interval.

Step S3-5: The system obtains the final estimated SOC value.

According to the embodiment of the method for estimating the state of charge based on the characteristics of the battery open circuit voltage curve involved in this embodiment, it may also have such a feature: the battery model used is an equivalent circuit model, an electrochemical model, a machine learning model, a mathematical any of the models.

According to the state of charge estimation method based on the battery open circuit voltage curve characteristics involved in this embodiment, according to some lithium batteries OCV has a large change rate in certain SOC intervals, and the SOC and SOC in these intervals during the battery aging process The corresponding relationship between OCVs has less change than other intervals, and an improved fusion algorithm is used to realize the accurate estimation of the SOC of the battery life cycle.

The advantages of using the state of charge estimation method based on the battery open circuit voltage curve characteristics involved in this embodiment are: 1) On the basis of basically not changing the existing SOC estimation algorithm, according to the characteristics of the SOC-OCV empirical model and different aging degrees 2) The complexity of the algorithm is lower and it has better feasibility; 3) The impact of temperature fluctuation on the accuracy of SOC estimation is smaller; 4) According to the obtained The accurate SOC estimation of the reliable interval of the battery model can further realize the current drift estimation or capacity error estimation in the ampere-hour integration method of the reliable interval of the non-battery model.

Finally, it is noted that the above preferred embodiments are only used to illustrate the technical solutions of the invention and not limit them. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that it may be possible in form and details. Various changes can be made to it without departing from the scope defined by the claims of the present invention.

Claims (5)

1. A method for estimating state of charge based on battery open circuit voltage curve characteristics, characterized in that: the method comprises the steps: S1: Perform a hybrid pulse power characteristic (Hybrid Pulse Power Characteristic, HPPC) test on the battery under test to obtain battery characteristic parameters; S2: Based on the open circuit voltage (OCV) curve characteristics of the tested battery, select the SOC interval with a large change rate of OCV with the state-of-charge (SOC) as the reliable interval of the battery model, and other SOC intervals as the reliable interval of the battery model. Non-battery model reliability interval; S3: In the reliable interval of the battery model, the SOC of the interval is estimated by the battery model and the extended Kalman filter (EKF) algorithm, and in the non-reliable interval of the battery model, the estimated SOC value is obtained by using the ampere-hour integration method. 2. A method for estimating the state of charge based on the characteristics of the open circuit voltage curve of the battery according to claim 1, wherein in step S2, the characteristic of the OCV curve of the battery is the SOC-OVC Rate curve, and the calculation method is as follows: <mrow><mi>S</mi><mi>O</mi><mi>C</mi><mo>-</mo><mi>O</mi><mi>C</mi><mi>V</mi><mi></mi><mi>R</mi><mi>a</mi><mi>t</mi><mi>e</mi><mo>=</mo><mfrac><mrow><mi>S</mi><mi>O</mi><mi>C</mi><mrow><mo>(</mo><mi>O</mi><mi>C</mi><mi>V</mi><mo>+</mo><mn>5</mn><mi>e</mi><mo>-</mo><mn>4</mn><mo>)</mo></mrow><mo>-</mo><mi>S</mi><mi>O</mi><mi>C</mi><mrow><mo>(</mo><mi>O</mi><mi>C</mi><mi>V</mi><mo>-</mo><mn>5</mn><mi>e</mi><mo>-</mo><mn>4</mn><mo>)</mo></mrow></mrow><mn>0.01</mn></mfrac></mrow> Among them, 5e-4 means 5×10 ^-4 V The reliable interval of the battery model is the SOC interval segment corresponding to the valley of the SOC-OCV ratio curve. 3. A method for estimating the state of charge based on the characteristics of the battery open circuit voltage curve according to claim 1, wherein step S3 comprises the following steps: S31: Obtain operating parameters of the target battery, including time, current, voltage and temperature; S32: Using the ampere-hour integration method to obtain the current SOC estimation value; S33: Determine whether the estimated SOC value belongs to the reliable interval of the battery model, if so, proceed to step S34; otherwise, skip to step S35; S34: Using the battery model combined with the EKF algorithm to obtain a new SOC estimation value; S35: Obtain the final SOC estimation value of the system. 4. A method for estimating the state of charge based on the characteristics of the battery open-circuit voltage curve according to claim 3, wherein the ampere-hour integration method is: <mrow><mi>S</mi><mi>O</mi><mi>C</mi><mo>=</mo><msub><mi>SOC</mi><mn>0</mn></msub><mo>-</mo><mfrac><mrow><mo>&amp;Integral;</mo><mi>&amp;eta;</mi><mi>I</mi>mi><mi>d</mi><mi>t</mi></mrow><msub><mi>C</mi><mi>n</mi></msub></mfrac></mrow> Among them, SOC ₀ is the initial SOC, which is obtained by looking up the SOC-OCV table in the battery balance state, η is the Coulombic efficiency, C _n is the battery capacity, and I is the measured value of the current passing through the battery. 5. The method for estimating the state of charge based on the characteristics of the battery open circuit voltage curve according to claim 1, wherein the battery model is an equivalent circuit model, an electrochemical model, a machine learning model or a mathematical model.