Battery parameter test method in V2G application scene

Abstract

The invention discloses a battery parameter testing method in a V2G application scene, and belongs to the technical field of battery testing. Comprising the following steps: step 1, analyzing EV output characteristics in a V2G application scene; 2, constructing a battery parameter test instruction according to the EV output characteristics in the step 1; and step 3, testing battery parameters based on the test instruction in the step 2 to obtain battery performance. According to the test method, the battery data can be accurately tested, the identification error under the two working conditions of peak clipping and valley filling and frequency modulation can be within 0.51%, and the identification precision is higher.

Description

Technical Field

[0001] The present invention relates to the technical field of battery testing, and in particular to a battery parameter testing method in a V2G application scenario. Background Art

[0002] As the number of new energy electric vehicles continues to rise, the huge power reserves of electric vehicle clusters cannot be ignored. Electric vehicles can not only be used as a means of transportation, but also as distributed mobile virtual energy storage. The introduction of V2G (Vehicle-to-Grid, V2G) technology has both advantages and disadvantages. On the one hand, the charging of a large electric vehicle cluster is disorderly and random, which poses a challenge to the safe and stable operation of the power grid. But on the other hand, using the energy of the electric vehicle cluster when it is idle as virtual energy storage to participate in the operation of the power grid and meet the needs of the power grid can help maintain the safe and stable operation of the power grid.

[0003] V2G technology allows electric vehicles (EVs) to interact with the grid as distributed energy storage resources through bidirectional energy exchange. This bidirectional energy flow provides flexibility and controllability, allowing V2G to play a role in various energy management strategies. These distributed energy storage resources can also effectively support the integration of renewable energy, smooth load fluctuations, and provide backup and emergency power support, increasing the energy storage capacity of the grid and improving the grid's ability to absorb new energy. In addition, V2G systems can participate in power markets and energy management mechanisms, such as providing frequency regulation, peak-valley balancing, and capacity markets, and earn revenue through power trading. This ability to participate in market mechanisms makes V2G systems commercial and economical, and provides additional revenue channels for electric vehicle owners. In general, V2G applications have the characteristics of bidirectional energy flow, flexible response, availability of energy storage resources, absorption of new energy, and participation in market mechanisms. These characteristics determine its uniqueness as distributed mobile energy storage and affect its role and effect in the energy system.

[0004] However, the battery performance of electric vehicles affects the stable operation of the power grid. The existing technology cannot accurately obtain the battery performance in a specific scenario under normal circumstances, which has an adverse effect on the subsequent battery parameter identification. Therefore, a battery parameter testing method in a V2G application scenario is needed to obtain the battery performance parameters and data in the V2G application scenario to observe the changes in battery performance and improve the parameter identification accuracy. Summary of the invention

[0005] The purpose of the present invention is to provide a battery parameter testing method in a V2G application scenario, comprising the following steps:

[0006] Step 1: Analyze the EV output characteristics in the V2G application scenario;

[0007] Step 2: Construct battery parameter test instructions based on the EV output characteristics of step 1;

[0008] Step 3: Test the battery parameters based on the test instructions in step 2 to obtain the battery performance.

[0009] The V2G application scenarios in step 1 include peak shaving and valley filling scenarios and frequency modulation scenarios.

[0010] Step 2 specifically includes the following sub-steps:

[0011] Step 21: Obtain the historical load curve of a certain area, and use the peak shaving and valley filling optimization model and the frequency regulation model to obtain the charging and discharging instruction sets of electric vehicles in the peak shaving and valley filling scenario and the frequency regulation scenario respectively;

[0012] Step 22: Decompose the charging and discharging instruction set obtained in step 21 into individual electric vehicles, and select the instruction set of one of the electric vehicles as a typical representative;

[0013] Step 23: scaling the instructions in proportion to the capacity of the test battery to obtain a test instruction set consistent with the capacity of the test battery;

[0014] Step 24: Process the test instruction set of step 23 on the time axis to obtain a battery parameter test instruction.

[0015] The upper and lower limits of the charge and discharge current in the frequency modulation scenario are set to ±2C, and the upper and lower limits of the charge and discharge current in the peak shaving and valley filling scenario are set to ±1C.

[0016] Step 24 is specifically as follows: for the peak shaving and valley filling instructions, the sequence of valley filling + peak shaving is continuous; for the frequency modulation instructions, the time intervals are equal.

[0017] The interval length for the frequency modulation instructions to be performed at equal time intervals is 30 seconds.

[0018] The battery parameters in step 3 include the maximum available capacity and the open circuit voltage.

[0019] The test steps for maximum available capacity include:

[0020] Step A1: Fully charge the battery at standard current using constant current and constant voltage method;

[0021] Step A2: Discharging at a standard constant current to a cut-off voltage;

[0022] Step A3: Determine whether the deviation between the discharge capacity of three consecutive tests and the average of the three test results is within 2%. If so, take the average of the three test results as the maximum available capacity of the battery.

[0023] The open circuit voltage test steps specifically include:

[0024] Step B1: Fully charge the power battery using a standard constant current and constant voltage charging method, leave it to rest for 3 hours, and test its terminal voltage value, which is regarded as the open circuit voltage value when SOC=100%;

[0025] Step B2: Perform constant current pulse discharge and equal capacity discharge on the battery, record the voltage and current changes, and test the terminal voltage value after standing for 1 hour;

[0026] Step B3: Repeat step B2 until the power battery reaches its discharge cut-off voltage.

[0027] The beneficial effects of the present invention are:

[0028] The testing method of the present invention can accurately test battery data, and can make the identification errors under the two working conditions of peak shaving and valley filling and frequency modulation within 0.51%, and the identification accuracy is higher. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a flow chart of a battery parameter testing method in a V2G application scenario of the present invention;

[0030] Figure 2 It is a curve diagram of daily load variation in a certain area;

[0031] Figure 3 Provide peak load reduction and valley filling output diagram for electric vehicle clusters;

[0032] Figure 4 A comparison chart of daily load curves before and after peak shaving and valley filling for electric vehicle clusters;

[0033] Figure 5 Provide frequency modulation output diagram for electric vehicle clusters;

[0034] Figure 6 It is a 30-day peak shaving and valley filling instruction chart;

[0035] Figure 7 To simulate the command diagram for a certain day;

[0036] Figure 8 It is a battery testing platform;

[0037] Fig. 9 The voltage and current variation curve of the capacity test;

[0038] Fig.10 is the OCV-SOC curve;

[0039] Fig.11 (a)(b) are the current and voltage curves under frequency modulation conditions;

[0040] Fig.12 It is the voltage and current curve under frequency modulation condition;

[0041] Fig.13 (a) (b) are the resistance and capacitance parameter identification results under frequency modulation conditions;

[0042] Fig.14 is the parameter identification error under frequency modulation conditions;

[0043] Fig.15 (a) (b) are the resistance and capacitance parameter identification results under peak shaving and valley filling conditions respectively;

[0044] Fig.16 is the parameter identification error under peak shaving conditions. DETAILED DESCRIPTION

[0045] The present invention proposes a battery parameter testing method in a V2G application scenario, which will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0046] Figure 1 The flowchart of the battery parameter testing method in the V2G application scenario of the present invention is as follows:

[0047] Step 1: Analyze the EV output characteristics in the V2G application scenario;

[0048] Grid peak shaving and valley filling refers to the power system adjusting power generation to meet user demand during the daily load peak and valley periods, smoothing out the peak-valley difference, ensuring the stable operation of the power system at all times, and improving power supply reliability.

[0049] To analyze the output of electric vehicles participating in peak-shaving and valley-filling in the power grid, we must first obtain the daily load curve and understand its peak-to-valley difference, and then obtain the output of electric vehicles by constructing a peak-shaving and valley-filling optimization model. Figure 2 It is the daily load variation in a certain area. The response instruction time scale of the electric vehicle cluster participating in the peak shaving and valley filling of the power grid is divided into 15 minutes, that is, a day is divided into 96 time periods.

[0050] During the analysis, according to the load fluctuation in each time period, the dispatch center formulates the charging and discharging power of the electric vehicle cluster participating in the peak-shaving and valley-filling auxiliary service in the corresponding time period, guides the charging and discharging of the electric vehicle cluster, and achieves the purpose of regulating the load peak-valley difference. A cluster electric vehicle dispatchable capacity model based on the travel state matrix and a model of electric vehicle cluster participating in the peak-shaving and valley-filling of the power grid are built. Figure 3 and Figure 4 They are the peak-shaving and valley-filling output diagram of the electric vehicle cluster and the comparison diagram of the daily load curves before and after the peak-shaving and valley-filling of the electric vehicle cluster.

[0051] Compared with peak shaving and valley filling, frequency modulation has a shorter time scale, with charging and discharging switching in seconds, requiring faster response capabilities. Traditional units are limited by factors such as thermal system inertia, mechanical system inertia, startup time, speed regulation system characteristics, and fuel supply, resulting in a low climbing rate and a slow response to frequency regulation, which cannot keep up with load changes. Electric vehicles, as distributed energy storage devices, have the advantage of fast response. When the power grid needs to adjust the frequency, electric vehicles can charge and discharge quickly to provide or absorb power, and the response speed is faster than traditional units. It also has the advantages of cleaner energy sources, lower operating costs, and more flexible adjustment methods. A model of electric vehicle clusters participating in power grid frequency modulation was built. The dynamic load disturbance module was used to simulate the frequency changes caused by load fluctuations in the power system, and a frequency modulation control strategy was formulated. Then a dynamic simulation model was built on the MATLAB / Simulink simulation platform to obtain the frequency modulation output. Figure 5 .

[0052] Step 2: Construct battery parameter test instructions based on the EV output characteristics of step 1;

[0053] In order to construct battery test instructions for the two scenarios of peak shaving and valley filling and frequency modulation, it is necessary to construct a test instruction set based on the charging and discharging characteristics of electric vehicles in the above two scenarios. The specific steps are as follows:

[0054] (1) The historical load curve of a certain area is obtained, and the peak shaving and valley filling optimization model and frequency regulation model are used to obtain the charging and discharging instruction sets of electric vehicles in the two scenarios of peak shaving and valley filling and frequency regulation respectively.

[0055] (2) Decompose the charge and discharge instructions obtained in step (1) into individual electric vehicles, and select the instruction set of one of the electric vehicles as a typical representative.

[0056] (3) The instructions are scaled proportionally according to the capacity of the test battery to obtain a test instruction set consistent with the test battery capacity. With the development of power batteries and charging technology, almost all electric vehicles on the market support fast charging technology, with a fast charging time of 0.5h and a charging current of 2C. Considering that the frequency modulation requirement is high power for a short time and the peak shaving and valley filling requirement is low power for a long time, the upper and lower limits of the charge and discharge current of the battery frequency modulation test are set to ±2C, and the upper and lower limits of the charge and discharge current of the peak shaving and valley filling test are set to ±1C.

[0057] (4) According to the requirements of the battery test experiment, the two test instruction sets of peak shaving and valley filling and frequency modulation are processed on the time axis. In order to shorten the test time, the daily peak shaving and valley filling instructions are processed continuously in the order of valley filling + peak shaving; considering the instruction change rate limit of the battery test equipment, the frequency modulation instructions are processed at equal time intervals, and the time interval selected in this embodiment is 30 seconds.

[0058] 1) Construct peak shaving and valley filling test instructions

[0059] According to the above steps, the test instructions for the peak shaving and valley filling scenario are obtained, such as Figure 6 shown.

[0060] In the peak shaving and valley filling scenario, the SOC lower limit is set to 0.5 to ensure travel needs. Considering the randomness of travel, the upper limit is set to a random number in [0.75-0.85]. Peak shaving and valley filling are performed only once a day, but considering the actual situation of the test, the idle time between peak shaving and valley filling is removed. After the valley filling test is completed, the peak shaving test of the day is performed continuously, and after the peak shaving test is completed, the valley filling test of the next day is performed continuously. Randomly select the initial value in the typical peak shaving and valley filling output, and continue charging / charging until the upper and lower limits are reached.

[0061] 2) Build frequency modulation test instructions

[0062] In the frequency modulation scenario, the upper and lower limits [0.5-1] are set for the battery SOC. The frequency modulation output changes every second. Since the battery test device does not support changing the charge and discharge instructions every second, the fastest change is once every 30 seconds. Therefore, based on the above frequency modulation instructions, the original data is extracted at intervals of 30 seconds to obtain the simulated frequency modulation instructions, such as Figure 7 shown.

[0063] Step 3: Test the battery parameters based on the test instructions in step 2 to obtain the battery performance;

[0064] The test can be divided into two parts, power battery performance test and peak shaving and valley filling, frequency modulation simulation test. The former aims to obtain battery performance parameters such as capacity and OCV-SOC relationship, while the latter aims to obtain simulated operating data when electric vehicles participate in valley filling and frequency modulation of the power grid to observe changes in battery performance.

[0065] The test selected 4 ternary lithium battery cells, divided into two groups, and the two cells in each group were produced in the same batch. The nominal capacity of the first group is 7.5Ah, and the two cells are tested for peak shaving and valley filling and frequency modulation respectively. The nominal capacity of the second group is 98.3Ah, and the two cells are tested for peak shaving and valley filling and frequency modulation respectively. All 4 cells need to be tested for performance. Before the test, the 4 cells have run 500 cycles and have been shelved for a period of time. It is more in line with the actual situation to use old batteries. The test platform is as follows: Figure 8 shown.

[0066] Based on the test purpose, the test plan is designed as follows:

[0067] 1. Maximum available capacity test

[0068] (1) Fully charge the battery using the constant current constant voltage (CC-CV) method at standard current;

[0069] (2) Discharge at a standard constant current to the cut-off voltage;

[0070] (3) Test three times in a row. If the deviation between the discharge capacity of the three tests and the average of the three test results is within 2%, the maximum available capacity test result of this time is valid, and the average of the three test results is taken as the maximum available capacity of the battery.

[0071] 2. Open circuit voltage test

[0072] (1) Fully charge the power battery using the standard CC-CV charging method, let it stand for 3 hours, and test its terminal voltage value, which is regarded as the open circuit voltage value when SOC = 100%;

[0073] (2) Perform constant current pulse discharge on the battery, discharge at equal capacity (5%), record the voltage and current changes, and test the terminal voltage value after standing for 1 hour;

[0074] (3) Jump to step (2) and repeat steps (2) and (3) until the power battery reaches its discharge cut-off voltage.

[0075] 3. Analog FM Test

[0076] Preparation in advance: The battery is fully charged using the CC-CV method at standard current

[0077] SOC range: 50% to 100%;

[0078] The charging and discharging conditions are described in the "frequency modulation test command" in the previous section, where negative values ​​represent charging and positive values ​​represent discharging. The duration of each command is 30s.

[0079] If the discharge reaches the SOC lower limit of 50% during the charge and discharge process, the current command will be stopped and the charging command will be waited for;

[0080] If the charging reaches the SOC upper limit of 100% during the charging and discharging process, the current instruction will be stopped and the discharge instruction will be waited for;

[0081] 4. Simulated Peak Shaving and Valley Filling Test

[0082] Preparation in advance: The battery is charged to 50% capacity using the CC-CV method at standard current;

[0083] SOC range: upper limit is randomly generated between (75% and 85%), lower limit is 50%;

[0084] The charging and discharging conditions are shown in the "peak shaving and valley filling test instructions" described in the previous section. Each cycle is charged first and then discharged.

[0085] If the SOC limit is reached during charging, the current command will be stopped and the subsequent discharge command will be waited for.

[0086] If the discharge reaches the SOC lower limit of 50% during the discharge process, the current instruction is stopped and the charging instruction of the next cycle is waited for. After one cycle is completed, the subsequent charge and discharge cycle will continue.

[0087] In addition, for peak shaving and valley filling and frequency modulation tests, a maximum available capacity test is performed every 50 cycles to regularly calibrate the capacity.

[0088] When testing the maximum available capacity, the voltage and current are as follows: Fig. 9 As shown, a 7.5Ah single cell is taken as an example.

[0089] During the test, the battery was first discharged at a current of 1 / 3C to a cut-off voltage of 2.8V, the original power of the battery was drained, and then the battery was left to stand for half an hour to stabilize. The first constant capacity results of the four single cells are shown in Table 1.

[0090] Table 1 Cell health factor and battery capacity Spearman coefficient

[0091]

[0092] The average of the three discharge capacities of each single cell was selected as the maximum available capacity for the first capacity setting, and the results were 7.02888Ah, 7.085661Ah, 97.56Ah, and 100.03Ah, respectively. After the third battery ran for 500 cycles, the capacity was almost the same as the nominal capacity. The first capacity setting of the fourth battery after 500 cycles was higher than the nominal capacity. There are two reasons for this. One is that the battery will be activated when it is not used again after being stored for a long time, which is manifested as capacity increase. The capacity increase phenomenon is just a slight increase in the battery capacity locally. The capacity of the fourth battery is higher than the nominal capacity. This is because of the battery design process. International regulations stipulate that the actual capacity does not exceed 110% of the nominal capacity, so in real life, the actual capacity of some battery designs will be higher than the theoretical capacity.

[0093] OCV-SOC test results are as follows Fig.10 As shown. The curve is made by pchip interpolation. pchip is the abbreviation of piecewise cubic Hermite interpolation method, which uses piecewise cubic Hermite interpolation function to approximate data points. The piecewise cubic Hermite interpolation function is a piecewise cubic polynomial, which can make the curve smooth between adjacent data points and retain the first-order derivative information of the data points. This means that the generated curve is not only continuous at the data points, but also has the same slope, so the curve generated by the 'pchip' method usually does not oscillate between data points.

[0094] The voltage and current changes of the frequency modulation test are taken as an example on one day. Fig.11 .

[0095] The voltage and current changes of the peak shaving and valley filling test are taken as an example on one day. Fig.12 . It is observed that at the moment when the constant current changes, the voltage will rise or fall sharply for a short period of time. This phenomenon is usually called "voltage jump". There are two main reasons for its occurrence: polarization effect and electrolyte flow. When changing the charging current, the polarization effect inside the battery takes a certain amount of time to adjust to the new working state, and the flow of electrolytes inside the battery also takes a certain amount of time to adapt to the new current conditions. The battery polarization reaction and electrolyte flow cannot keep up with the new current state, resulting in a voltage connection and a jump phenomenon.

[0096] In summary, the adaptive forgetting factor recursive least squares algorithm MI-AFFRLS based on multi-innovation matrix is ​​used for parameter identification to verify the accuracy of battery parameter testing.

[0097] Under frequency modulation conditions, taking the data of the first day as an example, the identification results are as follows: Fig.13 (a)(b) and Fig.14 As can be seen from the figure, the identified resistance and capacitance parameters are relatively stable and within a reasonable range except for C1, and C1 shows an upward trend over time. The error between the voltage calculated using the resistance and capacitance parameters and the true value of the voltage is basically controlled within 0.2%, and the identification accuracy is very high.

[0098] Under the peak shaving and valley filling conditions, taking the test data of the first day as an example, the parameter identification results are as follows: Fig.15 (a)(b) and Fig.16 , and the identification results for the other days are similar. As can be seen from the figure, the polarization resistor R1 and the polarization capacitor C1 will change at the current switching point, and show an overall upward trend over time. The remaining resistance and capacitance parameters R0, R2, and C2 are relatively stable and within a reasonable range. The error between the voltage value calculated by the resistance and capacitance parameters and the true value of the voltage appears at the current switching point. The greater the difference between the values ​​before and after the current switching, the greater the tip error. The maximum error is 0.51%. The errors at other times are very small, and the accuracy is generally good.

[0099] The testing method of the present invention can accurately test and obtain battery data, and can make the identification errors under the two working conditions of peak shaving and valley filling and frequency modulation within 0.51%, and the identification accuracy is higher.

Claims

1. A battery parameter testing method in a V2G application scenario, characterized in that: The following steps are involved: Step 1: Analyze the EV output characteristics in the V2G application scenario; Step 2: Construct battery parameter test instructions based on the EV output characteristics of step 1; Step 3: Test the battery parameters based on the test instructions in step 2 to obtain the battery performance.

2. The battery parameter testing method in the V2G application scenario according to claim 1 is characterized in that: The V2G application scenarios in step 1 include peak shaving and valley filling scenarios and frequency modulation scenarios.

3. The battery parameter testing method in the V2G application scenario according to claim 2 is characterized in that: The step 2 specifically includes the following sub-steps: Step 21: Obtain the historical load curve of a certain area, and use the peak shaving and valley filling optimization model and the frequency regulation model to obtain the charging and discharging instruction sets of electric vehicles in the peak shaving and valley filling scenario and the frequency regulation scenario respectively; Step 22: Decompose the charging and discharging instruction set obtained in step 21 into individual electric vehicles, and select the instruction set of one of the electric vehicles as a typical representative; Step 23: scaling the instructions in proportion to the capacity of the test battery to obtain a test instruction set consistent with the capacity of the test battery; Step 24: Process the test instruction set of step 23 on the time axis to obtain a battery parameter test instruction.

4. The battery parameter testing method in the V2G application scenario according to claim 3 is characterized in that: The upper and lower limits of the charge and discharge current in the frequency modulation scenario are set to ±2C, and the upper and lower limits of the charge and discharge current in the peak shaving and valley filling scenario are set to ±1C.

5. The battery parameter testing method in the V2G application scenario according to claim 3 is characterized in that: The step 24 specifically includes: for the peak shaving and valley filling instructions, the sequence of valley filling + peak shaving is continuous; for the frequency modulation instructions, the time intervals are equal.

6. The battery parameter testing method in the V2G application scenario according to claim 5, characterized in that: The interval length for the frequency modulation instructions to be performed at equal time intervals is 30 seconds.

7. The battery parameter testing method in the V2G application scenario according to claim 1, characterized in that: The battery parameters in step 3 include maximum available capacity and open circuit voltage.

8. The battery parameter testing method in the V2G application scenario according to claim 7, characterized in that: The test steps for maximum available capacity include: Step A1: Fully charge the battery at standard current using constant current and constant voltage method; Step A2: Discharging at a standard constant current to a cut-off voltage; Step A3: Determine whether the deviation between the discharge capacity of three consecutive tests and the average of the three test results is within 2%. If so, take the average of the three test results as the maximum available capacity of the battery.

9. The battery parameter testing method in the V2G application scenario according to claim 7, characterized in that: The open circuit voltage test steps specifically include: Step B1: Fully charge the power battery using a standard constant current and constant voltage charging method, leave it to rest for 3 hours, and test its terminal voltage value, which is regarded as the open circuit voltage value when SOC=100%; Step B2: Perform constant current pulse discharge and equal capacity discharge on the battery, record the voltage and current changes, and test the terminal voltage value after standing for 1 hour; Step B3: Repeat step B2 until the power battery reaches its discharge cut-off voltage.

Images