CN105759216B - A kind of soft bag lithium ionic cell charge state estimation method - Google Patents

Abstract

The invention relates to a method for estimating the state of charge of a soft-pack lithium-ion battery, comprising the steps of: S1: collecting dynamic stress on the surface of the battery and a signal of the working state of the battery, wherein the signal of the working state of the battery includes a signal used to indicate that the battery is charging, resting or the first data of the discharge state; S2: judge whether the battery is in a static state, if yes, execute step S3, if no, execute step S4; S3: use the previous estimation result as the current lithium-ion battery charge Electric state; S4: Obtain the static stress of the battery surface according to the dynamic stress of the battery surface, and perform step S5; S5: According to the obtained static stress of the battery surface, combined with the corresponding function of the static stress and the state of charge, estimate the lithium-ion battery state of charge. Compared with the prior art, the invention adopts a method based on stress measurement to realize the state of charge of the lithium-ion battery, thereby reducing the complexity of the system while improving the accuracy of the system.

Description

A method for estimating the state of charge of a soft-packed lithium-ion battery

technical field

The invention relates to a method for estimating the state of charge of a lithium-ion battery, in particular to a method for estimating the state of charge of a soft-pack lithium-ion battery.

Background technique

When the traditional battery management system (Battery Management System, BMS) estimates the internal state of the battery, especially the SOC, it is basically based on the open circuit voltage OCV of the lithium-ion battery or the battery equivalent circuit method. In fact, this method There is a congenital disadvantage, that is, the estimation of the voltage plateau area is inaccurate, especially for lithium iron phosphate batteries; some new methods, such as fuzzy logic, artificial neural network and support vector machine, are gradually applied to the estimation of SOC. However, the above methods require a large amount of data support, and the model is complex.

To sum up, the battery SOC estimation methods currently used in BMS systems are not accurate enough or are too complicated and costly. Therefore, it is necessary to provide an SOC estimation method based on battery surface stress measurement under the current BMS system architecture.

After 2000, research on the stress of lithium-ion batteries has gradually attracted people's attention. The inventors found that the surface stress of lithium-ion batteries is mainly related to lithium intercalation in electrodes, and the degree of lithium intercalation in electrodes can represent the state of charge of the battery. After in-depth experimental measurements and theoretical Analysis, the inventors found that there is a one-to-one relationship between the surface stress and SOC of the soft-pack lithium-ion battery, and the curve is monotonic, the charge and discharge hysteresis is not obvious, and the charge and discharge stress curves in the platform area basically coincide. If this discovery is applied In the estimation of the state of charge of lithium-ion batteries, it can solve the disadvantages of inaccurate estimation of the plateau area by methods such as equivalent circuits.

Contents of the invention

The object of the present invention is to provide a method for estimating the state of charge of a soft-pack lithium-ion battery in order to overcome the above-mentioned defects in the prior art.

The purpose of the present invention can be achieved through the following technical solutions:

A method for estimating the state of charge of a soft pack lithium-ion battery, comprising the steps of:

S1: Collect the dynamic stress on the battery surface and the battery working state signal, wherein the battery working state signal includes first data for indicating that the battery is in a charging, resting or discharging state;

S2: Determine whether the battery is in a static state, if yes, execute step S3, if no, execute step S4;

S3: Use the previous estimation result as the current state of charge of the lithium-ion battery;

S4: Obtain the static stress of the battery surface according to the dynamic stress of the battery surface, and perform step S5;

S5: According to the obtained static stress on the surface of the battery, combined with the corresponding function of the static stress and the state of charge, estimate the state of charge of the lithium-ion battery.

The gist of the present invention is based on the one-to-one correspondence between the battery surface stress and the state of charge of the lithium-ion battery. Due to the difficulty in collecting the static stress, the method of collecting the dynamic stress on the battery surface is used to estimate the charge of the lithium-ion battery. state.

Described step S4 specifically comprises the steps:

S41: Determine whether the battery is in a charging state, if yes, execute step S42, if no, execute step S43;

S42: According to the collected battery surface dynamic stress and continuous charging time, obtain the battery surface static stress:

DS-S=SS

Among them: DS is the dynamic stress of the battery surface, S is the stress attenuation value, and SS is the static stress of the battery surface;

S43: Use the dynamic stress on the battery surface as the static stress on the battery surface.

The battery working state signal also includes second data for recording the continuous charging time of the battery,

The stress attenuation value in the step S42 is obtained according to the stress attenuation model, and the stress attenuation model is specifically:

Among them: y ₀ is the initial stress, A is the attenuation amplitude, τ is the attenuation constant, and t _c is the continuous charging time of the battery.

Every time the battery starts charging, the continuous charging time of the battery is recalculated.

Described step S42 specifically comprises the steps:

S421: Estimating the state of charge of the lithium-ion battery according to the dynamic stress on the surface of the battery;

S422: Select the corresponding initial stress, attenuation amplitude and attenuation constant according to the estimated results, and substitute them into the stress attenuation model;

S423: Obtain the stress decay value according to the stress decay model and the continuous charging time of the battery;

S424: Obtain the static stress on the surface of the battery according to the stress attenuation value.

The corresponding function of the stress and the state of charge in the step S5 is specifically the corresponding function of the average static stress and the state of charge, wherein the average static stress is the average value of the static stress during charging and discharging under the same state of charge,

Step S5 is specifically: using the obtained static stress on the surface of the battery as the average static stress, and estimating the state of charge of the lithium-ion battery according to the corresponding function of the stress and the state of charge.

Compared with the prior art, the present invention has the following advantages:

1) The present invention uses a method based on stress measurement to realize the state of charge of the lithium-ion battery, which reduces the complexity of the system while improving the accuracy of the system.

2) The external data collection of the present invention is limited to surface stress and battery working state information (the state and continuous charging time), the amount of data required is small, and the amount of model calculation is small.

3) The current state of charge of the battery is directly calculated according to the stress, which has nothing to do with the historical state and error of the battery, and will not cause error accumulation.

4) When charging, the stress attenuation value is considered, which can improve the accuracy.

5) The state of charge is first estimated according to the dynamic stress, and then the corresponding initial stress, attenuation amplitude and attenuation constant are selected according to the state of charge and substituted into the attenuation model, which improves the estimation accuracy.

Description of drawings

Fig. 1 is a schematic flow chart of the main steps of the inventive method;

Fig. 2 is a schematic diagram of the relationship between the battery dynamic stress and the static stress measured through experiments;

Figure 3 Schematic diagram of the application of the stress decay model;

Figure 4 Attenuation amplitude and its fitting graph;

Figure 5 static stress and its fitting curve;

Figure 6 decay constant and its fitting curve;

Fig. 7 is the relationship diagram and average static stress curve of battery static stress and battery SOC measured in the experiment;

Fig. 8 is a detailed flow diagram of the present invention.

Detailed ways

The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is carried out on the premise of the technical solution of the present invention, and detailed implementation and specific operation process are given, but the protection scope of the present invention is not limited to the following embodiments.

Before the application of this application, it is necessary to pre-determine the one-to-one correspondence between the battery surface static stress and the state of charge of the lithium-ion battery. This one-to-one correspondence can be determined by experiments. The corresponding relationship between the static stress and the state of charge of the battery is basically the same, and the one-to-one correspondence is obtained, that is, the corresponding function between the static stress and the electrical state is obtained. This function can be fitted to a mathematical expression for storage, and also It can be stored in the form of a comparison table. Specifically, how to obtain the corresponding relationship and the accuracy of the corresponding function do not belong to the scope of this application, so this application will not describe it in detail. Measured, established data needs to be derived from measurement experiments within a single cycle of a single cell.

A method for estimating the state of charge of a soft-pack lithium-ion battery, as shown in Figure 1, includes steps:

S1: Collect the battery surface dynamic stress and the battery working state signal, wherein the battery working state signal includes the first data for indicating that the battery is in a state of charging, resting or discharging, because it is difficult to measure the static stress on the battery surface, and the dynamic stress is It can be measured by a pressure sensor, so this application chooses to measure the dynamic stress on the battery surface.

S2: Determine whether the battery is in a static state, if yes, execute step S3, if no, execute step S4;

S3: Use the previous estimation result as the current state of charge of the lithium-ion battery;

S4: Obtain the static stress of the battery surface according to the dynamic stress of the battery surface, and perform step S5;

Step S4 specifically includes steps:

S41: Determine whether the battery is in a charging state, if yes, execute step S42, if no, execute step S43;

S42: According to the collected battery surface dynamic stress and continuous charging time, obtain the battery surface static stress:

DS-S=SS

Among them: DS is the dynamic stress of the battery surface, S is the stress attenuation value, and SS is the static stress of the battery surface;

Figure 2 shows the dynamic stress (Dynamic Stress, DS), that is, the instantaneous stress on the surface of the lithium battery measured by the pressure sensor. Static stress (Static Stress, SS) is the stress obtained after the dynamic stress has been fully rested. Generally, it can be considered that the stress will not recover after standing for 2 hours, and the stress at this time is the static stress. Stress decay is a phenomenon in which the stress of a Li-ion battery recovers after the charging or discharging process stops. The three stresses have the following relationship:

SS=DS-S

The static stress of the battery can be obtained by intermittently charging the battery with the change curve of SOC, that is, every charge of 5% SOC is allowed to stand for 2 hours until the charge cut-off voltage is reached; the same is true for the discharge process. As shown in the figure below, the graph marked with a solid square is the dynamic stress during the charging process, the graph marked with a solid triangle is the static stress during the charging process; the graph marked with a hollow triangle is the dynamic stress during the discharge process, and the graph marked with a hollow circle The marked graph is the static stress during the discharge process; as can be seen from Figure 2, the static stress during the charging process is significantly smaller than the dynamic stress, and the two curves during the discharge process almost coincide. This is due to the stress decay characteristics of the charging and discharging process, the stress decay is obvious during charging, but there is almost no stress decay during discharging.

The battery working state signal also includes second data for recording the continuous charging time of the battery, and the continuous charging time of the battery is recalculated every time the battery starts charging.

Step S42 specifically includes steps:

S421: Estimating the state of charge of the lithium-ion battery according to the dynamic stress on the surface of the battery;

S422: Select the corresponding initial stress, attenuation amplitude and attenuation constant according to the estimated results, and substitute them into the stress attenuation model to obtain the stress attenuation value. The stress attenuation model is specifically:

Among them: y ₀ is the initial stress, A is the attenuation amplitude, τ is the attenuation constant, and t _c is the continuous charging time of the battery.

Since the initial stress, attenuation amplitude and attenuation constant in the stress attenuation model are different under different states of charge, this application adopts the method of suggesting a set of parameters for the stress attenuation model for different state of charge intervals.

Since the stress attenuation is related to the continuous charging time, the second data is added to the battery working state signal in this application to record the continuous charging time of the battery to improve the accuracy. In addition, because the stress decay model is related to SOC (state of charge, the same below), that is, the stress decay is a function of SOC, but when establishing the SOC estimation model, SOC is an estimated quantity, that is, an unknown quantity, so the stress decay model The SOC cannot be directly used as an input quantity in . Then it is necessary to pre-estimate the SOC, because there is a monotonous functional relationship between the SOC and the charging stress, so the dynamic stress (instead of the charging stress) can be used to pre-estimate the SOC. The SOC interval is determined by the pre-estimated SOC. In this application, the SOC is divided into multiple intervals, and then the stress attenuation fitting formula corresponding to the SOC at the right end point of each interval is used to approximate the stress attenuation of all points in the interval. Because The approximate alternative method is used, so the more SOC intervals are divided, the more accurate the model is, and the resulting error is smaller. Considering the problem of experimental time, in this embodiment, it is divided into ten intervals, and each 10% is used as an interval. Specifically as shown in Table 1

Table 1

SOC Dynamic stress (kg) SOC Dynamic stress (kg) 0～0.1 0.00～0.99 0.5～0.6 3.72～4.09 0.1～0.2 0.99～1.95 0.6～0.7 4.09～4.57 0.2～0.3 1.95～2.86 0.7～0.8 4.57～5.11 0.3～0.4 2.86～3.42 0.8～0.9 5.11～5.75 0.4～0.5 3.42～3.72 0.9～1.0 5.75～6.12

Then use the stress attenuation fitting formula in different SOC intervals to calculate the stress attenuation, as shown in Figure 3.

Taking the (0.5,0.6] interval as an example, according to the Maxwell stress decay model of lithium-ion batteries, the Polynomial Fit tool is used to fit the curve in Origin,

It can be seen from Figure 4 that as the continuous charging time increases, the attenuation range also increases continuously. The functional relationship between the two can be fitted by a quadratic polynomial, and the goodness of fit R ² can reach 0.998. Write the functional expression between the attenuation amplitude A and the continuous charging time t _c : A＝0.01503+0.00849t _c -9.43353×10 ^-5 t _c ²

As shown in Figure 5, as the continuous charging time increases, y ₀ increases continuously, and the functional relationship between the two can be fitted by a cubic polynomial, and the goodness of fit R ² can reach 0.999. The functional expression between the initial stress y ₀ and the continuous charging time t _c can be written:

y ₀ =4.1707-0.00408t _c -2.6539×10 ^-4 t _c ² +6.43776×10 ^-6 t _c ³

Figure 6 shows the decay constant τ and its fitting curve. It can be seen from the figure that as the continuous charging time increases, the decay constant increases continuously, and the functional relationship between the ^two can be fitted by a linear regression equation, and the goodness of fit R2 can reach 0.965. The functional expression between the decay constant τ and the continuous charging time t _c can be written:

τ=707.7+14.19376t _c

Using the same experimental method, the stress decay process corresponding to different continuous charging time was measured at ten SOC points from 10% to 100% SOC, and curve fitting was performed on each stress decay process in the Origin software, and the obtained Stress decay models corresponding to different continuous charging times at different SOC points, and then extract the parameters in each stress decay model, make images of these parameters (A, y ₀ , τ) and continuous charging time t _c and perform curve fitting.

The fitting results of the stress decay model parameters at different SOC points are listed below:

10%:

20%:

30%:

40%::

50%:

60%:

70%:

80%:

90%:

100%:

S423: Obtain the stress decay value according to the stress decay model and the continuous charging time of the battery;

S424: Obtain the static stress on the surface of the battery according to the stress attenuation value.

S43: Use the dynamic stress on the battery surface as the static stress on the battery surface.

S5: According to the obtained static stress on the surface of the battery, combined with the corresponding function of the stress and the state of charge, estimate the state of charge of the lithium-ion battery,

There can be two corresponding functions of stress and state of charge, which are respectively used to characterize the correspondence between charging static stress and state of charge, and to characterize the correspondence between static stress and state of charge during discharge. This improves precision. However, due to the small difference in static stress during charging and discharging, in order to simplify the calculation complexity, the corresponding function of the average static stress and the state of charge can be used, where the average static stress is the static stress on the battery surface during charging and the static stress on the battery surface during discharging average of.

Since the correspondence between the static stress and the state of charge during charging and discharging will be slightly different, but the gap is small, as shown in Figure 7, the solid line in Figure 7 represents the correspondence between the static stress and the state of charge during charging The function image, the long dotted line is the corresponding function image of the static stress and the state of charge during discharge, and the short dotted line is the corresponding function image of the mean stress and the charge state, as shown in the figure, it is a system generated by using "average static stress" to estimate SOC Error, the maximum error is not more than 3%, and it shows the characteristics of small error in the platform area, but it can greatly simplify the storage capacity and calculation process.

As shown in Figure 8, the input of the SOC estimation model is the battery state signal and the dynamic stress signal, and the two signals are synchronized. The battery status signal is the characterization quantity indicating that the battery is charging, resting or discharging. In this model, "1" represents the charging state, "0" represents the resting state, and "-1" represents the discharging state. The battery status signal is also used to calculate the continuous charging time. When the model detects that the battery is charging, the model starts to calculate the continuous charging time. When the model detects that the battery enters another state, it stops the calculation; when it detects that the battery is charging again, the model restarts. Start counting the continuous charging time. The dynamic stress signal is the surface pressure of the battery monitored by the pressure sensor in real time.

The model works as follows:

The model monitors the input signal in real time. When it is detected that the battery is in a charging state at a certain moment, the stress decay model is started to estimate the SOC through the dynamic stress signal, and the continuous charging time is obtained by counting the battery state signal. The estimated SOC and continuous charging time are used as The input of the stress decay model is calculated to obtain the static stress, and then the state of charge (SOC) of the battery at the current moment is obtained by looking up the table; when the model detects that the battery is in a static state at a certain moment, the model automatically maintains the SOC at the previous moment; When the battery is detected to be in a discharge state at a moment, since there is basically no stress attenuation in the discharge process, the dynamic stress in the discharge process is approximately equal to the static stress, and the battery state of charge at the current moment can be obtained directly by looking up the table.

For example, in this embodiment, the corresponding function of the static stress and the state of charge is in the form of a comparison table, as shown in Table 2:

Table 2

Claims (6)

1. A method for estimating the state of charge of a soft pack lithium-ion battery, is characterized in that, comprising the steps: S1: Collect the dynamic stress on the battery surface and the battery working state signal, wherein the battery working state signal includes first data for indicating that the battery is in a charging, resting or discharging state; S2: Determine whether the battery is in a static state, if yes, execute step S3, if no, execute step S4; S3: Use the previous estimation result as the current state of charge of the lithium-ion battery; S4: Obtain the static stress of the battery surface according to the dynamic stress of the battery surface, and perform step S5; S5: According to the obtained static stress on the surface of the battery, and according to the corresponding function of the stress and the state of charge, estimate the state of charge of the lithium-ion battery. 2. A method for estimating the state of charge of a soft-pack lithium-ion battery according to claim 1, wherein said step S4 specifically comprises the steps of: S41: Determine whether the battery is in a charging state, if yes, execute step S42, if no, execute step S43; S42: According to the collected battery surface dynamic stress and continuous charging time, obtain the battery surface static stress: DS-S=SS Among them: DS is the dynamic stress of the battery surface, S is the stress attenuation value, and SS is the static stress of the battery surface; S43: Use the dynamic stress on the battery surface as the static stress on the battery surface. 3. A method for estimating the state of charge of a soft pack lithium-ion battery according to claim 2, wherein the battery working state signal also includes the second data for recording the continuous charging time of the battery, The stress attenuation value in the step S42 is obtained according to the stress attenuation model, and the stress attenuation model is specifically: Among them: y ₀ is the initial stress, A is the attenuation amplitude, τ is the attenuation constant, and t _c is the continuous charging time of the battery. 4. A method for estimating the state of charge of a soft-pack lithium-ion battery according to claim 3, wherein the continuous charging time of the battery is recalculated each time the battery starts charging. 5. A method for estimating the state of charge of a soft-pack lithium-ion battery according to claim 3, wherein said step S42 specifically comprises the steps of: S421: Estimating the state of charge of the lithium-ion battery according to the dynamic stress on the surface of the battery; S422: Select the corresponding initial stress, attenuation amplitude and attenuation constant according to the estimated results, and substitute them into the stress attenuation model; S423: Obtain the stress decay value according to the stress decay model and the continuous charging time of the battery; S424: Obtain the static stress on the surface of the battery according to the stress attenuation value. 6. A method for estimating the state of charge of a soft-pack lithium-ion battery according to claim 3, wherein the corresponding function between stress and state of charge in the step S5 is specifically the correspondence between the average static stress and the state of charge function, wherein the average static stress is the average value of the static stress during charging and discharging under the same state of charge, Step S5 is specifically: taking the obtained battery surface static stress as the average static stress, and estimating the state of charge of the lithium-ion battery according to the corresponding function of the stress and the state of charge.