CN115219912A - Early fault diagnosis and safety advanced early warning method and system for energy storage battery - Google Patents
Early fault diagnosis and safety advanced early warning method and system for energy storage battery Download PDFInfo
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- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 4
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- GELKBWJHTRAYNV-UHFFFAOYSA-K lithium iron phosphate Chemical compound [Li+].[Fe+2].[O-]P([O-])([O-])=O GELKBWJHTRAYNV-UHFFFAOYSA-K 0.000 description 2
- 239000000178 monomer Substances 0.000 description 2
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- 239000002904 solvent Substances 0.000 description 2
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Abstract
The invention discloses a method and a system for early fault diagnosis and safe advanced early warning of an energy storage battery, wherein the method comprises the following steps: acquiring a voltage signal of an energy storage battery; performing a preliminary fault diagnosis result by adopting a plurality of fault diagnosis methods based on the voltage signal; mapping the preliminary fault diagnosis results of the multiple fault diagnosis methods into a characteristic value sequence; performing convex function processing on the characteristic value sequence, and adding bias; and integrating the processed characteristic value sequence with respect to time to obtain an early fault diagnosis result of the energy storage battery. Compared with the traditional fault diagnosis method, the fault diagnosis method has the advantages of small missed diagnosis risk and low misdiagnosis probability. Under the same misdiagnosis rate, the missed diagnosis rate is obviously reduced compared with the traditional fault diagnosis algorithm. For early tiny faults of the battery, the diagnosis precision and the diagnosis speed of the method are far superior to those of the traditional fault diagnosis method, so that the method can sense potential malignant accident risks more effectively and realize safe advanced early warning.
Description
Technical Field
The invention relates to the technical field of lithium battery fault diagnosis, in particular to an energy storage battery early fault diagnosis and safety advanced early warning method and system.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The lithium ion battery is used as a complex nonlinear system sensitive to environment and noise, has numerous fault types, hidden fault characteristics and extremely large individual difference, and has the double challenges of high misdiagnosis rate and high missed diagnosis rate in fault diagnosis, and the difficulty in accurately finding and positioning the fault is extremely large. In addition, the development speed of lithium ion battery faults is very fast, and the faults can cause the battery to release energy and generate heat in a large amount in a short time until the battery is combusted and exploded, for example, the internal short circuit of the battery is taken as an example. In order to avoid such a serious accident, a Battery Management System (BMS) must have the ability to discover and discriminate early faults, shorten the fault diagnosis time, and implement a safe advance warning to reduce the risk caused by the battery fault as much as possible. This places demands on the fault diagnosis subsystem of the BMS in terms of algorithm design and system architecture.
However, the concealment of lithium ion battery failure is particularly prominent in the early stages of failure, and the failure at this stage often causes only a very small variation in battery parameters. Taking a lithium iron phosphate strip battery as an example, the voltage change of the lithium iron phosphate strip battery is usually not more than 3% of the rated voltage at the early stage of the fault, which is very similar to the parameter change caused by environmental noise.
Most of the existing fault diagnosis algorithms adopt a threshold value type diagnosis method. And the fault diagnosis system carries out fault alarm when the abnormal characteristic value of the battery caused by the fault breaks through a preset threshold value. In practical application, the conventional diagnostic algorithm usually adopts a conservative high threshold setting to avoid fault misdiagnosis caused by environmental noise. But at the same time, the risk of an early failure being missed due to feature concealment is also high. If the threshold value is reduced to reduce the failure leakage rate, the lower threshold value can be frequently touched by abnormal characteristic values caused by environmental noise, so that frequent misdiagnosis is caused, and the normal operation of the battery system is influenced. It can be seen that the conventional threshold-based fault diagnosis method cannot solve the contradiction between misdiagnosis and missed diagnosis.
The prior art discloses a battery fault diagnosis method based on sample entropy, which is characterized in that a corrected sample entropy value sensitive to a fault type is obtained by correcting voltage characteristics related to the fault type on the basis of the sample entropy of a battery voltage sequence and is used as a basis for fault diagnosis. On the premise of obvious fault characteristics, stable load operation and small environmental noise, the method has good fault diagnosis effect. However, if the battery has a light fault, a large environmental noise and various load variations, the algorithm faces the contradiction that the missed diagnosis rate and the misdiagnosis rate cannot be simultaneously low, and the expected diagnosis effect is difficult to achieve.
The fault diagnosis method disclosed in the prior art obtains characteristic parameters with close fault relation through signal processing means such as wavelet change and the like. The parameter still needs to be compared with a threshold value to judge whether the fault exists or not. In addition, although the filtering means in the feature extraction process suppresses the influence of part of the environmental noise, the processing process also has a weakening effect on the features of early faults, and the dilemma that the missed diagnosis rate and the misdiagnosis rate cannot be both eliminated still cannot be overcome.
The battery fault diagnosis method based on the characteristic threshold is too simple in diagnosis mode, and only can find abnormality in the characteristic level and distinguish faults and noise by using the threshold. This determines that they are difficult to distinguish for small, noise-like faults and cannot be diagnosed. This also results in the failure diagnosis algorithm not finding the battery failure at an early stage, and thus, the risk of accidents of the battery system cannot be really reduced. In the prior art, a better diagnosis effect can be obtained only by distinguishing battery faults and environmental noises essentially from a diagnosis mode, so that the purpose of avoiding risks is achieved.
In addition, the diagnosis from a single angle is difficult to deal with all the possible fault types of the battery system, for example, the fault diagnosis method based on sample entropy is very sensitive to the fault type causing the battery parameter to change sharply, and is not good for the fault type causing the battery parameter to change slowly, such as leakage fault. The fault diagnosis method based on the correlation coefficient has a significant effect on the type of fault occurring in the battery cell, but is difficult to handle the battery pack fault. Therefore, the diagnosis at a single angle cannot guarantee the diagnosis effect of various types of faults.
Disclosure of Invention
In order to solve the problems, the invention provides a method and a system for early fault diagnosis and safe advanced early warning of an energy storage battery, which overcome the influence of noise and the like through a time sequence dependent fault diagnosis strategy and a hierarchical system structure and realize high-precision and high-sensitivity diagnosis of early micro faults of the battery; through accumulation and amplification of tiny abnormity, potential faults are monitored, malignant fault risks are further reduced, and a good safety advanced early warning effect is achieved.
In some embodiments, the following technical scheme is adopted:
an early fault diagnosis and safety advance early warning method for an energy storage battery comprises the following steps:
acquiring a voltage signal of an energy storage battery;
performing a preliminary fault diagnosis result by adopting a plurality of fault diagnosis methods based on the voltage signal;
mapping the preliminary fault diagnosis results of the multiple fault diagnosis methods into a characteristic value sequence;
performing convex function processing on the characteristic value sequence, and adding bias;
and integrating the processed characteristic value sequence with respect to time to obtain an early fault diagnosis result of the energy storage battery.
In other embodiments, the following technical solutions are adopted:
an energy storage battery early fault diagnosis and safety advanced early warning system comprises:
the data acquisition module is used for acquiring a voltage signal of the energy storage battery;
the fault preliminary diagnosis module is used for performing preliminary fault diagnosis results by adopting a plurality of fault diagnosis methods based on the voltage signals;
the preliminary fault diagnosis result processing module is used for mapping the preliminary fault diagnosis results of the multiple fault diagnosis methods into a characteristic value sequence; performing convex function processing on the characteristic value sequence, and adding bias;
and the early fault diagnosis module is used for integrating the processed characteristic value sequence with respect to time to obtain an early fault diagnosis result of the energy storage battery.
In other embodiments, the following technical solutions are adopted:
a terminal device comprising a processor and a memory, the processor being arranged to implement instructions; the memory is used for storing a plurality of instructions, and the instructions are suitable for being loaded by the processor and executing the early fault diagnosis and safety advance warning method of the energy storage battery.
In other embodiments, the following technical solutions are adopted:
a computer-readable storage medium, wherein a plurality of instructions are stored, and the instructions are adapted to be loaded by a processor of a terminal device and execute the method for early fault diagnosis and safety advance warning of an energy storage battery.
Compared with the prior art, the invention has the beneficial effects that:
(1) Compared with the traditional fault diagnosis method, the fault diagnosis method has the advantages of small missed diagnosis risk and low misdiagnosis probability. Under the same misdiagnosis rate, the missed diagnosis rate is obviously reduced compared with the traditional fault diagnosis algorithm. The diagnosis precision and the diagnosis speed of the method are far better than those of the traditional fault diagnosis method for the early tiny faults of the battery.
(2) According to the method, the diagnosable fault types are increased by analyzing the faults from multiple angles, and the risk of fault omission is further reduced; the algorithm has low complexity, strong practicability and larger application space.
(3) The invention can specifically distinguish the battery fault and the noise, solves the contradiction between the missed diagnosis rate and the misdiagnosis rate, and provides possibility for the diagnosis of the early tiny fault of the battery.
Additional features and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a schematic diagram of a method for diagnosing early failure of an energy storage battery based on accumulation of abnormal values according to an embodiment of the present invention;
FIG. 2 is a schematic voltage curve diagram of four battery cells in an embodiment of the present invention;
FIGS. 3 (a) and 3 (b) are parameter curves of fault detection of four single cells by using a sample entropy method and a correlation coefficient method in the embodiment of the present invention;
fig. 4 (a) and 4 (b) are the results of fault diagnosis using the method of the present embodiment;
FIG. 5 is a ROC curve for three fault diagnosis algorithms.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
In one or more embodiments, an energy storage battery early fault diagnosis and safety advance warning method is disclosed, which, with reference to fig. 1, specifically includes the following steps:
(1) Acquiring a voltage signal of an energy storage battery;
specifically, a voltage signal of the energy storage battery is acquired through a sensor.
(2) Performing a preliminary fault diagnosis result by adopting a plurality of fault diagnosis methods based on the voltage signal;
in this embodiment, the diagnostic results of the selected fault diagnosis method are all presented in the form of battery characteristic values in a numerical form, and the value range should be included in a non-negative number domain and positively correlated with the fault probability.
The fault diagnosis method selected by the embodiment comprises the following steps: a sample entropy method, a correlation coefficient method and a current, voltage and temperature comprehensive comparison method; the sample entropy method uses the sample entropy of the battery voltage sequence in a certain time scale as a battery characteristic value of a diagnosis basis, and calculates the sample entropy of the battery voltage sequence in the latest period in real time through the sliding of a window. And at the moment, the sample entropy of the voltage sequence at the moment t is the algorithm diagnosis result. The correlation coefficient method is used for calculating the correlation coefficient between the voltages of the single batteries connected in series within a certain time scale, and the correlation coefficient is used as a battery characteristic basis for fault diagnosis. And at the moment t, the voltage correlation coefficient between the single batteries is the algorithm diagnosis result. The current, voltage and temperature comprehensive comparison method is used for counting the average values of current, voltage and temperature in the running process of the battery, and the Euclidean distance between a vector consisting of the three parameters of the battery at the moment to be diagnosed and a vector consisting of the average values is used as a battery characteristic basis for fault diagnosis, wherein the Euclidean distance is the diagnosis result of the algorithm.
Of course, those skilled in the art can select other fault diagnosis methods for calculation according to needs.
The fault diagnosis results of the sample entropy method and the current, voltage and temperature comprehensive comparison method are numerical results positively correlated with the fault probability and can be directly used; the diagnosis result of the correlation coefficient method is a numerical value which is in a value range of [0,1] and is negatively correlated with the fault probability, and the numerical value needs to be mapped according to the following rule:
d i ′(t)＝-d i (t)+1
wherein, d i (t) is the result of the fault diagnosis at time t using the correlation coefficient method, d i ' (t) is the result after mapping, d after mapping i ' (t) instead of d i (t) as input for a subsequent integration process U. After mapping, the value domain is changed into a function value which is contained in a non-negative number domain and is positively correlated with the fault probability.
(3) Mapping the preliminary fault diagnosis results of the multiple fault diagnosis methods into a characteristic value sequence related to time;
in particular, the amount of the solvent to be used,
u(t)＝U(d 1 (t),d 2 (t),…,d n (t))
wherein d is i (t) represents the fault diagnosis result of the ith fault diagnosis algorithm at the time t, and u (t) is a characteristic parameter d i (t) the result of the integration process, U, is an abstraction of the integration method. Specifically in this embodiment, i =1,2,3.
In this embodiment, U has two calculation modes to choose from according to the application scenario:
(1) tends to ensure low missed diagnosis rate, and when the algorithm effect in the initial diagnosis link is known, a root mean square form with additional weight can be adopted, that is,
wherein, a 1 ,a 2 ,…,a n The weight parameter is determined by a priori experience.
(2) With a focus on system stability, extreme forms may be used when there is a tendency to ensure low misdiagnosis rates, at which time
u(t)＝max[a 1 ·d 1 (t),a 2 ·d 2 (t),…,a n ·d n ]
Wherein, constant a 1 ,a 2 ,…,a n Is used to normalize d i (t), the values are made to approach.
Since the integrated diagnostic results are all positively correlated with the failure probability, u (t) is a feature quantity positively correlated with the failure probability at time t after integration.
(4) Performing convex function processing on the characteristic value sequence, and adding bias;
in this embodiment, the convex function processing process is as follows:
c(t)＝C(u(t))+b
c is a mapping function which carries out convex function processing on the characteristic u (t) and adds bias b, and after the C processing, the characteristic u (t) is mapped into a convex function C (t) related to the fault probability.
In this example, C has two alternative forms:
(1) when the power supply is used for a battery system with variable working states, in order to ensure the stability of the system, a power function is selected as concrete of C:
c(t)＝u(t) e +b
wherein e is a constant, and the convex functionalization effect is more obvious when e is larger, and e should be larger than 1 in application.
(2) When the method is used for a battery system with a relatively constant working state, in order to find abnormality acutely, an exponential function is selected as concretization of C, including,
c(t)＝e u(t) +b
wherein e is a constant, and the convex functionalization effect is more obvious when e is larger, and e should be larger than 1 in application.
b is an offset constant, and the value range of c (t) after offset crosses a zero point and comprises a negative value part. Due to the characteristics of the convex function, when the fault probability rises, the value of c (t) rises at a greater and greater rate, which is beneficial to the rapid diagnosis of dangerous faults, and the introduction of the bias enables the value of c (t) to be negative when the working state of the battery is normal, so that the integral value is reduced in the subsequent integration.
(5) And integrating the processed characteristic value sequence with respect to time to obtain an early fault diagnosis result of the energy storage battery.
In particular, the amount of the solvent to be used,
where o (t) is the integral of u (t) over time, which is also the output value of the frame.
When the value of o (t) exceeds a preset threshold, the battery is considered to have failed. For fault diagnosis of lithium ion batteries, finding a fault is often more important than determining the type of the fault. Therefore, the present embodiment focuses more on the detection of faults. When a fault causes o (t) to break through the threshold, the type of the fault can be roughly determined from the shape of the curve of o (t) in the period before the fault is found. If the curve has larger gradient and is slightly bent in the rising process, the internal short circuit fault of the battery can be preliminarily diagnosed. If the curve rising process is accompanied with fluctuation and a short-time falling phenomenon exists, the battery can be preliminarily diagnosed to have poor contact fault. If the curve rises extremely and reaches the threshold value quickly, the battery sensor can be diagnosed preliminarily that the battery sensor has a fault.
The application of the method in the embodiment in the fault diagnosis of the battery pack of the electric vehicle is taken as an example for explanation, and the fault characteristics of the battery pack are considered, and the initial diagnosis is performed by using a sample entropy method and a correlation coefficient method. The value of r in the sample entropy method is set to be 0.15, and the window sizes of the two algorithms are both selected to be 20.
Selecting the root mean square of the additional weights as the integration method, weight a 1 ,a 2 Are respectively 20, 15, i.e.,
and (3) selecting an exponential form to carry out convex functionalization treatment on u (t), wherein the value of a is 3.5, and the value of b is 0.5. Namely:
c(t)＝3.5 u(t) -0.5
the above parameters are only for the real experimental conditions of the present embodiment, and the necessary parameter adjustment is needed when the application is applied to other scenes.
The voltage data (obtained by simulation experiments) of four series-connected battery monomers in the existing battery pack of the electric automobile. Fig. 2 depicts the voltage curves of the four battery cells. The left frame is encircled by the single body 4, and the middle frame is encircled by the single body 4, so that the whole battery pack is in failure, and the voltage signals of all the single batteries are abnormal. In the right frame circle-out interval, the single body 4 is influenced by load fluctuation, the voltage signal is abnormal, but the single body 4 does not break down at the moment, and the abnormality is caused by noise.
The four sections of monomers are subjected to fault detection by using a sample entropy method and a correlation coefficient method, and the obtained parameter curves are shown in fig. 3 (a) and fig. 3 (b). As can be seen from fig. 3 (a), the sample entropy method diagnoses the battery pack overall fault indicated by the middle box, but the reaction to the battery cell fault marked by the left box is obvious. And when the noise indicated by the right frame occurs, the sample entropy makes an obvious reaction, and the visible sample entropy method cannot make obvious distinction between the battery fault and the noise.
Similar problems exist with the correlation coefficient method, and the curve of fig. 3 (b) is a correlation coefficient curve of the cell voltage and the average voltage of four cells. It can be seen that the fault circled by the left frame in the voltage curve graph causes the reduction of the correlation coefficient and is accurately identified by the method, but the fault which is indicated by the middle frame and occurs in the battery pack and causes the same influence on all the battery cells cannot be effectively identified. The parameter abnormality caused by the noise also causes the battery to see a decrease in the correlation coefficient, and is treated as a failure.
FIG. 4 (a) is a result of fault diagnosis of a same faulty cell based on the disclosed method; according to the graph, two different faults are identified by the algorithm, and in the fault duration period, the diagnosis indexes based on the abnormal accumulation type fault diagnosis algorithm reach preset peak values, so that the algorithm has the diagnosable fault types of a sample entropy method and a correlation coefficient method, and has a larger diagnosis range. In addition, the maximum noise influence is not enough to enable the diagnostic index to reach a preset peak value, which shows that the influence of noise on fault diagnosis is diluted in the accumulation process of abnormal values, so that the overall anti-noise capacity of the fault diagnosis system is improved. Fig. 4 (b) is a comparison of the diagnosis results of a normal battery and a faulty battery in a noisy environment, where battery 1,3 is a normal battery, and battery 2 is a faulty battery; it can be known from the figure that under the influence of strong noise, the diagnostic indexes of all the batteries are increased, but only the fault battery 2 finally touches the preset threshold value to trigger the fault alarm, and the diagnostic index of the normal battery is in a descending trend along with the attenuation of the noise intensity. Comparing fig. 3 (a) and fig. 3 (b), the algorithm of the present embodiment effectively shields noise interference, and has a fault discrimination capability far exceeding that of the conventional diagnostic algorithm.
And repeating a plurality of groups of similar experiments, wherein the false positive rate of the algorithm diagnosis result is taken as the abscissa, and the curve with the true positive rate as the ordinate is the ROC curve of the fault diagnosis algorithm. Fig. 5 plots ROC curves of the three algorithms, and the ROC curve of the fault diagnosis algorithm based on the accumulation of abnormal values in the present embodiment completely includes curves of the other two fault diagnosis algorithms. The area difference of the AUG (area surrounded by the curve) is obvious, and the fault diagnosis algorithm provided by the embodiment is obviously improved compared with the traditional fault diagnosis algorithm.
Example two
In one or more embodiments, disclosed is an early failure diagnosis and safety advance warning system for an energy storage battery, comprising:
the data acquisition module is used for acquiring a voltage signal of the energy storage battery;
the fault preliminary diagnosis module is used for performing preliminary fault diagnosis results by adopting a plurality of fault diagnosis methods based on the voltage signals;
the primary fault diagnosis result processing module is used for mapping the primary fault diagnosis results of the multiple fault diagnosis methods into a characteristic value sequence; performing convex function processing on the characteristic value sequence, and adding bias;
and the early fault diagnosis module is used for integrating the processed characteristic value sequence with respect to time to obtain an early fault diagnosis result of the energy storage battery.
It should be noted that, the specific implementation of each module described above has been described in detail in the first embodiment, and is not described in detail here.
EXAMPLE III
In one or more embodiments, a terminal device is disclosed, which includes a server, where the server includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the energy storage battery early failure diagnosis and safety advance warning method in the first embodiment when executing the program. For brevity, no further description is provided herein.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software.
Example four
In one or more embodiments, a computer-readable storage medium is disclosed, in which a plurality of instructions are stored, and the instructions are adapted to be loaded by a processor of a terminal device and execute the method for early fault diagnosis and safety advance warning of an energy storage battery in the first embodiment.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. An early fault diagnosis and safety advanced early warning method for an energy storage battery is characterized by comprising the following steps:
acquiring a voltage signal of an energy storage battery;
performing a preliminary fault diagnosis result by adopting a plurality of fault diagnosis methods based on the voltage signal;
mapping the preliminary fault diagnosis results of the multiple fault diagnosis methods into a characteristic value sequence;
performing convex function processing on the characteristic value sequence, and adding bias;
and integrating the processed characteristic value sequence with respect to time to obtain an early fault diagnosis result of the energy storage battery.
2. The early fault diagnosis and safety advanced early warning method of the energy storage battery as claimed in claim 1, wherein a plurality of fault diagnosis methods are adopted to perform the preliminary fault diagnosis result, and the fault diagnosis method comprises: sample entropy method, correlation coefficient method, and current, voltage, temperature comprehensive comparison method.
3. The energy storage battery early fault diagnosis and safety advance early warning method as claimed in claim 1, wherein the preliminary fault diagnosis results of the multiple fault diagnosis methods are mapped to a characteristic value sequence with respect to time, specifically:
u(t)＝U(d 1 (t),d 2 (t),…,d n (t))
wherein d is i (t) represents the fault diagnosis result of the ith fault diagnosis algorithm at the time t, i =1,2, …, n; u (t) is a pair characteristic parameter d i (t) integrating the processed feature quantities.
4. The early-stage fault diagnosis and safety early-warning method for the energy storage battery as claimed in claim 3, wherein the characteristic parameter d is calculated i (t) integrated treatment, specifically:
wherein, a 1 ,a 2 ,…,a n Is a weight parameter.
5. The method for early diagnosis of the faults of the energy storage battery and early warning of the safety as claimed in claim 3, wherein the characteristic parameter d is set i (t) integrated treatment, specifically:
u(t)＝max[a 1 ·d 1 (t),a 2 ·d 2 (t),…,a n ·d n (t)]
wherein, a 1 ,a 2 ,…,a n Is a weight parameter.
6. The early fault diagnosis and safety advance early warning method of the energy storage battery as claimed in claim 1, characterized in that the characteristic value sequence is processed by a convex function, and a bias is added; the method specifically comprises the following steps:
c(t)＝u(t) e +b
where c (t) is a convex function and b is an offset.
7. The early fault diagnosis and safety advance early warning method of the energy storage battery as claimed in claim 1, characterized in that the characteristic value sequence is processed by a convex function, and a bias is added; the method specifically comprises the following steps:
c(t)＝e u(t) +b
where c (t) is a convex function and b is an offset.
8. The utility model provides an energy storage battery early failure diagnosis and safe advanced early warning system which characterized in that includes:
the data acquisition module is used for acquiring a voltage signal of the energy storage battery;
the fault preliminary diagnosis module is used for performing preliminary fault diagnosis results by adopting a plurality of fault diagnosis methods based on the voltage signals;
the preliminary fault diagnosis result processing module is used for mapping the preliminary fault diagnosis results of the multiple fault diagnosis methods into a characteristic value sequence; performing convex function processing on the characteristic value sequence, and adding bias;
and the early fault diagnosis module is used for integrating the processed characteristic value sequence with respect to time to obtain an early fault diagnosis result of the energy storage battery.
9. A terminal device comprising a processor and a memory, the processor being arranged to implement instructions; the memory is used for storing a plurality of instructions, wherein the instructions are suitable for being loaded by the processor and executing the energy storage battery early failure diagnosis and safety advance warning method according to any one of claims 1 to 7.
10. A computer-readable storage medium having stored thereon a plurality of instructions, wherein the instructions are adapted to be loaded by a processor of a terminal device and execute the method for early fault diagnosis and safety advance warning of an energy storage battery according to any one of claims 1 to 7.
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