Intelligent electric meter battery reliability evaluation method and system

Abstract

The invention relates to a method for evaluating the reliability of a battery of an intelligent electric meter, which is characterized by comprising the following steps of: carrying out pulse test on a sample battery of the intelligent electric meter so as to obtain pulse parameters of the sample battery of the intelligent electric meter; utilizing the pulse parameters to construct a capacity model of the sample intelligent electric meter battery, and adopting a least square method to solve capacity model parameters; fitting the capacity attenuation speed of the sample intelligent electric meter battery according to the accelerated degradation test; and acquiring operation parameters of the current intelligent electric meter battery in the working state, solving the failure time of the current intelligent electric meter battery by using the capacity model parameters and the capacity attenuation rate, and giving an alarm based on the failure time.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and more specifically, to a smart meter battery reliability assessment method and system. Background Art

[0002] Smart meters can not only accurately measure electricity consumption, but also realize real-time transmission of user electricity consumption data and remote control by power companies through remote communication technology. As the core power supply unit of smart meters, the performance and life of batteries are crucial to the stable operation of smart meters. Since smart meter batteries need to work for a long time and are usually in an environment without human monitoring, how to effectively predict their life and evaluate their reliability has become an important issue in the current technical field.

[0003] Existing smart meter battery evaluation technologies have the following shortcomings: First, many evaluation methods rely too much on the instantaneous parameters of the battery, such as voltage and current, but these parameters cannot fully reflect the health status of the battery, especially the capacity decay of the battery after long-term operation and the performance changes caused by internal chemical reactions. Second, existing technologies usually ignore the impact of environmental factors on battery life. The working environment of smart meters is often changeable, and fluctuations in conditions such as temperature and humidity will significantly affect the degradation rate of the battery. Existing evaluation methods are difficult to effectively simulate the effects of these external conditions on the battery aging process.

[0004] In view of the above problems, a smart meter battery reliability assessment method and system are urgently needed. Summary of the invention

[0005] In order to solve the deficiencies in the prior art, the present invention provides a smart meter battery reliability assessment method and system, which constructs a capacity model of the smart meter battery through pulse testing and fits the capacity decay rate function of the smart meter battery through accelerated degradation testing to assess the failure time of the current smart meter.

[0006] The present invention adopts the following technical solution.

[0007] A first aspect of the present invention relates to a method for evaluating the reliability of a smart meter battery, the method comprising the following steps: performing a pulse test on a sample smart meter battery to obtain pulse parameters of the sample smart meter battery; constructing a capacity model of the sample smart meter battery using the pulse parameters, and solving the capacity model parameters using the least squares method; fitting the capacity decay rate of the sample smart meter battery according to an accelerated degradation test; collecting operating parameters of the current smart meter battery in a working state, solving the failure time of the current smart meter battery using the capacity model parameters and the capacity decay rate, and issuing an alarm based on the failure time.

[0008] Preferably, a pulse test is performed on the sample smart meter battery to obtain the pulse parameters of the sample smart meter battery, including: selecting multiple groups of sample smart meter batteries with different initial battery capacities, and performing a pulse load discharge test on each group of sample smart meter batteries; during the test, collecting the pulse voltage, pulse current, internal resistance and pulse duration of the sample smart meter battery, and the load time of the battery, and recording the ambient temperature during the test and the internal temperature of the sample smart meter battery.

[0009] Preferably, the capacity model of the sample smart meter battery is constructed using pulse parameters, including: constructing a second battery capacity model based on the pulse power and internal resistance of the smart meter battery, ambient temperature, and battery load time; constructing a first battery capacity model based on the pulse current of the smart meter, pulse duration, and the temperature difference between the smart meter battery and the environment; and summing the first battery capacity model and the second battery capacity model to obtain the capacity model of the sample smart meter battery.

[0010] Preferably, the capacity model of the sample smart meter battery is constructed by using the pulse parameters, including: the capacity model of the sample smart meter battery is:

[0011]

[0012] Where V p (t) is the instantaneous value of the pulse voltage at time t during the pulse test,

[0013] I p (t) is the instantaneous value of the pulse current at time t during the pulse test.

[0014] R i (t) is the internal resistance of the battery at time t during the pulse test,

[0015] T i (t) is the internal temperature of the battery at time t during the pulse test,

[0016] τ p is the pulse duration,

[0017] T e is the ambient temperature,

[0018] α1, α2, β1, β2, γ1, γ2 and δ1 are the model parameters respectively.

[0019] Preferably, the least square method is used to solve the capacity model parameters, including: to solve the model parameters, calculate

[0020]

[0021] Where n is the total number of tests,

[0022] C m,i is the actual battery capacity collected during the i-th test,

[0023] C p,i is the simulated battery capacity during the i-th test simulated using the capacity model.

[0024] Preferably, the capacity decay rate of the sample smart meter battery is fitted according to the accelerated degradation test; the ambient temperature and load current of the sample smart meter battery are set, and the sample smart meter battery is continuously discharged; during the continuous discharge process, the remaining capacity and current temperature of the sample smart meter battery are collected at preset time points; a capacity decay model is constructed, and the parameters of the capacity decay model are fitted based on the initial capacity, remaining capacity, current temperature, and interval time between preset time points of the sample smart meter battery; and the capacity decay rate of the sample smart meter battery is calculated using the solved capacity decay model parameters.

[0025] Preferably, constructing a capacity decay model includes: the capacity decay model is:

[0026] C t =C b (t)exp{-K[T i (t)]*t r}

[0027] In the formula, C t is the failure battery capacity threshold,

[0028] C b (t) is the current capacity of the smart meter battery,

[0029] K[·] is the capacity decay rate function,

[0030] t r The current smart meter battery expiration time.

[0031] Preferably, the operating parameters of the current smart meter battery in the working state are collected, and the failure time of the current smart meter battery is solved by using the capacity model parameters and the capacity decay rate, including: collecting the operating parameters of the current smart meter battery in the working state, and solving the current capacity of the current smart meter battery by using the capacity model and the capacity model parameters; solving the failure time of the current smart meter battery by using the current capacity, the capacity decay rate and the capacity decay model.

[0032] Preferably, collecting the operating parameters of the current smart meter battery in the working state includes: collecting the pulse voltage, pulse current, pulse duration, internal resistance, battery internal temperature, and ambient temperature of the current smart meter battery as the operating parameters of the current smart meter battery.

[0033] A second aspect of the present invention relates to a smart meter battery reliability assessment device; the device includes an acquisition module, a preset module, and a calculation module; wherein the acquisition module is used to acquire operating parameters of the current smart meter battery in a working state; the preset module is used to pre-store capacity model parameters obtained during a pulse test of the sample smart meter battery and a capacity decay rate of the sample smart meter battery fitted according to an accelerated degradation test; the calculation module is used to use the capacity model parameters and the capacity decay rate to solve the failure time of the current smart meter battery, and evaluate the reliability of the current smart meter battery based on the failure time.

[0034] The beneficial effect of the present invention is that, compared with the prior art, a smart meter battery reliability assessment method and system in the present invention constructs a capacity model of the smart meter battery through pulse testing, and uses accelerated degradation testing to fit the capacity attenuation rate function of the smart meter battery, so as to assess the failure time of the current smart meter.

[0035] The beneficial effects of the present invention also include:

[0036] By simulating the working conditions of batteries in different temperature environments and capturing the accelerated aging process of batteries, especially their performance at extreme temperatures, the deficiency of existing technologies that ignore the impact of ambient temperature factors on battery life is effectively solved, thereby achieving a more realistic working environment simulation and improving the accuracy of battery life prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 The present invention is a flowchart of a smart meter battery reliability assessment method. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solution and advantages of the present invention clearer and more accurate, the technical solution of the present invention is described in detail below through multiple specific implementation methods. The embodiments adopted by the present invention are only used to explain the present invention and are not used to limit the content of the present invention.

[0039] Figure 1 FIG. 1 is a flow chart of a method for evaluating the reliability of a smart meter battery according to the present invention. Figure 1 As shown, the first aspect of the present invention relates to a smart meter battery reliability assessment method, the method comprising steps 1 to 4.

[0040] Step 1: Perform a pulse test on a sample smart meter battery to obtain pulse parameters of the sample smart meter battery.

[0041] A pulse test is performed on a sample smart meter battery to obtain pulse parameters of the sample smart meter battery, including: selecting multiple groups of sample smart meter batteries with different initial battery capacities, and performing a pulse load discharge test on each group of sample smart meter batteries; during the test, the pulse voltage, pulse current, internal resistance and pulse duration of the sample smart meter battery are collected, and the ambient temperature during the test and the internal temperature of the sample smart meter battery are recorded.

[0042] Before the pulse test, the smart meter battery can be processed using passivation elimination, static treatment and pulse load discharge test methods to obtain multiple groups of sample smart meter batteries with different initial battery capacities, and their initial capacities are recorded.

[0043] In one example, after the passivation elimination is completed, the internal resistance of the battery is monitored. If the internal resistance drops to less than 50% of the internal resistance before passivation, the passivation elimination process is considered successful. If the internal resistance fails to reach the expected value, the passivation elimination process is repeated until the internal resistance of the battery drops to the target value. After the passivation elimination is completed, the battery needs to be left to stand to ensure that the internal chemical reaction of the battery returns to stability and to avoid the short-term activity after the passivation elimination from misleading subsequent tests. Set the standing time, during which the battery is placed at a constant ambient temperature to avoid the influence of external temperature fluctuations on the internal chemical reaction of the battery.

[0044] A pulse test is performed on each group of sample smart meter batteries to obtain their pulse voltage, pulse current, internal resistance and pulse duration. At the same time, the ambient temperature and internal temperature of each sample smart meter battery during the pulse test are recorded and a data table is formed.

[0045] In one embodiment, after the static treatment is completed, a pulse load discharge test is performed to obtain the pulse characteristic parameters of the battery, set the pulse discharge current and pulse duration, and set the pulse current to 2-3 times the rated current of the battery according to the battery specifications to simulate the working state of the battery under extreme load. The pulse duration is set to 200ms to ensure that the pulse test does not cause excessive discharge to the battery. At the set ambient temperature, a pulse current is applied to the pulse duration, and the pulse voltage and current of the battery are recorded, and the voltage drop during the pulse discharge process is also recorded.

[0046] Select multiple groups of sample smart meter batteries with different initial battery capacities, which can be in the range of 60%-100% of the rated capacity of the battery, to ensure that the model can cover batteries in different health states. The number of sample batteries is no less than 10 groups to ensure the reliability and fitting accuracy of the statistical results. For each group of sample smart meter batteries, its initial capacity is recorded.

[0047] In one embodiment, the smart meter battery is a lithium thionyl chloride battery of the same model. According to the battery actually used in the smart meter, the method of the present invention can be applied to batteries of other models.

[0048] Step 2: Use the pulse parameters to construct a capacity model of the sample smart meter battery, and use the least squares method to solve the capacity model parameters.

[0049] The capacity model of the sample smart meter battery is constructed using pulse parameters, including: constructing a second battery capacity model according to the pulse power and internal resistance of the smart meter battery, ambient temperature, and test duration; constructing a first battery capacity model according to the pulse current of the smart meter, pulse duration, and the temperature difference between the smart meter battery and the environment; and summing the first battery capacity model and the second battery capacity model to obtain the capacity model of the sample smart meter battery.

[0050] The product integral of the pulse voltage and the pulse current describes the battery discharge amount. By analyzing its impact on the battery capacity, the main correlation between the battery discharge amount and the battery capacity change can be obtained. Ambient temperature and battery internal resistance also affect the battery capacity change to a certain extent. On the other hand, the difference between the battery's operating temperature and the ambient temperature describes to a certain extent the process of converting the battery's internal chemical energy into electrical energy, while the pulse duration and instantaneous current characterize the impact of unstable load access on the battery capacity.

[0051] The capacity model of the sample smart meter battery is constructed by using the pulse parameters, including: the capacity model of the sample smart meter battery is:

[0052]

[0053] Where V p (t) is the instantaneous value of the pulse voltage at time t during the pulse test,

[0054] I p (t) is the instantaneous value of the pulse current at time t during the pulse test.

[0055] R i (t) is the internal resistance of the battery at time t during the pulse test,

[0056] T i (t) is the internal temperature of the battery at time t during the pulse test,

[0057] τ p is the pulse duration,

[0058] T e is the ambient temperature,

[0059] α1, α2, β1, β2, γ1, γ2 and δ1 are the model parameters respectively.

[0060] The least square method is used to solve the capacity model parameters, including: To solve the model parameters, calculate

[0061]

[0062] In the formula, i is the test sample number, n is the total number of test samples,

[0063] C m,i is the actual battery capacity collected during the i-th test,

[0064] C p,i is the simulated battery capacity during the i-th test simulated using the capacity model.

[0065] The error between the simulated battery capacity and the actual battery capacity is calculated. When the error sum is minimum, the least square method converges to the optimum, and the model parameters at this time are used as the optimal model parameters of the current smart meter battery to be tested.

[0066] Under different working conditions, pulse tests are performed on sample smart meter batteries, new pulse parameters are collected, and the actual battery capacity is measured. The pulse parameters are then substituted into the battery capacity detection model to calculate the current capacity of the battery. The error between the calculated value and the measured value is compared. If the error is large, the parameters in the battery capacity detection model are readjusted.

[0067] Compared with the traditional linear capacity detection model, this model more accurately captures the dynamic characteristics of the battery by introducing the complex nonlinear relationship between pulse voltage, current and temperature. It adopts the integral form of pulse voltage and current, which can reflect the instantaneous response of the battery under different loads. It introduces nonlinear terms of temperature difference and pulse time, which can accurately describe the influence of ambient temperature and pulse duration on battery capacity. It simulates the saturation effect of battery capacity through the power exponential function, which increases the scope of application of the model.

[0068] Step 3: Fit the capacity decay rate of the sample smart meter battery according to the accelerated degradation test.

[0069] The capacity decay rate of the sample smart meter battery is fitted according to the accelerated degradation test; the ambient temperature and load current of the sample smart meter battery are set, and the sample smart meter battery is continuously discharged; during the continuous discharge process, the remaining capacity and current temperature of the sample smart meter battery are collected at preset time points; a capacity decay model is constructed, and the parameters of the capacity decay model are fitted based on the initial capacity, remaining capacity, current temperature, and interval time between preset time points of the sample smart meter battery; and the capacity decay rate of the sample smart meter battery is calculated using the solved capacity decay model parameters.

[0070] Set multiple temperature conditions for accelerated degradation tests. According to the operating temperature range of lithium thionyl chloride batteries, select three or four typical temperatures. These temperature points cover the normal and high temperature operating ranges of the battery and can effectively accelerate the degradation of the battery. Set the load current for the accelerated degradation test to simulate different power consumption scenarios of smart meter batteries in actual work, and calculate the current initial capacity of the smart meter battery at each temperature.

[0071] Set the test cycle and test frequency. The test at each temperature should last from several weeks to several months, depending on the battery degradation rate. In order to capture the continuity of battery degradation, perform a capacity test at fixed intervals. Set multiple temperature conditions for accelerated degradation tests, set the load current for accelerated degradation tests, simulate different power consumption scenarios of smart meter batteries in actual work, and calculate the current initial capacity of smart meter batteries at each temperature.

[0072] According to the influence of temperature on the battery capacity attenuation rate, the battery capacity attenuation rate is calculated, and the expression is:

[0073]

[0074] Where T is the current absolute temperature of the battery, k0 is the pre-exponential factor, and E a is the activation energy and R is the molar gas constant.

[0075] The smart meter battery is placed under set temperature and load current conditions, and the discharge test is continuously performed. At each set time point, the test is stopped and the remaining battery capacity of the battery at the set time point is collected. Based on the accelerated degradation test results, a battery capacity degradation model is constructed.

[0076] Construct a capacity decay model, including:

[0077] The capacity decay model is:

[0078] C t =C b (t)exp{-K[T i (t)]*t r}

[0079] In the formula, C t is the failure battery capacity threshold,

[0080] C b (t) is the current capacity of the smart meter battery,

[0081] K[·] is the capacity decay rate function,

[0082] t r The current smart meter battery expiration time.

[0083] Use some accelerated degradation test data to verify the model. Keep the data under one set of temperature conditions as the verification set, use the remaining data for fitting, and then apply the fitted model to the verification set to observe the error between the remaining capacity calculated by the model and the actual test results.

[0084] Calculate the average relative error on the validation set, expressed as,

[0085]

[0086] Among them, MRE is the error value, C pred (t i ) is the remaining capacity calculated by the battery capacity degradation model, C true (t i ) is the actual test capacity, and n is the number of verifications; if the error value is small, it means that the model has good generalization ability.

[0087] The battery capacity failure threshold of the smart meter battery is set. According to the current remaining battery capacity of the smart meter battery, the time required for the current remaining battery capacity to decay to the failure battery capacity is calculated. The expression is:

[0088]

[0089] The battery capacity degradation model is combined with the calculation results of the battery capacity detection model to predict the failure time of the smart meter battery and evaluate the reliability of the battery.

[0090] Step 4: collect operating parameters of the current smart meter battery in the working state, use capacity model parameters and capacity decay rate to solve the failure time of the current smart meter battery, and evaluate the reliability of the current smart meter battery based on the failure time.

[0091] Collecting the operating parameters of the current smart meter battery in the working state includes: collecting the pulse voltage, pulse current, pulse duration, internal resistance, battery internal temperature, and ambient temperature of the current smart meter battery as the operating parameters of the current smart meter battery.

[0092] During the operation of the battery, a pulse load is set, that is, a short-term high-power load. While the pulse load is applied, a voltage sensor and a current sensor are used to collect the instantaneous voltage and current of the battery. The smart meter will have a built-in current sensor and voltage sensor to collect pulse current and voltage signals when the battery is working, and obtain the instantaneous value of the battery pulse voltage and the instantaneous value of the pulse current.

[0093] Internal resistance refers to the resistance inside the battery, which reflects the energy loss of the battery when it is working. The measurement of internal resistance can be carried out through AC impedance test or DC internal resistance test. The AC impedance test method is to apply a small AC signal to both ends of the battery, measure the phase difference and amplitude difference between the voltage and current, and then calculate the internal resistance of the battery. The DC internal resistance test method is to apply a pulse current to both ends of the battery, measure the transient voltage drop of the battery, and calculate the internal resistance of the battery using Ohm's law. The present invention adopts online internal resistance monitoring, and integrates an internal resistance monitoring module in the smart meter. When the battery is working, the internal resistance change of the battery is calculated in real time by applying a pulse load and measuring the voltage change. In this way, the internal resistance of the battery can be monitored uninterruptedly during the operation of the battery.

[0094] A temperature sensor is placed near the battery casing. The temperature sensor can collect the temperature inside the battery in real time. In the application of smart meters, a temperature sensor is arranged near the battery to monitor the operating temperature of the battery. The sensor is placed on the battery casing, close to the core of the battery, to reflect the changes in the internal temperature as accurately as possible. The ambient temperature is measured by the external temperature sensor of the meter, which is located outside the battery and is used to monitor the temperature of the environment where the battery is located.

[0095] The load time of the battery is determined by controlling the load test time of the battery. The instantaneous value of the battery pulse voltage, the instantaneous value of the battery pulse current, the pulse duration, the internal resistance, the internal temperature of the battery and the ambient temperature collected in real time are input into the battery capacity detection model to calculate the current battery capacity of the smart meter battery in real time.

[0096] The operating parameters of the current smart meter battery in a working state are collected, and the failure time of the current smart meter battery is solved by using capacity model parameters and capacity decay rate, including: collecting the operating parameters of the current smart meter battery in a working state, and solving the current capacity of the current smart meter battery by using capacity model and capacity model parameters; solving the failure time of the current smart meter battery by using the current capacity, capacity decay rate and capacity decay model.

[0097] Combine the battery capacity degradation model with the calculation results of the battery capacity detection model to predict the failure time of the smart meter battery. First, perform a pulse test on the sample smart meter battery, collect new pulse parameters, substitute the pulse parameters into the battery capacity detection model, and calculate the current capacity of the battery. Secondly, calculate the battery capacity of the current smart meter battery through the battery capacity model, and record the current working environment temperature and load current of the smart meter battery. Combine the battery capacity degradation model and the Arrhenius equation to calculate the battery capacity decay rate under the current working environment temperature and load current.

[0098] The battery capacity detection model is used to calculate the current battery capacity of the smart meter battery in real time. When the current battery capacity is close to the battery capacity failure threshold, an alarm is issued to prompt the user to replace the battery.

[0099] The total failure time of the battery can also be predicted by predicting the power consumption pattern when the smart meter is running stably, monitoring the working status of the smart meter battery, and issuing an alarm when the current usage time of the battery is close to the total failure time.

[0100] A second aspect of the present invention relates to a smart meter battery reliability assessment device; the device includes an acquisition module, a preset module, and a calculation module; wherein the acquisition module is used to acquire the operating parameters of the current smart meter battery in a working state; the preset module is used to pre-store the capacity model parameters obtained during the pulse test of the sample smart meter battery and the capacity decay rate of the sample smart meter battery fitted according to the accelerated degradation test; the calculation module is used to use the capacity model parameters and the capacity decay rate to solve the failure time of the current smart meter battery, and evaluate the reliability of the current smart meter battery based on the failure time.

[0101] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention is described in detail with reference to the above embodiments, it should be understood by those skilled in the art that the technical solutions of the present invention still include contents that can modify or replace the specific embodiments of the present invention. Any modification or replacement that does not depart from the spirit and scope of the present invention shall be included in the protection scope of the claims of the present invention.

Claims

1. A smart meter battery reliability assessment method, characterized in that: The method comprises the following steps: Performing a pulse test on a sample smart meter battery to obtain pulse parameters of the sample smart meter battery; The capacity model of the sample smart meter battery is constructed using pulse parameters, and the least square method is used to solve the capacity model parameters; The capacity decay rate of the sample smart meter battery is fitted according to the accelerated degradation test; The operating parameters of the current smart meter battery in the working state are collected, the failure time of the current smart meter battery is solved by using the capacity model parameters and the capacity decay rate, and an alarm is issued based on the failure time.

2. A smart meter battery reliability assessment method according to claim 1, characterized in that: The pulse test is performed on the sample smart meter battery to obtain the pulse parameters of the sample smart meter battery, including: Select multiple groups of sample smart meter batteries with different initial battery capacities, and perform a pulse load discharge test on each group of sample smart meter batteries; During the test, the pulse voltage, pulse current, internal resistance, pulse duration, and load time of the sample smart meter battery are collected, and the ambient temperature during the test and the internal temperature of the sample smart meter battery are recorded.

3. A smart meter battery reliability assessment method according to claim 2, characterized in that: The method of constructing a capacity model of a sample smart meter battery using pulse parameters includes: Constructing a second battery capacity model according to the pulse power and internal resistance of the smart meter battery, the ambient temperature, and the load time of the battery; Constructing the first battery capacity model based on the pulse current of the smart meter, the pulse duration, and the temperature difference between the smart meter battery and the environment; The first battery capacity model and the second battery capacity model are summed to obtain a capacity model of the sample smart meter battery.

4. A smart meter battery reliability assessment method according to claim 3, characterized in that: The method of constructing a capacity model of a sample smart meter battery using pulse parameters includes: The capacity model of the sample smart meter battery is: Where V p (t) is the instantaneous value of the pulse voltage at time t during the pulse test. I p (t) is the instantaneous value of the pulse current at time t during the pulse test. R i (t) is the internal resistance of the battery at time t during the pulse test, T i (t) is the internal temperature of the battery at time t during the pulse test, τ p is the pulse duration, T e is the ambient temperature, α1, α2, β1, β2, γ1, γ2 and δ1 are the model parameters respectively.

5. A smart meter battery reliability assessment method according to claim 4, characterized in that: The least square method is used to solve the capacity model parameters, including: To solve the model parameters, calculate Where n is the total number of tests, C m，i is the actual battery capacity collected during the i-th test, C p，i is the simulated battery capacity during the i-th test simulated using the capacity model.

6. A smart meter battery reliability assessment method according to claim 5, characterized in that: The capacity decay rate of the sample smart meter battery is fitted according to the accelerated degradation test; Set the ambient temperature and load current of the sample smart meter battery, and continuously discharge the sample smart meter battery; During the continuous discharge process, the remaining capacity and current temperature of the sample smart meter battery are collected at preset time points; Constructing a capacity decay model, and fitting the parameters of the capacity decay model based on the initial capacity, remaining capacity, current temperature, and interval time between preset time points of the sample smart meter battery; The capacity decay rate of the sample smart meter battery is calculated using the solved capacity decay model parameters.

7. A smart meter battery reliability assessment method according to claim 6, characterized in that: The constructing of the capacity attenuation model comprises: The capacity decay model is: C t =C b (t)exp{-K[T i (t)]*t r } In the formula, C t is the failure battery capacity threshold, C b (t) is the current capacity of the smart meter battery, K[·] is the capacity decay rate function, t r The current smart meter battery expiration time.

8. A smart meter battery reliability assessment method according to claim 7, characterized in that: The collecting the operating parameters of the current smart meter battery in the working state, and solving the failure time of the current smart meter battery by using the capacity model parameters and the capacity decay rate, includes: Collecting operating parameters of a current smart meter battery in a working state, and solving a current capacity of the current smart meter battery using a capacity model and capacity model parameters; The current capacity, capacity decay rate and capacity decay model are used to calculate the failure time of the current smart meter battery.

9. A smart meter battery reliability assessment method according to claim 8, characterized in that: The collecting of the current operating parameters of the smart meter battery in the working state includes: The pulse voltage, pulse current, pulse duration, internal resistance, battery internal temperature, and ambient temperature of the current smart meter battery are collected as operating parameters of the current smart meter battery.

10. A smart meter battery reliability assessment device; characterized in that: The device includes a collection module, a preset module, and a calculation module; wherein, The acquisition module is used to acquire the operating parameters of the current smart meter battery in the working state; The preset module is used to pre-store the capacity model parameters obtained during the pulse test of the sample smart meter battery and the capacity decay rate of the sample smart meter battery fitted according to the accelerated degradation test; The calculation module is used to solve the failure time of the current smart meter battery by using the capacity model parameters and the capacity decay rate, and evaluate the reliability of the current smart meter battery based on the failure time.

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