Storage battery health state assessment method, system, equipment and medium

Abstract

The invention belongs to the technical field of battery fault prediction, and relates to a storage battery health state assessment method, system and device and a medium. The method comprises the following steps: acquiring key operation parameters of the storage battery; calculating storage battery health state evaluation index parameters by using the key operation parameters; the storage battery health state evaluation index parameters comprise the storage battery internal resistance, the internal resistance change rate, the storage battery charging state, the storage battery operation age limit and the storage battery operation temperature; preprocessing the storage battery health state evaluation index parameters to obtain preprocessed health state evaluation data; inputting the preprocessed health state evaluation data into a constructed health degree evaluation model to obtain a storage battery health degree representing the health state of the storage battery; and performing trend dynamic prediction on the health degree of the storage battery through a time sequence analysis algorithm to obtain a trend prediction result. The problem that an existing method is not accurate enough in battery fault prediction is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of battery failure prediction, and in particular relates to a method, system, device and medium for evaluating the health status of a battery. Background Art

[0002] With the continuous development of new distribution network technologies, the reliability and stability of distribution terminal equipment (FTU, DTU, etc.) are crucial to the overall operation of the distribution system. Batteries, as key energy storage components of distribution terminals, are usually used to support the control and communication functions of distribution terminal equipment. However, the health status of batteries will deteriorate with the use time and changes in environmental conditions, which directly affects the reliability of distribution terminals. At present, battery health status monitoring mainly relies on regular manual inspections or simple evaluations through a single indicator (such as voltage). This method cannot reflect the health status of batteries in real time and accurately, and there are potential risks.

[0003] After searching, the patent document with publication number CN115407216A discloses an online monitoring system for the health status of the battery of the ring main unit, including: a monitoring terminal and a monitoring server; a slave control module is used to collect the voltage, current, and temperature data information of the battery, and transmit the collected data information to the master control module; a data processing unit is used to estimate the SOC value of the battery based on the SOC algorithm when the battery is in the charging or discharging state; the battery current I is detected in real time, and the battery SOC value at the current time is estimated by the Ampere integral method, and the battery charge and discharge power is obtained, and the battery attenuation state is obtained by the charge and discharge power and the SOC value; the battery internal resistance, capacity and other parameters can be accurately identified online, and the battery health status can be accurately judged locally and the command operation and maintenance can be preset in time, and the early warning data signal can be uploaded. This device ensures the reliability of the battery of the ring main unit as a backup power supply. Although the patent document case includes temperature monitoring, it does not fully consider the comprehensive impact of temperature on battery performance and life, and lacks an effective temperature compensation mechanism:

[0004] 1) The impact of temperature on SOC calculation: Temperature has a significant impact on the charge and discharge characteristics of the battery. Under low temperature conditions, the internal resistance of the battery increases and the electrochemical reaction rate decreases, resulting in inaccurate SOC estimation. There is no mention of how to dynamically adjust the SOC in combination with temperature changes, nor is a temperature compensation model introduced.

[0005] 2) The long-term impact of temperature on battery degradation: Temperature not only affects the immediate performance of the battery, but also has a significant impact on its long-term health status, especially under long-term high temperature conditions, the battery capacity decay will accelerate. The patent does not mention how to estimate the long-term battery degradation trend by monitoring temperature changes, and lacks the ability to predict health degradation caused by thermal effects.

[0006] Therefore, developing a multi-parameter-based dynamic health status assessment method that can monitor the health status of batteries in real time and predict possible faults and hidden dangers in advance is of great significance for ensuring the stable operation of distribution terminals. Summary of the invention

[0007] The purpose of the present invention is to provide a method, system, device and medium for evaluating the health status of a battery, which solves the problem that the existing methods are not accurate enough in predicting battery failures.

[0008] The present invention is achieved through the following technical solutions:

[0009] The present invention discloses a method for evaluating the health status of a battery, comprising:

[0010] Obtaining key operating parameters of the battery in the power distribution terminal; the key operating parameters include battery voltage, battery charging current, battery operating temperature and battery operating life;

[0011] Calculating battery health status evaluation index parameters using key operating parameters; the battery health status evaluation index parameters include battery internal resistance, internal resistance change rate and battery charging state, battery operating years and battery operating temperature;

[0012] Preprocessing the battery health status evaluation index parameters to obtain preprocessed health status evaluation data;

[0013] The preprocessed health status assessment data is input into the constructed health evaluation model to obtain the battery health representing the health status of the battery;

[0014] The trend of battery health is dynamically predicted through time series analysis algorithm to obtain trend prediction results.

[0015] Further, the calculation process of the battery internal resistance is as follows:

[0016] The linear regression is performed using the least square method to fit the linear relationship between the battery voltage and the battery charging current, thereby obtaining a linear regression model;

[0017] The linear regression model is:

[0018] U i =aI i +b

[0019] Among them, U i is the battery voltage, I i The battery charging current;

[0020] Based on the linear regression model, a is calculated using the least squares method:

[0021]

[0022] Where n is the number of continuous sampling of voltage and current in a certain continuous time; i = 1, 2...n; a certain continuous time is from t-▽t to t;

[0023] The internal resistance of the battery before temperature compensation is compensated by the temperature compensation model to obtain the internal resistance of the battery. The temperature compensation model is as follows:

[0024] R c (t) = R (t) · (1 + α (T (t) - T ref ))

[0025] Among them, R c (t) is the internal resistance of the battery, specifically the internal resistance value after temperature compensation at time t; R(t) = a, R(t) is the internal resistance of the battery before temperature compensation calculated at time t; α is the temperature compensation coefficient; T(t) is the operating temperature of the battery at time t; T ref is the reference operating temperature.

[0026] Furthermore, the internal resistance change rate ΔR c Calculated by the following formula:

[0027]

[0028] The calculation of the internal resistance change rate is based on the internal resistance values ​​measured at different time points; R c (t1) represents the internal resistance of the battery obtained at time t1; R c (t2) represents the internal resistance of the battery obtained at time t2.

[0029] Furthermore, the battery state of charge is calculated using the constructed SOC temperature compensation model, and the SOC temperature compensation model is as follows:

[0030]

[0031] Among them, SOC com (t) is the battery SOC after temperature compensation at time t; SOC com (t0) is the battery SOC after temperature compensation at the initial time t0; U com (t) is the battery voltage after temperature compensation at time t; com (t) is the battery current after temperature compensation at time t; k is a constant;

[0032] U com (t) = U ocv (t)+β·(T(t)-T ref )

[0033] Uocv (t) = U (t) - I (t) × R c (t)

[0034] I com (t) = I (t) · (1 + γ (T (t) - T ref ))

[0035] SOC com (t0) = f ocv (U com (t0)

[0036] Among them, U ocv (t) is the voltage of the battery at time t; β is the temperature compensation coefficient of the voltage; T(t) is the operating temperature of the battery at time t; T ref is the reference operating temperature;

[0037] U(t) is the battery voltage at time t; I(t) is the battery charging current at time t; γ is the current correction factor based on temperature; R c (t) is the internal resistance of the battery; f ocv () is the standard relationship function between voltage and SOC obtained by experimental fitting; U com (t0) is the battery voltage after temperature compensation at the initial time t0.

[0038] Furthermore, the health evaluation model is constructed according to the battery health status evaluation index parameters, and the expression is:

[0039] H(t)=ω1·f1(R c (t))+ω2·f2(ΔR c (t))+ω3·f3(SOC com (t))+ω4·f4(A(t))+ω5·f5(T(t))

[0040] Among them, H(t) represents the battery health; ω1, ω2, ω3, ω4, ω5 are the weights of each indicator, satisfying R c (t) represents the internal resistance of the battery, SOC com (t) represents the battery charge state, A(t) represents the operating life of the battery at time t; T(t) is the operating temperature of the battery at time t;

[0041] f1, f2, f3, f4, and f5 are functions that map each indicator to standard health.

[0042] Further, the f1(R c (t)) The calculation model is as follows:

[0043]

[0044] Among them, R er is the typical internal resistance of the battery; R fail is the internal resistance of the battery failure;

[0045] f2(ΔR c (t)) The calculation model is as follows:

[0046]

[0047] Where μΔR is the expected change rate of the typical internal resistance of the battery; σΔR is the standard deviation, reflecting the allowable fluctuation range of the change rate;

[0048] f3(SOC com (t)) The calculation model is as follows:

[0049]

[0050] Among them, SOC min It is the minimum SOC allowed for normal operation of the battery; SOC max It is the maximum SOC allowed for normal operation of the battery; SOC er It is the optimal SOC lower limit allowed for normal battery operation;

[0051] The calculation model of f4(A(t)) is as follows:

[0052]

[0053] Among them, A er is the nominal service life of the battery; A max is the maximum service life of the battery;

[0054] f5(T i (t)) The calculation model is as follows:

[0055]

[0056] Among them, T ref is the battery operating reference temperature; T sca is the attenuation factor affected by battery temperature; T min is the lowest temperature allowed for the battery to operate, T max It is the maximum temperature at which the battery is allowed to operate.

[0057] Furthermore, the battery health classification standard is: healthy, good, fair, poor, dangerous;

[0058] The corresponding battery health value range is: healthy: H(t)>90%; good: H(t) between 80% and 90%; normal: H(t) between 60% and 80%; poor: H(t) between 40% and 60%; dangerous: H(t)<40%; H(t) represents the battery health;

[0059] The time series analysis algorithm includes a comprehensive model combining an autoregressive moving average model and an exponentially weighted average model. The autoregressive moving average model is used to predict the short-term operating state of the battery health to obtain a short-term health state prediction value of the battery; the exponentially weighted average model is used to capture the long-term operating trend of the battery health to obtain a long-term health state prediction value of the battery;

[0060] The forecast results of the autoregressive moving average model and the exponentially weighted average model constitute the overall trend forecast results;

[0061] Generate battery operation and maintenance suggestions based on the current battery health and trend prediction results; the battery operation and maintenance suggestions include optimizing charging and discharging strategies, adjusting maintenance cycles, and recommending battery replacement.

[0062] The present invention also discloses a battery health status assessment system, comprising:

[0063] A data acquisition module is used to acquire key operating parameters of the battery in the power distribution terminal; the key operating parameters include battery voltage, battery charging current, battery operating temperature and battery operating life;

[0064] A parameter processing module is used to calculate battery health status evaluation index parameters using key operating parameters; the battery health status evaluation index parameters include battery internal resistance, internal resistance change rate and battery charging state, battery operating life and battery operating temperature;

[0065] A preprocessing module is used to preprocess the battery health status evaluation index parameters to obtain preprocessed health status evaluation data;

[0066] An evaluation module, used for inputting the pre-processed health status evaluation data into the constructed health evaluation model to obtain the battery health representing the health status of the battery;

[0067] The dynamic prediction module is used to dynamically predict the trend of battery health through a time series analysis algorithm to obtain trend prediction results.

[0068] The present invention also discloses a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the battery health status assessment method when executing the computer program.

[0069] The present invention also discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the battery health status assessment method are implemented.

[0070] Compared with the prior art, the present invention has the following beneficial technical effects:

[0071] The present invention relates to a method for dynamically evaluating the health status of batteries for power distribution terminals, which aims to accurately evaluate the health status of batteries by real-time monitoring and analyzing and calculating key parameters such as the internal resistance, internal resistance change rate, operating temperature, operating years and state of charge (SOC) of the battery. The method includes data acquisition and preprocessing, establishment of a health assessment model, real-time health status assessment and trend prediction, and generation of dynamic maintenance suggestions. By constructing a multi-parameter comprehensive health assessment model and combining it with time series analysis technology for trend prediction, the present invention can evaluate the health status of batteries in power distribution terminals in real time and accurately, and provide effective maintenance suggestions to improve the overall operational reliability of power distribution terminal equipment.

[0072] The present invention can not only evaluate the current health status of the battery in real time, but also combine time series analysis technology to dynamically predict the health status and identify potential failure trends in advance. Compared with the static evaluation method in the prior art, the present invention can better cope with the dynamic changes in the health status of the battery and improve the timeliness of early warning and maintenance.

[0073] Compared with the prior art which focuses on batteries of a specific type or environment, the method of the present invention is applicable to various types of batteries and different distribution terminal equipment, has a wider range of applications, and can better adapt to the different needs of smart grids and distribution automation systems.

[0074] Furthermore, the present invention comprehensively evaluates the health status of the battery based on multi-dimensional key parameters, including internal resistance, internal resistance change rate, temperature, operating years and charging state SOC. The existing technology usually only focuses on a single or a few parameters and cannot fully reflect the actual health status of the battery.

[0075] Furthermore, the present invention specifically considers the impact of temperature on the health status of the battery, and corrects parameters such as internal resistance and SOC through a temperature compensation model. The existing technology may ignore the impact of temperature or under-compensate, while the temperature compensation mechanism of the present invention improves the accuracy of evaluation results under different working environments, especially under extreme temperature conditions, and can provide a more reliable health status assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 A flowchart of a method for dynamically evaluating the health status of a battery for a power distribution terminal according to the present invention;

[0077] Figure 2 The calculation of the battery internal resistance R in Example 2 of the present invention c (t) Flowchart of

[0078] Figure 3 The battery charging state SOC in Example 2 of the present invention com (t) Model construction flow chart;

[0079] Figure 4 The present invention is a block diagram of a system for dynamically evaluating the health status of batteries for power distribution terminals. DETAILED DESCRIPTION

[0080] In order to make the purpose, technical solution and advantages of the present invention more clear, the following is further described in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the embodiments described are only part of the embodiments of the present invention, not all embodiments.

[0081] The detailed description of the embodiments of the present invention provided in the following drawings is not intended to limit the scope of the invention claimed for protection, but merely represents a selected embodiment of the present invention. Based on the drawings and embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0082] The features and performances of the present invention are further described in detail below in conjunction with the embodiments.

[0083] Example 1

[0084] like Figure 1 As shown, the present invention discloses a method for dynamically evaluating the health status of a battery for a power distribution terminal, comprising the following steps:

[0085] Obtaining key operating parameters of the battery in the power distribution terminal; the key operating parameters include battery voltage, battery charging current, battery operating temperature and battery operating life;

[0086] Calculating battery health status evaluation index parameters using key operating parameters; the battery health status evaluation index parameters include battery internal resistance, internal resistance change rate and battery charging state, battery operating years and battery operating temperature;

[0087] Preprocessing the battery health status evaluation index parameters to obtain preprocessed health status evaluation data;

[0088] The preprocessed health status assessment data is input into the constructed health evaluation model to obtain the battery health representing the health status of the battery;

[0089] The trend of battery health is dynamically predicted through time series analysis algorithm to obtain trend prediction results.

[0090] Example 2

[0091] Based on the embodiment, the calculation process of the battery health status evaluation index parameters is introduced in detail, as follows.

[0092] 1. Battery internal resistance R c (t)

[0093] By sampling the voltage and current multiple times in a certain continuous period of time, multiple sets of voltage and current data will be obtained. The linear regression is performed using the least squares method to fit the linear relationship between voltage and charging current and obtain a linear regression model. The linear regression model is:

[0094] U i =aI i +b

[0095] Among them, U i is the battery voltage, I i The battery charging current;

[0096] Calculate a and b using the least squares method:

[0097]

[0098] n is the number of continuous sampling of voltage and current in a certain continuous time period, i=1,2...n; the certain continuous time period is from t-▽t to time t.

[0099] The internal resistance of the battery before temperature compensation is calculated according to the linear regression model, and the internal resistance of the battery before temperature compensation is equal to a.

[0100] Generally, as the battery operating temperature increases, the internal resistance generally decreases. To reduce the impact of temperature on the health of the battery, Figure 2 As shown, the internal resistance is compensated by the temperature compensation model, and the temperature compensation model is as follows:

[0101] R c (t) = R (t) · (1 + α (T (t) - T ref ))

[0102] Among them, R c (t) is the internal resistance of the battery, specifically the internal resistance value after temperature compensation at time t; R(t) = a, R(t) is the internal resistance of the battery before temperature compensation calculated at time t; α is the temperature compensation coefficient (the rate of change of internal resistance per degree Celsius, which can be obtained through experiments, such as 0.005Ω / ℃); T(t) is the operating temperature of the battery at time t; T ref is the reference operating temperature (generally 25°C);

[0103] The temperature compensation model based on the battery temperature designed in the present invention can better follow the operating environment of the battery for dynamic evaluation.

[0104] 2. Internal resistance change rate ΔR c , calculated by the following formula:

[0105]

[0106] The calculation of the internal resistance change rate in this method is based on the internal resistance values ​​measured at different time points, and generally days, months, and years are used as the basic calculation units for the change rate.

[0107] 3. Battery State of Charge SOC com (t)

[0108] The present invention describes SOC from the design perspective com (t) construction process.

[0109] like Figure 3 As shown, a comprehensive estimation model of battery SOC before temperature compensation is first constructed, and the expression is as follows:

[0110] SOC(t)=k·SOC ocv (t)+(1-k)·SOC cou (t)

[0111] Among them, SOC(t) is the comprehensive estimation model of battery SOC before temperature compensation; k is the weight factor dynamically adjusted according to the actual working condition of the battery (for example, when the battery is stationary for a long time, the value of k can be increased to rely more on voltage for SOC estimation); SOC ocv (t) Battery SOC estimation model under voltage method; SOC cou(t) Battery SOC estimation model under Coulomb integration method; SOC(t) comprehensive estimation model can flexibly adjust the influence proportion of voltage method and Coulomb integration method according to actual working conditions to improve the robustness of SOC estimation model. ocv (t), SOC cou The model of (t) is as follows:

[0112] SOC ocv (t) = f ocv (U ocv (t))

[0113] U ocv (t) = U (t) - I (t) × R c (t)

[0114] Among them, U ocv (t) is the voltage of the battery at time t; U(t) is the voltage of the battery at time t; I(t) is the charging current of the battery at time t; R c (t) is the internal resistance of the battery; f ocv () is the standard relationship function between voltage and SOC obtained through experimental fitting, which can generally be obtained from the battery technical specification;

[0115]

[0116] SOC(t0)=f ocv (U ocv (t0)

[0117] Among them, SOC cpu (t) is the battery SOC at time t; SOC(t0) is the battery SOC at the initial time t0; C nor is the rated capacity of the energy storage battery (calculated in Ah);

[0118] Temperature has a significant impact on the measurement of voltage and current. In order to improve the estimation accuracy of battery SOC, the present invention introduces a SOC temperature compensation model. The calculation model is as follows:

[0119]

[0120] U com (t) = U ocv (t)+β·(T(t)-T ref )

[0121] I com (t)=I(t)·g(T(t))=I(t)·(1+γ(T(t)-T ref ))

[0122] SOCcom (t0) = f ocv (U com (t0)

[0123] Among them, SOC com (t) is the battery SOC estimation model after temperature compensation; SOC com (t0) is the battery SOC at the initial time t0 after temperature compensation; U com (t) is the battery voltage after temperature compensation at time t; com (t) is the battery current after temperature compensation at time t; k is a constant; T(t) is the operating temperature of the battery at time t;

[0124] β is the temperature compensation coefficient of voltage (for example, -0.006V / ℃, generally obtained from experimental data); γ is the current correction factor based on temperature (0.012 / ℃, generally obtained from experimental data).

[0125] Example 3

[0126] Based on Example 1, a health assessment model is introduced. The health assessment model constructed by the present invention is different from the traditional static or single factor analysis model (common internal resistance measurement, operating years, battery voltage), and is particularly suitable for the complex environment and dynamic operating conditions in the distribution terminal. It is calculated by the following formula:

[0127] H(t)=ω1·f1(R c (t))+ω2·f2(ΔR c (t))+ω3·f3(SOC com (t))+ω4·f4(A(t))+ω5·f5(T(t))

[0128] Among them: H(t) represents the battery health; ω1, ω2, ω3, ω4, ω5 are the weights of each indicator, satisfying f1, f2, f3, f A ,f5 is the function that maps each indicator to the standard health degree;

[0129] The present invention combines actual engineering experience and experimental data to design a specific function for mapping each indicator to the standard health degree as follows:

[0130] f1(R c (t)), the internal resistance usually increases with the aging of the battery. In order to more realistically reflect the degradation process of the battery, the present invention adopts a piecewise exponential decay model to define the standard health function of the battery internal resistance. The calculation model is as follows:

[0131]

[0132] Among them, R er is the typical internal resistance of the battery; R fail It is the internal resistance value of the battery during fault conditions. The typical value is the rated internal resistance value of the battery, which is generally provided by the battery manufacturer and reflects the internal resistance value of the battery under normal operating conditions.

[0133] f2(ΔR c (t)), the internal resistance change rate reflects the rate of increase of the internal resistance of the battery. The greater the change rate, the lower the health. The present invention uses a Gaussian function to reflect the impact of the internal resistance change rate on the health, taking into account the fluctuation characteristics of the change rate. The calculation model is as follows:

[0134]

[0135] Where μΔR is the expected rate of change of the typical internal resistance of the battery (in units similar to ΔR c (t) is consistent, for example, 0.05mΩ / month); σΔR is the standard deviation, reflecting the allowable fluctuation range of the rate of change (unit is the same as ΔR c (t) remains consistent, for example 0.3 mΩ / month).

[0136] f3(SOC com (t)), the battery's charging SOC state has a certain correlation with its health, especially in extremely low or extremely high SOC states, the health may be negatively affected. The present invention uses a typical piecewise function mapping model to evaluate it, and the calculation model is as follows:

[0137]

[0138] Among them, SOC min It is the minimum SOC allowed for normal operation of the battery; SOC max It is the maximum SOC allowed for normal operation of the battery; SOC er It is the optimal SOC lower limit allowed for normal battery operation.

[0139] f4(A(t)), the health of the battery decreases with the increase of operating years. The present invention uses a linear mapping function to express the health status of the battery at different life cycle stages. The calculation model is as follows:

[0140]

[0141] Among them, A er is the nominal service life of the battery; A max It is the maximum service life of the battery.

[0142] f5(T(t)), temperature has an important influence on the health of the battery. The present invention adopts an exponential function to reflect the influence of temperature on the health. The calculation model is as follows:

[0143]

[0144] Among them, T ref is the battery operating reference temperature (generally 25°C); T sca is the attenuation factor affected by battery temperature; T min is the lowest temperature allowed for the battery to operate, T max It is the maximum temperature at which the battery is allowed to operate.

[0145] Example 4

[0146] The battery health grading standard is defined as: healthy, good, general, poor, and dangerous, and the corresponding H(t) value range is: healthy: H(t)>90%; good: H(t) between 80% and 90%; general: H(t) between 60% and 80%; poor: H(t) between 40% and 60%; dangerous: H(t)<40%.

[0147] Example 5

[0148] More preferably, based on Example 1, step S5 is also included: the system automatically generates battery operation and maintenance suggestions based on the health status and trend prediction results of the current distribution terminal battery.

[0149] The battery operation and maintenance suggestions include optimizing charging and discharging strategies, adjusting maintenance cycles, and suggesting battery replacement. The generation of the battery operation and maintenance suggestions is processed through the platform, and real-time monitoring and remote access functions are provided.

[0150] Example 6

[0151] Based on Example 1, a time series analysis algorithm is introduced.

[0152] The time series analysis algorithm includes a comprehensive model combining an autoregressive moving average model (ARIMA) and an exponentially weighted average model (EWMA). The ARIMA model predicts the short-term operating state of the battery health to obtain a short-term health state prediction value of the battery; the EWMA model captures the long-term operating trend of the battery health to obtain a long-term health state prediction value of the battery; the prediction results of the autoregressive moving average model and the exponentially weighted average model constitute an overall trend prediction result.

[0153] For example, if the current health status is 95%, the ARIMA model predicts that the health status in 2 weeks may be 92%, and the EWMA model predicts that the health status in 3 months may be 88%.

[0154] Example 7

[0155] like Figure 4 As shown, the present invention discloses a battery health status assessment system, comprising:

[0156] A data acquisition module is used to acquire key operating parameters of the battery in the power distribution terminal; the key operating parameters include battery voltage, battery charging current, battery operating temperature and battery operating life;

[0157] A parameter processing module is used to calculate battery health status evaluation index parameters using key operating parameters; the battery health status evaluation index parameters include battery internal resistance, internal resistance change rate and battery charging state, battery operating life and battery operating temperature;

[0158] A preprocessing module is used to preprocess the battery health status evaluation index parameters to obtain preprocessed health status evaluation data;

[0159] An evaluation module, used for inputting the pre-processed health status evaluation data into the constructed health evaluation model to obtain the battery health representing the health status of the battery;

[0160] The dynamic prediction module is used to dynamically predict the trend of battery health through a time series analysis algorithm to obtain trend prediction results.

[0161] Example 8

[0162] The present invention also discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for dynamically evaluating the health status of a battery for a power distribution terminal are implemented. The memory may include a memory, such as a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk memory, etc. The processor, the network interface, and the memory are interconnected through an internal bus, which may be an industrial standard architecture bus, a peripheral component interconnection standard bus, an extended industrial standard structure bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory is used to store programs. Specifically, the program may include a program code, and the program code includes computer operation instructions. The memory may include a memory and a non-volatile memory, and provide instructions and data to the processor.

[0163] Example 9

[0164] The present invention also discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for dynamically evaluating the health status of a battery for a power distribution terminal are implemented. Specifically, the computer-readable storage medium includes, but is not limited to, for example, a volatile memory and / or a non-volatile memory. The volatile memory may include a random access memory and / or a cache memory, etc. The non-volatile memory may include a read-only memory, a hard disk, a flash memory, an optical disk, a magnetic disk, etc.

[0165] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) containing computer-usable program code.

[0166] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0167] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0168] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0169] 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 it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for evaluating the health status of a battery, characterized in that: include: Obtaining key operating parameters of the battery in the power distribution terminal; the key operating parameters include battery voltage, battery charging current, battery operating temperature and battery operating life; Calculating battery health status evaluation index parameters using key operating parameters; the battery health status evaluation index parameters include battery internal resistance, internal resistance change rate and battery charging state, battery operating years and battery operating temperature; Preprocessing the battery health status evaluation index parameters to obtain preprocessed health status evaluation data; The preprocessed health status assessment data is input into the constructed health evaluation model to obtain the battery health representing the health status of the battery; The trend of battery health is dynamically predicted through time series analysis algorithm to obtain trend prediction results.

2. A battery health status assessment method according to claim 1, characterized in that: The calculation process of the battery internal resistance is as follows: The linear regression is performed using the least square method to fit the linear relationship between the battery voltage and the battery charging current, thereby obtaining a linear regression model; The linear regression model is: U i =aI i +b Among them, U i is the battery voltage, I i The battery charging current; Based on the linear regression model, a is calculated using the least squares method: Where n is the number of continuous sampling of voltage and current in a certain continuous time; i = 1, 2...n; a certain continuous time is from t-▽t to t; The internal resistance of the battery before temperature compensation is compensated by the temperature compensation model to obtain the internal resistance of the battery. The temperature compensation model is as follows: R c (t)=R(t)·(1+α(T(t)-T ref )) Among them, R c (t) is the internal resistance of the battery, specifically the internal resistance value after temperature compensation at time t; R(t) = a, R(t) is the internal resistance of the battery before temperature compensation calculated at time t; α is the temperature compensation coefficient; T(t) is the operating temperature of the battery at time t; T ref is the reference operating temperature.

3. A battery health status assessment method according to claim 1, characterized in that: Internal resistance change rate ΔR c Calculated by the following formula: The calculation of the internal resistance change rate is based on the internal resistance values ​​measured at different time points; R c (t1) represents the internal resistance of the battery obtained at time t1; R c (t2) represents the internal resistance of the battery obtained at time t2.

4. A battery health status assessment method according to claim 1, characterized in that: The battery state of charge is calculated using the constructed SOC temperature compensation model, which is as follows: Among them, SOC com (t) is the battery SOC after temperature compensation at time t; SOC com (t0) is the battery SOC after temperature compensation at the initial time t0; U com (t) is the battery voltage after temperature compensation at time t; com (t) is the battery current after temperature compensation at time t; k is a constant; U com (t)=U ocv (t)+β·(T(t)-T ref ) U ocv (t)=U(t)-I(t)×R c (t) I com (t)=I(t)·(1+γ(T(t)-T ref )) SOC com (t0)=f ocv (U com (t0)) Among them, U ocv (t) is the voltage of the battery at time t; β is the temperature compensation coefficient of the voltage; T(t) is the operating temperature of the battery at time t; T ref is the reference operating temperature; U(t) is the battery voltage at time t; I(t) is the battery charging current at time t; γ is the current correction factor based on temperature; R c (t) is the internal resistance of the battery; f ocv () is the standard relationship function between voltage and SOC obtained by experimental fitting; U com (t0) is the battery voltage after temperature compensation at the initial time t0.

5. A battery health status assessment method according to claim 1, characterized in that: The health evaluation model is constructed according to the battery health status evaluation index parameters, and the expression is: H(t)=ω1·f1(R c (t))+ω2·f2(ΔR c (t))+ω3·f3(SOC com (t))+ω4·f4(A(t))+ω5·f5(T(t)) Among them, H(t) represents the battery health; ω1, ω2, ω3, ω4, ω5 are the weights of each indicator, satisfying R c (t) represents the internal resistance of the battery, SOC com (t) represents the battery charge state, A(t) represents the operating life of the battery at time t; T(t) is the operating temperature of the battery at time t; f1, f2, f3, f4, and f5 are functions that map each indicator to standard health.

6. A battery health status assessment method according to claim 5, characterized in that: The f1(R c (t)) The calculation model is as follows: Among them, R er is the typical internal resistance of the battery; R fail is the internal resistance of the battery failure; f2(ΔR c (t)) The calculation model is as follows: Where μΔR is the expected change rate of the typical internal resistance of the battery; σΔR is the standard deviation, reflecting the allowable fluctuation range of the change rate; f3(SOC com (t)) The calculation model is as follows: Among them, SOC min It is the minimum SOC allowed for normal operation of the battery; SOC max It is the maximum SOC allowed for normal operation of the battery; SOC er It is the optimal SOC lower limit allowed for normal battery operation; The calculation model of f4(A(t)) is as follows: Among them, A er is the nominal service life of the battery; A max is the maximum service life of the battery; f5(T i (t)) The calculation model is as follows: Among them, T ref is the battery operating reference temperature; T sca is the attenuation factor affected by battery temperature; T min is the lowest temperature allowed for the battery to operate, T max It is the maximum temperature at which the battery is allowed to operate.

7. A battery health status assessment method according to claim 1, characterized in that: The battery health classification standards are: healthy, good, fair, poor, and dangerous; The corresponding battery health value range is: healthy: H(t)>90%; good: H(t) between 80% and 90%; Average: H(t) is between 60% and 80%; Poor: H(t) is between 40% and 60%; Danger: H(t)<40%; H(t) represents the health of the battery; The time series analysis algorithm includes a comprehensive model combining an autoregressive moving average model and an exponentially weighted average model. The autoregressive moving average model is used to predict the short-term operating state of the battery health to obtain a short-term health state prediction value of the battery; the exponentially weighted average model is used to capture the long-term operating trend of the battery health to obtain a long-term health state prediction value of the battery; The forecast results of the autoregressive moving average model and the exponentially weighted average model constitute the overall trend forecast results; Generate battery operation and maintenance suggestions based on the current battery health and trend prediction results; the battery operation and maintenance suggestions include optimizing charging and discharging strategies, adjusting maintenance cycles, and recommending battery replacement.

8. A battery health status assessment system, characterized in that: include: A data acquisition module is used to acquire key operating parameters of the battery in the power distribution terminal; the key operating parameters include battery voltage, battery charging current, battery operating temperature and battery operating life; A parameter processing module is used to calculate battery health status evaluation index parameters using key operating parameters; the battery health status evaluation index parameters include battery internal resistance, internal resistance change rate and battery charging state, battery operating life and battery operating temperature; A preprocessing module is used to preprocess the battery health status evaluation index parameters to obtain preprocessed health status evaluation data; An evaluation module, used for inputting the preprocessed health status evaluation data into the constructed health evaluation model to obtain the battery health representing the health status of the battery; The dynamic prediction module is used to dynamically predict the trend of battery health through a time series analysis algorithm to obtain trend prediction results.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the battery health status assessment method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the battery health status assessment method as claimed in any one of claims 1 to 7 are implemented.

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