A method and system for evaluating the remaining power of a lithium-ion battery

Abstract

The present invention is applicable to the technical field of smart electric meters, and relates to a method and system for evaluating the remaining power of a lithium-ion battery, including: decomposing a signal obtained in a certain sampling process based on a wavelet pyramid decomposition algorithm to obtain three sub-signals; decomposing the sub-signals; calculating the average energy of the noise component in the collected signal; extracting the noise coefficient of the decomposed sub-signals of each layer; reconstructing the signal layer by layer using a wavelet pyramid reconstruction algorithm; calculating the ratio of large noise signals; sending the average energy and the ratio of large noise signals to a master station; the master station evaluates the remaining power of the clock battery, and if the remaining power in the meter meets the conditions, the master station will issue a battery replacement notice for the meter. The present invention has a simple process, convenient operation, accurate evaluation of the remaining power of the meter battery, and achieves more efficient battery resource utilization, reduces the average number of replacement times of the meter battery, and reduces the operation and maintenance cost of the meter throughout the product life cycle.

Description

Technical Field

[0001] The present invention belongs to the technical field of smart electric meters, and in particular relates to a method and system for evaluating the remaining capacity of a lithium-ion battery. Background Art

[0002] Although lithium thionyl chloride battery (referred to as Li-thionyl chloride battery) is a non-rechargeable battery, it has been widely used in some smart meters including electric meters due to its advantages such as stable working voltage, wide temperature adaptability range, high energy density, low self-discharge and long shelf life. According to the national grid standard requirements, the clock battery in the smart meter needs to work normally for at least 5 years, but it is affected by many factors, including the passivation of the battery itself and the problem of factory defective rate, the increase of battery power consumption caused by the performance degradation of the meter components and local circuits in the long-term environment replacement, and the self-discharge and long-term micro-current leakage of the battery during use. Even if Li-thionyl chloride batteries with excellent performance are used, there will still be a high proportion of clock batteries that cannot fully meet the service life requirements. Some electric meters have experienced clock battery undervoltage alarms after two to three years of field operation.

[0003] In order to reduce the probability of clock battery failure, managers often adopt a short-term collective clock battery replacement strategy, that is, all meters in an area are installed with clock batteries of the same manufacturer and model at the same time, and then when a small number of clock battery failures occur in this area, all meters in the area are replaced collectively. Although the above replacement strategy can effectively reduce the number of meter clock battery failures, it is affected by the short board effect. The small number of clock batteries with the worst performance determines the length of the replacement cycle, resulting in most of the replaced batteries still working normally for a long time, thus causing serious waste of battery resources; in addition, the comprehensive cost of replacing a meter clock battery on site is about 10 times the cost of the battery itself, and too frequent battery replacement will also greatly increase the operation and maintenance costs.

[0004] Therefore, under the premise of meeting the stable power supply requirements of the clock battery, accurately evaluating the remaining power of the clock battery so as to achieve on-demand replacement and reduce the number of times the clock battery is replaced during the service life of the meter has become a focus of power grid companies. Summary of the invention

[0005] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a method for evaluating the remaining power of a lithium-ion battery, so as to solve the problem that the battery replacement strategy in the prior art cannot evaluate the remaining power of a lithium-ion battery, but can only replace lithium-ion batteries in a centralized manner, resulting in waste of battery resources and increased operation and maintenance costs; in addition, the present invention also provides a system for evaluating the remaining power of a lithium-ion battery.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a method for evaluating the remaining capacity of a lithium-ion battery, comprising the following steps:

[0008] S10. Assume that the signal obtained by the clock battery sampling circuit during a certain sampling process is The data processing module decomposes the signal x based on the wavelet pyramid decomposition algorithm to obtain the low-pass sub-signal , bandpass signal and high pass sub-signal ; S20, use the wavelet pyramid decomposition algorithm to decompose the low-pass sub-signal , bandpass signal and high pass sub-signal Decompose and get sub-signals 、cd [n] ,ce [n] ; S30, calculate the average energy of the noise component in the collected signal; S40, extract the noise coefficient of the decomposed sub-signal of each layer through the hard threshold function; S50, The value of is set to zero, and the signal in steps S30 and S40 is reconstructed layer by layer using the wavelet pyramid reconstruction algorithm to obtain the signal ,in It indicates a signal Noise components in; S60, set the threshold, calculate the signal The proportion of the absolute value of the digital element in which is greater than the threshold is recorded as the large noise signal ratio value; S70, the average energy in step S30 and the large noise signal ratio value in step S60 are sent to the main station through the communication module of the electric meter; S80, the main station will evaluate the remaining power of the clock battery through the BP neural network algorithm combined with the average energy of the noise component and the large noise signal ratio value reported by the electric meter; S90, if the remaining power evaluation value in a certain electric meter is less than the preset value for three consecutive days, the main station will issue a battery replacement notice for this electric meter, and the tester will go to the site to replace the battery of the electric meter.

[0009] Further, in step S10, the signal x is decomposed to obtain three components with lengths of The sub-signals are:

[0010] ;

[0011] in , and Represent the unit impulse response coefficients of the low-pass, band-pass, and high-pass filters, respectively, so the sub-signal , and They respectively represent the time domain signals obtained after low-pass, band-pass and high-pass filtering of the voltage sampling data.

[0012] Furthermore, in step S20, the low-pass sub-signal After decomposition, the three lengths are The sub-signals are:

[0013]

[0014] where cc [n] Low-pass sub-signal sub-signal, cd [n] Bandpass signal The sub-signal, ce [n] High-pass sub-signal sub-signal.

[0015] Furthermore, in step S30, the average energy is recorded as .

[0016] Furthermore, the step S40 specifically includes:

[0017] and ;

[0018] in is the first layer bandpass sub-signal The noise figure, is the first layer high-pass sub-signal The noise figure, is the second layer bandpass sub-signal cd [n] The noise figure, is the second layer high pass sub-signal ce [n] The noise coefficient of the first layer is , the threshold of the second layer is .

[0019] Furthermore, in step S80, the BP neural network structure includes an input layer, a hidden layer and an output layer. The average energy in step S30 and the large noise signal ratio value in step S60 are used as the input of the model, and the remaining power is used as the output of the model. is the input variable of the model input layer, is the output value of the model output layer, and are the connection weights from the input layer to the hidden layer, and from the hidden layer to the output layer. There is the following corresponding relationship between the unit outputs of each layer:

[0020] and

[0021] Where P i is the input from the input layer to the hidden layer, Q k is the output from the hidden layer to the output layer, is the offset, is the activation function of the hidden layer, is the activation function of the output layer.

[0022] Furthermore, the Select the tansig function, that is:

[0023] ;

[0024] Said Select the linear purelin function, that is:

[0025] ;

[0026] where z is the output of purelin(z).

[0027] Furthermore, the threshold value set in step S60 is .

[0028] Furthermore, in step S90, the preset value is 8%.

[0029] In a second aspect, the present invention further provides a lithium-ion battery remaining capacity assessment system, comprising:

[0030] A DC voltage sampling chip is used to collect voltage data of the meter clock battery; a data processing module is used to decompose and process the voltage data of the meter clock battery; an meter communication module is used to report the data in the data processing module to the main station; a load resistor is used to reduce the power loss of the meter clock battery; a digital switch is used to control the opening and closing of the system; the meter communication module, the data processing module, and the DC voltage sampling chip are connected in communication in sequence, the digital switch is connected in series with the load resistor, the DC voltage sampling chip is connected in parallel with the load resistor, one end of the digital switch is connected to the positive electrode of the meter clock battery, and the other end is connected to one end of the load resistor, and the other end of the load resistor is connected to the negative electrode of the meter clock battery.

[0031] Compared with the prior art, the method and system for evaluating the remaining capacity of a lithium-ion battery provided by the present invention have at least the following beneficial effects:

[0032] The battery replacement strategy of the prior art cannot evaluate the remaining power of lithium-ion batteries, but can only replace lithium-ion batteries in a centralized manner, resulting in a waste of battery resources and increased operation and maintenance costs. The present invention has a simple process and is easy to operate. It uses a wavelet frame function to analyze and process sampling signals, extracts two health factors that are highly correlated with the remaining power of lithium-ion batteries, namely the average energy of the noise component and the ratio of large noise signals, and reports the data to the main station through the meter communication module. The main station evaluates the remaining power of lithium-ion batteries through the BP neural network. The battery remaining power evaluation method provided by the present invention has high accuracy, which can help managers realize on-demand replacement of meter clock batteries, effectively reducing the average number of clock battery replacements during the life cycle of the meter, achieving more efficient battery resource utilization, and reducing the operation and maintenance costs of the meter throughout the product life cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the scheme of the present invention, a brief introduction is given below to the figures required for use in the description of the embodiments. Obviously, the figures described below are some embodiments of the present invention. For ordinary technicians in this field, other figures can be obtained based on these figures without paying any creative work.

[0034] Figure 1 A flowchart of a method for evaluating the remaining power of a lithium-ion battery provided by an embodiment of the present invention;

[0035] Figure 2 A schematic diagram of the structure of a lithium-ion battery remaining capacity evaluation system provided by an embodiment of the present invention;

[0036] Figure 3 A graph between voltage and discharge time in fast discharge mode provided by battery manufacturers;

[0037] Figure 4 A graph showing the average energy of noise components and the ratio of large noise signals of a certain experimental battery at different days in a method for evaluating the remaining capacity of a lithium-ion battery provided by an embodiment of the present invention;

[0038] Figure 5 A diagram showing a result of evaluating the remaining power of a battery in a method for evaluating the remaining power of a lithium-ion battery provided by an embodiment of the present invention;

[0039] FIG6 is a diagram showing the extended life time corresponding to 95 electric meter batteries in a method for assessing the remaining power of a lithium-ion battery provided by an embodiment of the present invention;

[0040] Figure 7 A diagram showing the remaining power of 95 electric meter batteries in a method for assessing the remaining power of a lithium-ion battery provided by an embodiment of the present invention;

[0041] Figure 8 A diagram showing the remaining power of 230 electric meter batteries in a method for evaluating the remaining power of a lithium-ion battery provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by those skilled in the art of the present invention; the terms used in the specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For example, the terms "length", "width", "upper", "lower", "left", "right", "front", "back", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or locations based on the positions or locations shown in the drawings, which are only for the convenience of description and cannot be understood as limiting the present technical solution. The terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions; the terms "first", "second" and the like in the specification and claims of the present invention or the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In the specification and claims of the present invention and the above-mentioned drawings, when an element is referred to as "fixed to" or "mounted on" or "set on" or "connected to" another element, it may be directly or indirectly located on the other element. For example, when an element is referred to as "connected to" another element, it may be directly or indirectly connected to the other element. In addition, reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0043] The present invention provides a method for evaluating the residual current of a lithium-ion battery, which is applied to the operation and maintenance of a clock battery in a smart meter. The method for evaluating the residual current of a lithium-ion battery comprises the following steps: S10, assuming that the signal obtained during a sampling process of a DC voltage sampling chip is The data processing module decomposes the signal x based on the wavelet pyramid decomposition algorithm to obtain the low-pass sub-signal , bandpass signal and high pass sub-signal ; S20, use the wavelet pyramid decomposition algorithm to decompose the low-pass sub-signal Decompose and get sub-signals ; S30, calculate the average energy of the noise component in the collected signal; S40, extract the noise coefficient of the decomposed sub-signal of each layer through the hard threshold function; S50, The value of is set to zero, and the signal in steps S30 and S40 is reconstructed layer by layer using the wavelet pyramid reconstruction algorithm to obtain the signal ,in It indicates a signal Noise components in; S60, set the threshold, calculate the signal The proportion of the absolute value of the digital element in which is greater than the threshold is recorded as the large noise signal ratio value; S70, the average energy in step S30 and the large noise signal ratio value in step S60 are sent to the main station through the communication module of the electric meter; S80, the main station will use the BP neural network algorithm to combine the average energy of the noise component and the large noise signal ratio value reported by the electric meter to evaluate the remaining power of the clock battery; S90, if the remaining power evaluation value in a certain electric meter is less than the preset value for three consecutive days, the main station will issue a battery replacement notice for this electric meter, and the tester will go to the site to replace the battery of the electric meter.

[0044] The present invention has a simple process and is easy to operate, and can accurately evaluate the remaining power of the battery of the electric meter, thereby achieving more efficient battery resource utilization, reducing the average number of replacement times of the battery of the electric meter, and reducing the operation and maintenance cost of the electric meter throughout the product life cycle.

[0045] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings.

[0046] The present invention provides a method for evaluating the residual current of a lithium-ion battery, which is applied to the operation and maintenance of the clock battery in the smart meter. Wavelet analysis is widely used in the field of signal analysis and processing, and the wavelet framework is an extension of the concept of space basis, which has the characteristics of both wavelet and framework, and overcomes the defect that the traditional orthogonal wavelet basis cannot have compact support, continuity and symmetry at the same time. In addition, while the decomposition and reconstruction formula of the framework can be used, the classic pyramid decomposition and reconstruction algorithm in the wavelet theory can also be used. The wavelet framework is in the discrete signal space. It has the following properties:

[0047] Lemma 1: If the sequence Generated A sum sequence on space The minimum energy wavelet frame associated with it, then for any sequence , its wavelet pyramid decomposition algorithm is as follows:

[0048] ;

[0049] The corresponding wavelet pyramid reconstruction algorithm is as follows:

[0050] ;

[0051] At the same time, the above decomposition process also satisfies the following energy invariance:

[0052] ;

[0053] Since the meter clock battery is a disposable battery, in order to minimize the consumption of battery power by the sampling circuit, the voltage sampling circuit only performs short-term sampling periodically. The typical value of the cycle value is 1 day, and the typical value of the length of each sampling time is 1 minute. In order to reduce the impact of the meter load on the sampling signal, the sampling process is only implemented when the meter enters a low-power operation state.

[0054] For disposable batteries such as lithium-ion batteries, the remaining power is a key indicator for measuring their health status and evaluating their remaining normal working time. In order to extract some information related to the remaining power in the voltage sampling curve, the embodiment of the present invention selects the following wavelet framework composed of three sub-functions for signal analysis and processing:

[0055] .

[0056] Combination Figures 1 to 8 In this embodiment, the residual current evaluation method of the lithium-ion battery comprises the following steps:

[0057] S10, let the signal obtained by the clock battery sampling circuit in a certain sampling process be recorded as , then the data processing module uses the above function value to process the signal based on the above wavelet pyramid decomposition algorithm After decomposition, the three lengths are The sub-signal of

[0058] Specifically:

[0059] .

[0060] In this embodiment, the decomposition process of the wavelet frame can be equivalent to a time domain convolution operation of an orthogonal digital filter bank followed by 2-fold sampling, where , and Represent the unit impulse response coefficients of the low-pass, band-pass, and high-pass filters, respectively, so the sub-signal , and They respectively represent the time domain signals obtained after low-pass, band-pass and high-pass filtering of the voltage sampling data.

[0061] S20, continue to use the wavelet pyramid decomposition algorithm to decompose the low-pass sub-signal , bandpass signal and high pass sub-signal Decompose it and get 3 lengths The sub-signal of

[0062] Specifically:

[0063]

[0064] where cc [n] Low-pass sub-signal sub-signal, cd [n] Bandpass signal The sub-signal, ce [n] High-pass sub-signal sub-signal.

[0065] S30, calculating the average energy of the noise component in the collected signal ;

[0066] Specifically recorded as .

[0067] S40, extracting the noise coefficient of the decomposed sub-signal of each layer through a hard threshold function;

[0068] Specifically: and .

[0069] In this embodiment, is the first layer bandpass sub-signal The noise figure, is the first layer high-pass sub-signal The noise figure, is the second layer bandpass sub-signal cd [n] The noise figure, is the second layer high pass sub-signal ce [n] The noise coefficient of the first layer is , the threshold of the second layer is .

[0070] S50, general signal The values ​​of are all set to zero, and the signal in steps S30 and S40 is reconstructed layer by layer using the wavelet pyramid reconstruction algorithm to obtain the signal ,in It indicates a signal The noise component in .

[0071] S60, set threshold , calculate the signal The absolute value of the number of elements in is greater than the threshold The ratio of large noise signal .

[0072] S70, average energy of noise components and large noise signal ratio Considered as a sampled signal The two key information factors extracted in step S30 and the large noise signal ratio value in step S60 are sent to the main station through the communication module of the electric meter.

[0073] S80, the master station will use the BP neural network algorithm combined with the average energy of the noise component reported by the meter and large noise signal ratio , to evaluate the remaining power of the clock battery;

[0074] Specifically, BP neural network generally adopts a three-layer structure of input layer, hidden layer and output layer, so the factor is used in RP evaluation. and As the input of the model, the remaining power is the output of the model. is the input variable of the model input layer, is the output value of the model output layer, and are the connection weights from the input layer to the hidden layer, and from the hidden layer to the output layer. There is the following corresponding relationship between the unit outputs of each layer:

[0075] and ;

[0076] Among them P i is the input from the input layer to the hidden layer, Q k is the output from the hidden layer to the output layer, is the offset, As the activation function of the hidden layer, the present invention selects the tansig function, that is,

[0077] ;

[0078] As the activation function of the output layer, the present invention selects the linear purelin function, that is,

[0079] ;

[0080] where z is the output of purelin(z).

[0081] In this embodiment, the specific steps of evaluating the remaining power of the lithium-ion battery are as follows:

[0082] a. Extracting data and preprocessing: Although the remaining power of lithium-ion batteries can be measured more accurately using professional equipment in the laboratory, the related process costs are high and will cause significant performance damage to the test battery samples. In order to obtain more sample data at a lower cost, the embodiment of the present invention first contacts the lithium-ion battery manufacturer to obtain the curve between the voltage and discharge time of its product when it adopts the fast discharge mode at room temperature (the battery is discharged to 2.0V@25±2°C at a constant current of 10mA), such as Figure 3 As shown, in the experiment, 100 sample batteries just out of the factory were subjected to rapid discharge under the same conditions, and voltage data were sampled and two factors were extracted every 24 hours to establish a corresponding database. It is assumed that the remaining capacity of the battery will decrease linearly with the discharge time, so the sampling time of the data is used to replace the remaining capacity of the battery, that is, the discharge cut-off time of the battery is set to T, and if the sampling time of the current data is T1, the remaining capacity is 1- T1 / T;

[0083] b. RP evaluation: Using the total obtained in step a Build a training set with data and , and the test set and , where the training set and test set account for 80% and 20% respectively. and Bring it into the BP model for training. When the training is completed, As input for RP evaluation, the obtained evaluation values ​​constitute the evaluation set ;

[0084] c. Performance analysis: The mean absolute error (MAE) is used as the evaluation criterion for BP prediction performance analysis:

[0085] .

[0086] S90. In the master station test, if the remaining power evaluation value of a meter is less than 8% for three consecutive days, the master station will issue a battery replacement notice for this meter. The tester will go to the site to test and replace the battery of the meter and check the operating status of the current clock of the meter. If the status is normal, it is considered that the lithium-ion battery in the meter has successfully extended its working life for a period of time; if there is a problem with the status, it is considered that the lithium-ion battery in the meter has failed to extend its life. After completing the relevant inspections, the extended working time of the battery will be recorded and sent to a professional laboratory for remaining power detection.

[0087] In the embodiment of the present invention, the correlation between the two key information extracted from the sampling signal and the remaining power of the lithium-ion battery is analyzed based on laboratory test data. The lithium-ion battery can fully meet the power supply demand of the electric meter when the remaining power is large. Therefore, the research focus of the embodiment of the present invention is mainly concentrated on some obvious characteristics that appear when the remaining power of the lithium-ion battery is small. The experimenters purchased 10 disposable lithium-ion batteries for smart meters from the same manufacturer and the same batch. In order to reduce the experimental time, these batteries were first discharged at a constant current in the laboratory, and the discharge amount was about 70% of the nominal capacity of the battery.

[0088] After the discharge is completed, these batteries are installed in the circuit simulating the operation of the electric meter to supply power, and then the voltage signals of these batteries in the circuit are collected and stored for 1 minute every day. This test lasted for 1 year in the laboratory, and then the corresponding average energy of the noise component and the proportion value of the large noise signal are calculated for the acquired data using the above-mentioned algorithm of the embodiment of the present invention, which are recorded as:

[0089] and .

[0090] Figure 4 The average energy value of the noise component and the ratio of the large noise signal of one of the experimental batteries at different days are given. Since the experimental battery is a disposable battery, the remaining battery power and time are in a strictly decreasing relationship. Figure 4 It can be seen that as time goes by, although the values ​​show a certain degree of jitter, the average energy of the noise component and the proportion of large noise signals are increasing in the overall trend. Although the data of the other 9 experimental batteries are slightly different, the overall trend is basically the same. Figure 4 The results remain consistent.

[0091] From the perspective of electrochemical characteristics, the lithium-ion battery is discharged once through oxidation of the lithium negative electrode and reduction of the electrolyte SOCl2 to sulfur, sulfur dioxide and chloride ions. Cl- reacts with Li+ and peels off from the lithium negative electrode to form LiCl deposited on the carbon surface until passivation. As the discharge process proceeds for a long time, the remaining power decreases, the active materials inside the battery decrease, the impedance of charge transfer increases, the orderliness of the electrochemical process is gradually weakened, and the randomness is significantly enhanced. Although the output voltage signal is still a relatively stable DC signal, the average energy of the noise component in the signal and the probability of a noise signal point (mutation point) with a large amplitude in the signal will increase. Therefore, it can be seen that the average energy of the noise component proposed in the embodiment of the present invention and large noise signal ratio There is a strong correlation between these two factors and the remaining capacity of the lithium-ion battery.

[0092] A large number of test results show that the ambient temperature will also have a certain impact on the working performance of the lithium-ion battery. Since the experiment time exceeds one year, the ambient temperature cannot be kept relatively stable. Therefore, the ambient temperature corresponding to the samples in the database established by the embodiment of the present invention has certain differences when sampling, and the temperature is mainly concentrated in the range of 10°C to 30°C.

[0093] In the embodiment of the present invention, the average energy of the noise component extracted from the voltage sampling signal and the ratio value of the large noise signal are used as input for model training. The evaluation result of the remaining power of a battery in the test set is as follows: Figure 5 The remaining 19 batteries have a remaining charge capacity. Figure 5 Has a high similarity, from Figure 5 It can be seen from the results that due to the combined influence of factors such as inconsistent ambient temperature and errors in the actual value itself, there are large errors between the evaluation value and the actual value of some data. On the whole, the evaluation results can more accurately represent the remaining power of the lithium-ion battery, which basically meets the performance requirements of the master station for online monitoring of the remaining power of the clock battery in the meter.

[0094] In order to further verify the application effect of the embodiment of the present invention in the actual area, the experimenters obtained a batch of scrapped lithium-ion batteries from electric meters that were installed at the same time and just replaced from the on-site area, and manually selected 325 batteries with good overall condition, and installed them one by one in the electric meters in a certain experimental area in Hainan, where these electric meters were all modified based on the embodiment of the present invention. The test process lasted for 12 months, during which the main station issued a total of 95 electric meters. The clock operation status of these meters was in normal state, and the extended life of these meters is as follows: Figure 6 As shown in the figure, the remaining power of these meters tested in professional laboratories is as follows Figure 7 As shown in the figure, after inspection, the clock status of the remaining 230 meters is also in normal state, and the remaining power of the batteries of these meters is as follows: Figure 8 shown.

[0095] It can be seen from the above test results that the existing collective batch replacement mechanism of electric meter batteries has great shortcomings, and a high proportion of the replaced batteries still have relatively abundant remaining power. The evaluation result of the online monitoring mechanism of the remaining power of the electric meter batteries provided by the embodiment of the present invention is highly accurate. Under the premise of basically not increasing the overall cost of the electric meter and affecting the normal operation of the electric meter, it is possible to fully utilize the power contained in the disposable electric meter batteries and realize on-demand battery replacement, thereby achieving more efficient battery resource utilization, reducing the average number of replacement times of the electric meter batteries, and reducing the operation and maintenance cost of the electric meter throughout the product life cycle.

[0096] The present invention also provides a lithium-ion battery remaining capacity evaluation system, comprising:

[0097] A DC voltage sampling chip is used to collect voltage data of the meter clock battery; a data processing module is used to decompose and process the voltage data of the meter clock battery; a meter communication module is used to report the data in the data processing module to the main station; a load resistor is used to reduce the power loss of the meter clock battery; a digital switch is used to control the opening and closing of the system; the meter communication module, the data processing module, and the DC voltage sampling chip are connected in sequence, the digital switch is connected in series with the load resistor, the DC voltage sampling chip is connected in parallel with the load resistor, one end of the digital switch is connected to the positive pole of the meter clock battery, and the other end is connected to one end of the load resistor, and the other end of the load resistor is connected to the negative pole of the meter clock battery.

[0098] In this embodiment, since the nominal voltage of the clock battery of domestic electric meters is generally 3.6V, in order to reduce the consumption of the clock battery power by the sampling circuit, a load resistor with a larger resistance value is selected to simulate the overall resistance of the electric meter under low power consumption operation state. The typical value is 180 kiloohms. In addition, in order to ensure the convergence of the voltage sampling circuit at different temperatures, the load resistor needs to select a low-temperature drift resistor, that is, a resistor with a relatively strong resistance value stability within the operating temperature range of the electric meter.

[0099] Compared with the prior art, the lithium-ion battery residual current evaluation method and system described in the above embodiment cannot evaluate the remaining power of lithium-ion batteries in the battery replacement strategy of the prior art, but can only replace lithium-ion batteries in a centralized manner, resulting in waste of battery resources and increased operation and maintenance costs. The present invention has a simple process and convenient operation. It uses a wavelet frame function to analyze and process the sampled signal, extracts two health factors that are highly correlated with the remaining power of lithium-ion batteries, namely the average energy of the noise component and the large noise signal ratio value, and reports the data to the main station through the meter communication module. The main station evaluates the remaining power of the lithium-ion battery through the BP neural network. The battery remaining power evaluation method provided by the present invention has high accuracy, which can help managers realize on-demand replacement of meter clock batteries, effectively reducing the average number of clock battery replacements during the life cycle of the meter, achieving more efficient battery resource utilization, and reducing the operation and maintenance costs of the meter throughout the product life cycle.

[0100] Obviously, the embodiments described above are only preferred embodiments of the present invention, rather than all embodiments. The preferred embodiments of the present invention are shown in the accompanying drawings, but they do not limit the patent scope of the present invention. The present invention can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present invention more thorough and comprehensive. Although the present invention has been described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific embodiments, or to perform equivalent replacements for some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of the present invention, directly or indirectly used in other related technical fields, is also within the scope of patent protection of the present invention.

Claims

1. A method for evaluating the remaining capacity of a lithium-ion battery, characterized in that: The following steps are involved: S10. Assume that the signal obtained by the DC voltage sampling chip during a certain sampling process is The data processing module decomposes the signal x based on the wavelet pyramid decomposition algorithm to obtain the low-pass sub-signal , bandpass signal and high pass sub-signal ; S20, use the wavelet pyramid decomposition algorithm to decompose the low-pass sub-signal , bandpass signal and high pass sub-signal Decompose and get sub-signals 、cd [n] ,ce [n] ; S30, calculating the average energy of the noise component in the collected signal; S40, extracting the noise coefficient of the decomposed sub-signal of each layer through a hard threshold function; S50, general signal The value of is set to zero, and the signal in steps S30 and S40 is reconstructed layer by layer using the wavelet pyramid reconstruction algorithm to obtain the signal ,in It indicates a signal The noise component in S60, set threshold, calculate signal The ratio of the absolute value of the number of elements in the image that is greater than the threshold is recorded as the ratio of large noise signals; S70, sending the average energy in step S30 and the large noise signal ratio value in step S60 to the main station through the communication module of the electric meter; S80, the master station will evaluate the remaining power of the clock battery by combining the average energy of the noise component and the ratio value of the large noise signal reported by the electric meter through the BP neural network algorithm; S90. If the remaining power evaluation value in a meter is less than the preset value for three consecutive days, the master station will issue a battery replacement notice for this meter, and the test personnel will go to the site to replace the battery of the meter.

2. A method for evaluating the remaining capacity of a lithium-ion battery according to claim 1, characterized in that: In step S10, the signal x is decomposed to obtain three components with lengths of The sub-signals are: ； in , and Represent the unit impulse response coefficients of the low-pass, band-pass, and high-pass filters, respectively, so the sub-signal , and They respectively represent the time domain signals obtained after low-pass, band-pass and high-pass filtering of the voltage sampling data.

3. A method for evaluating the remaining capacity of a lithium-ion battery according to claim 2, characterized in that: In step S20, the low-pass sub-signal , bandpass signal and high pass sub-signal After decomposition, the three lengths are The sub-signals are: ； where cc [n] Low-pass sub-signal sub-signal, cd [n] Bandpass signal The sub-signal, ce [n] High-pass sub-signal sub-signal.

4. A method for evaluating the remaining capacity of a lithium-ion battery according to claim 3, characterized in that: In step S30, the average energy is recorded as .

5. A method for evaluating the remaining capacity of a lithium-ion battery according to claim 4, characterized in that: The step S40 specifically includes: and ; in is the first layer bandpass sub-signal The noise figure, is the first layer high-pass sub-signal The noise figure, is the second layer bandpass sub-signal cd [n] The noise figure, is the second layer high pass sub-signal ce [n] The noise coefficient of the first layer is , the threshold of the second layer is .

6. A method for evaluating the remaining capacity of a lithium-ion battery according to claim 1, characterized in that: In step S80, the BP neural network structure includes an input layer, a hidden layer and an output layer. The average energy in step S30 and the large noise signal ratio value in step S60 are used as the input of the model, and the remaining power is used as the output of the model. is the input variable of the model input layer, is the output value of the model output layer, and are the connection weights from the input layer to the hidden layer, and from the hidden layer to the output layer. There is the following corresponding relationship between the unit outputs of each layer: and ; Among them P i is the input from the input layer to the hidden layer, Q k is the output from the hidden layer to the output layer, is the offset, is the activation function of the hidden layer, is the activation function of the output layer.

7. A method for evaluating the remaining capacity of a lithium-ion battery according to claim 6, characterized in that: Said Select the tansig function, that is: ； Said Select the linear purelin function, that is: ； where z is the output of purelin(z).

8. A method for evaluating the remaining capacity of a lithium-ion battery according to claim 1, characterized in that: The threshold value set in step S60 is .

9. A method for evaluating the remaining capacity of a lithium-ion battery according to claim 1, characterized in that: In step S90, the preset value is 8%.

10. A system using the method according to any one of claims 1 to 9, characterized in that: include: A DC voltage sampling chip is used to collect voltage data of the meter clock battery; A data processing module, used for decomposing and processing the voltage data of the clock battery of the electric meter; An electric meter communication module, used for reporting the data in the data processing module to a main station; Load resistor, used to reduce the loss of battery power in the meter clock; A digital control switch, used to control the opening and closing of the system; The electric meter communication module, data processing module, and DC voltage sampling chip are connected in sequence for communication, the digital control switch is connected in series with the load resistor, the DC voltage sampling chip is connected in parallel with the load resistor, one end of the digital control switch is connected to the positive pole of the electric meter clock battery, and the other end is connected to one end of the load resistor, and the other end of the load resistor is connected to the negative pole of the electric meter clock battery.

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