Task profile-based energy storage system reliability evaluation method

Abstract

The invention discloses an energy storage system reliability evaluation method based on a task profile, and belongs to the field of energy storage system reliability evaluation. According to the method, device-equipment-system multi-level reliability evaluation of an energy storage system can be completed according to the task profile of the energy storage system.

Description

Technical Field

[0001] The present invention belongs to the technical field of reliability assessment of energy storage systems, and in particular relates to a reliability assessment method of an energy storage system based on a mission profile. Background Art

[0002] In modern power systems, energy storage systems are increasingly used and have become an important component of achieving efficient use of renewable energy and optimizing power dispatch. The reliability of energy storage systems is directly related to their performance in the power grid and their overall operational safety. However, with the continuous advancement of energy storage technology, its operating environment and application scenarios have become increasingly complex, making it more difficult to evaluate the reliability of energy storage systems.

[0003] Energy storage systems are usually composed of a variety of key components, among which insulated gate bipolar transistors (power devices) and capacitors are the core parts. As efficient switching devices, power devices play an important role in energy conversion and control; while capacitors are used to balance energy storage and release to ensure the stable operation of the system. However, power devices and capacitors are prone to aging and failure under extreme working conditions such as high temperature and high frequency, resulting in reduced reliability of the entire energy storage system.

[0004] Traditional reliability assessment methods usually focus on the analysis of individual system components, ignoring the comprehensive impact of different tasks on system performance in actual operation. This method is difficult to fully reflect the actual reliability performance of energy storage systems under different operating conditions and task loads. In order to more accurately evaluate the reliability of energy storage systems, a novel method is urgently needed to comprehensively consider the performance of the system under specific tasks, especially to conduct a detailed analysis of the working status of power devices and capacitors.

[0005] This patent proposes a reliability assessment method for energy storage systems based on mission profiles, which aims to provide a more accurate reliability assessment by analyzing the operating characteristics and performance indicators of the system under different mission conditions. This method combines the techniques of mission analysis and system modeling. By analyzing various tasks of the energy storage system, it can identify potential failure risks and optimize the use strategy and maintenance plan of power devices and capacitors, thereby improving the safety and stability of the entire system. Through the comprehensive evaluation of key components, this method provides strong support for the design, operation and management of energy storage systems. Summary of the invention

[0006] The purpose of the present invention is to propose a reliability assessment method for an energy storage system based on mission profile.

[0007] The reliability assessment method of energy storage system based on mission profile mainly includes the following steps: Figure 1 As shown:

[0008] S1. Obtain the mission profile of the energy storage system;

[0009] S2. Estimate the state of charge of the energy storage battery and extract the SOC curve of the energy storage battery;

[0010] S3. Calculate the junction temperature of the power devices in the energy storage converter;

[0011] S4. Calculate the hot spot temperature of the capacitor in the energy storage converter;

[0012] S5. Extracting cycle and calendar components from the battery SOC curve;

[0013] S6. Extracting thermal load information of power devices and capacitors;

[0014] S7. Calculate battery life damage based on the battery life model;

[0015] S8. Calculate the life damage of power devices based on the power device life model;

[0016] S9. Calculate capacitor life damage based on capacitor life model;

[0017] S10.Reliability evaluation of energy storage system based on Monte-Carlo simulation.

[0018] The method for evaluating the reliability of an energy storage system based on a mission profile obtains the mission profile of the energy storage system, such as Figure 2 As shown, including the active output of the energy storage system and the ambient temperature.

[0019] The reliability assessment method of the energy storage system based on the mission profile estimates the state of charge of the energy storage battery and extracts the SOC curve of the energy storage battery; the change value of the battery state of charge SOC is obtained by integrating the loop current over time and then dividing it by the rated capacity, and then adding it to the initial value to obtain the current battery SOC, thereby obtaining the energy storage battery SOC operation curve.

[0020]

[0021] In the formula, SOC(0) is the initial state of charge of the battery, i in (t) is the battery input current, C e is the rated capacity of the battery.

[0022] The reliability assessment method of the energy storage system based on the mission profile calculates the junction temperature of the power device in the energy storage converter, and uses the power device thermoelectric coupling model to calculate the power device temperature, such as Figure 3 As shown in the figure, the power device power loss is divided into switching loss and conduction loss, and the calculation formula is:

[0023]

[0024] In the formula, is the conduction loss of the power device; δ(t) is the duty cycle; i a is the output current of the power device; T j is the junction temperature of the power device; V CE_25℃ is the rated on-state voltage drop of the power device at 25°C; r CE_25℃ K is the rated on-state resistance of the power device at 25°C; V_T K is the temperature influence coefficient of the on-state voltage drop of the power device; r_T is the temperature influence coefficient of the on-state resistance of the power device; f sw K is the frequency of turning on and off the power device module; sw is the temperature influence coefficient of the switching loss of the power device; K1(V dc ) is the voltage coefficient of the converter DC side voltage that affects the switching power loss of the power device; K2(R g ) is the gate resistance R g The resistance coefficient that affects the switching power loss of power devices;

[0025] Use the Foster model and power loss model to perform thermoelectric conversion of power devices and convert the power loss profile into a thermal stress profile

[0026]

[0027] Where, T j is the junction temperature of the power device; T a is the ambient temperature; is the power loss of the power device and the anti-parallel diode; Z tjh , Z ha is the thermal impedance parameter in the Foster model.

[0028] The reliability assessment method of the energy storage system based on the mission profile calculates the hot spot temperature of the capacitor in the energy storage converter and calculates the capacitor temperature using the capacitor thermoelectric coupling model, such as Figure 4 As shown, the total power loss of DC capacitor can be expressed as:

[0029]

[0030] In the formula, R ESR is the ESR of the DC bus capacitor; I c is the current harmonic amplitude of the capacitor; then the hot spot temperature of the DC capacitor can be expressed as:

[0031] T h =P c,loss R th +T a (7)

[0032] Where, T h is the hot spot temperature of the capacitor; T a is the ambient temperature; R th is the thermal resistance of the capacitor given in the datasheet.

[0033] The method for evaluating the reliability of an energy storage system based on a mission profile extracts the cycle and calendar components in the battery SOC curve, wherein the cycle capacity decay refers to the phenomenon that the capacity of the energy storage battery gradually decreases during the charge and discharge cycle, and the calendar capacity decay refers to the battery capacity loss caused by chemical reactions when the battery is in long-term storage or standby state.

[0034] The reliability assessment method of the energy storage system based on the mission profile extracts the thermal load information of the power device and the capacitor, and uses the rain flow counting method to count the thermal load information of the power device and the capacitor. The rain flow counting method is similar to the falling process of raindrops. The counting principle of the rain flow counting method is single-phase cycle and cycle until all counts are extracted. Figure 5 As shown, the specific steps are as follows:

[0035] Step 1: Start from the valley or peak value, and stop the loop when a valley or peak value is lower or higher than the starting point;

[0036] Step 2: Starting from the valley / peak value, the cycle stops when encountering raindrops flowing down the upper slope;

[0037] Step 3: Complete the operations of step 1 and step 2 and extract all loops;

[0038] Step 4: After completing steps 1, 2, and 3, a diverging-converging wave will be left. At this time, you need to cut the waveform into two segments at the peak or trough, and then connect the leftmost and rightmost ends of the two segments, and repeat processes 1, 2, and 3 until the recording is completed.

[0039] The reliability assessment method of the energy storage system based on the mission profile calculates the battery life damage based on the battery life model. The battery cycle aging capacity attenuation model is expressed as

[0040]

[0041] Where n c is the number of cycles; SOC cyc is the average state of charge in each cycle; cd is the depth of charge and discharge (DOD) of the energy storage battery; T B is the battery operating temperature;

[0042] The calendar aging capacity attenuation model is expressed as:

[0043]

[0044] Where, t cal is the battery standby time; SOC cal It is the fixed charge state of the energy storage battery when it is at rest.

[0045] The reliability assessment method of the energy storage system based on the mission profile calculates the life damage of the power device based on the power device life model, and selects the Bayerer model for life assessment. The model can be expressed as

[0046]

[0047] Where, ΔT j is the junction temperature fluctuation; T jmin is the minimum junction temperature; t on is the heating time of the power cycle; I is the current passing through the bonding wire; D is the bonding wire diameter; V is the blocking voltage; A, β1, β2, β3, β4, β5, and β6 are the Bayerer model parameters.

[0048] The mission profile-based energy storage system reliability assessment method calculates capacitor life damage based on a capacitor life model, and the capacitor life model takes into account the influence of thermal load and voltage.

[0049]

[0050] Where, L and L0 are the life of the capacitor under actual working conditions and test conditions, respectively; V and V0 are the voltage under actual working conditions and test conditions, respectively; T and T0 are the temperatures under actual working conditions and test conditions, respectively; E a is the activation energy of the capacitor; K B is the Boltzmann constant; n is the voltage stress exponent.

[0051] The reliability assessment method of an energy storage system based on a mission profile implements the reliability assessment of the energy storage system based on Monte-Carlo simulation, obtains the life distribution of power devices through Monte-Carlo random sampling simulation, fits the scale parameter and shape parameter of the two-parameter Weibull distribution, and thus obtains the Weibull probability density function (Probability Density Function, PDF) of the device life distribution.

[0052]

[0053] Where: β is the scale parameter of Weibull distribution; η is the shape parameter of Weibull distribution;

[0054] The lifetime distribution probability density curve is integrated to obtain the Weibull cumulative distribution function (CDF), which is then used to evaluate the unreliability of the energy storage system.

[0055]

[0056] On this basis, the reliability operation curve of the energy storage system is obtained, such as Figure 6 shown. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 Flowchart of reliability assessment of energy storage system based on mission profile;

[0058] Figure 2 Energy storage battery mission profile;

[0059] Figure 3 Power device hot spot coupling model;

[0060] Figure 4 Capacitor hot spot coupling model;

[0061] Figure 5 Rainflow counting method;

[0062] Figure 6 Reliability operation curve of energy storage system.

Claims

1. A reliability assessment method for energy storage systems based on mission profiles, characterized in that: The method mainly includes the following steps: S1. Obtaining the mission profile of the energy storage system; S2. Estimate the state of charge of the energy storage battery and extract the SOC curve of the energy storage battery; S3. Calculate the junction temperature of the power devices in the energy storage converter; S4. Calculate the hot spot temperature of the capacitor in the energy storage converter; S5. Extracting cycle and calendar components from the battery SOC curve; S6. Extracting thermal load information of power devices and capacitors; S7. Calculate battery life damage based on the battery life model; S8. Calculate the life damage of power devices based on the power device life model; S9. Calculate capacitor life damage based on capacitor life model; S10.Reliability evaluation of energy storage system based on Monte-Carlo simulation.

2. The reliability assessment method of energy storage system based on mission profile is characterized in that: The obtaining of the energy storage system mission profile includes active output of the energy storage system and ambient temperature.

3. The reliability assessment method of energy storage system based on mission profile is characterized in that: The energy storage battery state of charge is estimated and the energy storage battery SOC curve is extracted; the change value of the battery state of charge SOC is obtained by integrating the loop current over time and then dividing it by the rated capacity, and then adding it to the initial value to obtain the current battery SOC, thereby obtaining the energy storage battery SOC operation curve In the formula, SOC(0) is the initial state of charge of the battery, i in (t) is the battery input current, C e is the rated capacity of the battery.

4. The reliability assessment method of energy storage system based on mission profile is characterized in that: The power device junction temperature in the energy storage converter is calculated by using a power device thermoelectric coupling model. The power device electrical loss is divided into switching loss and conduction loss, and the calculation formula is: In the formula, is the conduction loss of the power device; δ(t) is the duty cycle; i a is the output current of the power device; T j is the junction temperature of the power device; V CE_25℃ is the rated on-state voltage drop of the power device at 25°C; r CE_25℃ K is the rated on-state resistance of the power device at 25°C; V_T K is the temperature influence coefficient of the on-state voltage drop of the power device; r_T is the temperature influence coefficient of the on-state resistance of the power device; f sw K is the frequency of turning on and off the power device module; sw is the temperature influence coefficient of the switching loss of the power device; K1(V dc ) is the voltage coefficient of the DC side voltage of the power device affecting the switching power loss of the power device; K2(R g ) is the gate resistance R g The resistance coefficient that affects the switching power loss of power devices; Use the Foster model and power loss model to perform thermoelectric conversion of power devices and convert the power loss profile into a thermal stress profile Where, T j is the junction temperature of the power device; T a is the ambient temperature; is the power loss of the power device and the anti-parallel diode; Z tjh , Z ha is the thermal impedance parameter in the Foster model.

5. The reliability assessment method of energy storage system based on mission profile is characterized in that: The hot spot temperature of the capacitor in the energy storage converter is calculated by using a capacitor thermoelectric coupling model to calculate the capacitor temperature, wherein the total power loss of the DC capacitor can be expressed as: In the formula, R ESR is the ESR of the DC bus capacitor; I c is the current harmonic amplitude of the capacitor; then the hot spot temperature of the DC capacitor can be expressed as: T h =P c,loss R th +T a (7) Where, T h is the hot spot temperature of the capacitor; T a is the ambient temperature; R th is the thermal resistance of the capacitor given in the datasheet.

6. The reliability assessment method of energy storage system based on mission profile is characterized in that: The cycle and calendar components in the battery SOC curve are extracted, wherein the cycle capacity decay refers to the phenomenon that the capacity of the energy storage battery gradually decreases during the charge and discharge cycle, and the calendar capacity decay refers to the battery capacity loss caused by chemical reactions when the battery is stored for a long time or in standby state.

7. The reliability assessment method of energy storage system based on mission profile is characterized in that: Extract the thermal load information of power devices and capacitors, and use the rain flow counting method to count the thermal load information of power devices and capacitors. The rain flow counting method is similar to the falling process of raindrops. The counting principle of the rain flow counting method is single-phase cycle and cycle until all counts are extracted. The specific steps are as follows: Step 1: Start from the valley or peak value, and stop the loop when a valley or peak value is lower or higher than the starting point; Step 2: Starting from the valley / peak value, the cycle stops when encountering raindrops flowing down the upper slope; Step 3: Complete the operations of step 1 and step 2 and extract all loops; Step 4: After completing steps 1, 2, and 3, a diverging-converging wave will be left. At this time, you need to cut the waveform into two segments at the peak or trough, and then connect the leftmost and rightmost ends of the two segments, and repeat processes 1, 2, and 3 until the recording is completed.

8. The reliability assessment method of energy storage system based on mission profile is characterized in that: The battery life damage is calculated based on the battery life model. The battery cycle aging capacity attenuation model is expressed as: Where n c is the number of cycles; SOC cyc is the average state of charge in each cycle; cd is the depth of charge and discharge (DOD) of the energy storage battery; T B is the battery operating temperature; The calendar aging capacity attenuation model is expressed as: Where, t cal is the battery standby time; SOC cal It is the fixed charge state of the energy storage battery when it is at rest.

9. The reliability assessment method of energy storage system based on mission profile is characterized in that: Based on the power device life model, the power device life damage is calculated, and the Bayerer model is selected for life assessment. The model can be expressed as In the formula, ΔT j is the junction temperature fluctuation; T jmin is the minimum junction temperature; t on is the heating time of the power cycle; I is the current passing through the bonding wire; D is the bonding wire diameter; V is the blocking voltage; A, β1, β2, β3, β4, β5, and β6 are the Bayerer model parameters.

10. The reliability assessment method of energy storage system based on mission profile is characterized in that: Calculate capacitor life damage based on capacitor life model, capacitor life model, taking into account the influence of thermal load and voltage Where, L and L0 are the life of the capacitor under actual working conditions and test conditions, respectively; V and V0 are the voltage under actual working conditions and test conditions, respectively; T and T0 are the temperatures under actual working conditions and test conditions, respectively; E a is the activation energy of the capacitor; K B is the Boltzmann constant; n is the voltage stress exponent.

11. The method for evaluating the reliability of an energy storage system based on a mission profile is characterized in that: The reliability assessment of energy storage system is realized based on Monte-Carlo simulation. The lifetime distribution of power devices is obtained by random sampling simulation of Monte-Carlo. The scale parameter and shape parameter of the two-parameter Weibull distribution are fitted to obtain the Weibull probability density function (PDF) of the device lifetime distribution. The lifetime distribution probability density curve is integrated to obtain the Weibull cumulative distribution function (CDF), which is then used to evaluate the unreliability of power devices. Where: β is the scale parameter of Weibull distribution; η is the shape parameter of Weibull distribution.

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