A lithium battery health status cloud monitoring method and system

Abstract

The present invention discloses a cloud-based monitoring method and system for the health status of lithium batteries, which relates to the field of lithium batteries and includes: pre-acquiring performance indicators reflecting the health status of lithium batteries, measuring the health status of lithium batteries in an experimental environment, generating input data sets and output data sets, adding a constructed fully connected neural network model for model training, and uploading the data sets to a cloud server. The cloud server can then output the actual health status of the lithium battery based on the actual performance indicators of the lithium battery collected by the user end and return it. Subsequently, the performance indicators of the lithium battery under different external environments will be continuously acquired, the fully connected neural network model will be optimized in real time, and the fully connected neural network model of the cloud server will be dynamically updated. The present invention puts the health status monitoring of lithium batteries on the cloud, solving the problem of inaccurate monitoring due to insufficient local computing power and readable storage space on the user end, and continuously optimizes and dynamically updates the detection model, improving the generalization capability of lithium battery health status monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium battery monitoring, and in particular to a cloud-based monitoring method and system for the health status of lithium batteries. Background Art

[0002] Lithium battery is a rechargeable battery that stores and releases energy based on the migration of lithium ions between positive and negative electrodes. It has the advantages of high energy density, long life and low self-discharge rate. It is widely used in various electronic devices and electric vehicles. The development of lithium batteries has gone through two stages: lithium metal batteries and lithium-ion batteries. The latter has become the mainstream due to its higher safety. In recent years, lithium battery technology has been continuously optimized and its application areas have been continuously expanded.

[0003] Thanks to the widespread application of lithium batteries in electric vehicles, energy storage, consumer electronics and other fields, lithium battery health status monitoring technology is crucial to ensure battery safety and understand battery performance and life. This technology monitors key parameters such as battery voltage, current, temperature, capacity, and uses data collection, transmission, processing and analysis to achieve real-time monitoring and prediction of lithium battery health status to ensure its reliability during use.

[0004] However, on user-end devices such as electric vehicles and electronic products, due to the limitations of computing power and the size of readable storage space, it may not be possible to accurately monitor the health status of lithium batteries. On the other hand, the lithium battery health status detection method based on machine learning models will cause the pre-trained machine learning model to lose universality due to changes in the external environment of the lithium battery, and the data set previously used to train the model will also lose its representativeness to a certain extent. Summary of the Invention

[0005] In response to the above-mentioned technical deficiencies, the purpose of the present invention is to provide a cloud-based monitoring method and system for the health status of lithium batteries, aiming to solve the problem that the computing power and readable storage space on the user-end device are limited, making it impossible to accurately monitor the health status of lithium batteries. At the same time, the lithium battery health status detection method based on the machine learning model will cause the pre-trained machine learning model to lose universality due to changes in the external environment of the lithium battery. In addition, the data set previously used to train the model will also lose its representativeness to a certain extent.

[0006] In view of the above problems, the present application provides a cloud-based monitoring method and system for the health status of lithium batteries.

[0007] The first aspect disclosed in the present application provides a method for cloud-based monitoring of the health status of a lithium battery, the method comprising the following steps:

[0008] Step 1: Pre-obtain a set of performance indicators reflecting the health status of the lithium battery from each charge and discharge cycle throughout the life cycle of the lithium battery, and measure the health status of the lithium battery by AC impedance analysis and chemical analysis during each charge and discharge cycle throughout the life cycle of the lithium battery. The performance indicators include the number of charge and discharge cycles, charge time, discharge time, voltage variance during charge, voltage variance during discharge, current curve during discharge, lithium battery temperature during charge, and lithium battery temperature during discharge. The health status of the lithium battery is expressed as a percentage value.

[0009] Step 2: Construct vector x i =[n i ,t i ,T i ,f i ,F i ,s i ,S i ], where n i is the number of charge and discharge cycles in the i-th group of performance indicators, t i is the charging time in the i-th group of performance indicators, T i is the discharge time in the i-th group of performance indicators, f i is the voltage variance during the charging process in the i-th group of performance indicators, F i is the voltage variance during the discharge process in the i-th group of performance indicators, s i is the lithium battery temperature during charging in the i-th group of performance indicators, S i is the temperature of the lithium battery during the discharge process in the i-th group of performance indicators, i=1,2,3, ...,N, N is the number of charge and discharge cycles in the entire life cycle of the lithium battery, and the matrix [x1 T ,x2 T ,x3 T , ...,x N T ] T As input dataset;

[0010] Step 3: Calculate the discharge capacity of the lithium battery in the i-th charge and discharge cycle by the current curve during the discharge process in the i-th group of performance indicators, and then add the weighted sum of the lithium battery health status measured by AC impedance analysis and chemical analysis in the i-th charge and discharge cycle in the entire life cycle of the lithium battery to obtain the weighted lithium battery health status in the i-th charge and discharge cycle, expressed as g i =0.3j i +0.3h i +0.4r i / R×100%, where g i represents the weighted health status of the lithium battery in the i-th charge and discharge cycle, j i and hi They represent the health status of the lithium battery measured by AC impedance analysis and chemical analysis in the i-th charge and discharge cycle, r i represents the discharge capacity of the lithium battery in the i-th charge and discharge cycle, R represents the rated capacity of the lithium battery, and the vector [g1,g2,g3, ... ,g N ] as the output data set, where i=1,2,3, ... ,N, where N is the number of charge and discharge cycles in the entire life cycle of the lithium battery;

[0011] Step 4: Build a fully connected neural network model, add the input and output datasets to the fully connected neural network model to perform model training, and upload the trained fully connected neural network model to the cloud server;

[0012] Step 5: Upload the real-time performance indicators of the lithium battery collected by the user end to the cloud server, construct the real-time feature vector of the lithium battery e=[n,t,T,f,F,s,S], add the real-time feature vector of the lithium battery to the fully connected neural network model of the cloud server, output the real-time health status of the lithium battery, and return it to the user end, where n is the number of real-time charge and discharge cycles, t is the real-time charge time, T is the real-time discharge time, f is the voltage variance during the real-time charge process, F is the voltage variance during the real-time discharge process, s is the lithium battery temperature during the real-time charge process, and S is the lithium battery temperature during the real-time discharge process;

[0013] Step 6: Continue to obtain performance indicators reflecting the health status of the lithium battery from each charge and discharge cycle throughout the life cycle of the lithium battery under different external environments, use the operation of step 2 to generate an additional input data set, and use AC impedance analysis and chemical analysis to obtain the health status of the lithium battery in each charge and discharge cycle in this scenario, use the same operation in step 3 to calculate the weighted lithium battery health status, generate an additional output data set, perform real-time gradient descent on the fully connected neural network model of the cloud server through the additional input data set and the additional output data set, optimize the fully connected neural network model in real time, and update the fully connected neural network model of the cloud server to the optimized fully connected neural network model.

[0014] Preferably, the method for calculating the discharge capacity of the lithium battery is: by performing definite integral calculation on the current curve during the discharge process within the discharge time, the value of the discharge capacity of the lithium battery can be obtained, which is expressed as , where r represents the discharge capacity of the lithium battery, T represents the discharge time, represents the time variable, Represents the current curve during the discharge process.

[0015] Preferably, the steps of constructing the fully connected neural network model are:

[0016] Create the input layer, the first hidden layer, the second hidden layer, and the output layer in sequence. The input layer has 7 neurons, the first hidden layer has 12 neurons, the second hidden layer has 7 neurons, and the output layer has 1 neuron. The connection between neurons in each layer is fully connected.

[0017] Randomly sample from a Gaussian distribution with a mathematical expectation of 0 and a standard deviation of 1, and assign initial values ​​to the weight parameters of the full connection of each layer;

[0018] Set the loss function to mean square error function and the activation function to sigmoid function.

[0019] Preferably, the input data set needs to be normalized before being added to the fully connected neural network model to perform model training.

[0020] Preferably, the lithium battery actual feature vector needs to be normalized before being added to the fully connected neural network model of the cloud server.

[0021] Preferably, the normalization operation is maximum normalization, specifically expressed as y m * =y m / max 1:M (y), where y m is the mth value to be normalized, max 1:M (y m ) is the maximum value among a column of M values, y m * y m The value after maximum normalization is performed.

[0022] The second aspect disclosed in the present application provides a lithium battery health status cloud monitoring system, which is used in the above-mentioned lithium battery health status cloud monitoring method, and the system includes:

[0023] An acquisition module, the acquisition module is used to pre-acquire a set of performance indicators reflecting the health status of the lithium battery from each charge and discharge cycle in the entire life cycle of the lithium battery, and to determine the health status of the lithium battery by AC impedance analysis and chemical analysis in each charge and discharge cycle in the entire life cycle of the lithium battery, wherein the performance indicators include the number of charge and discharge cycles, charge time, discharge time, voltage variance during charge, voltage variance during discharge, current curve during discharge, lithium battery temperature during charge, and lithium battery temperature during discharge, and the health status of the lithium battery is expressed as a percentage value;

[0024] Build a vector module, the vector building module is used to build a vector x i =[n i ,ti ,T i ,f i ,F i ,s i ,S i ], where n i is the number of charge and discharge cycles in the i-th group of performance indicators, t i is the charging time in the i-th group of performance indicators, T i is the discharge time in the i-th group of performance indicators, f i is the voltage variance during the charging process in the i-th group of performance indicators, F i is the voltage variance during the discharge process in the i-th group of performance indicators, s i is the lithium battery temperature during charging in the i-th group of performance indicators, S i is the temperature of the lithium battery during the discharge process in the i-th group of performance indicators, i=1,2,3, ...,N, N is the number of charge and discharge cycles in the entire life cycle of the lithium battery, and the matrix [x1 T ,x2 T ,x3 T , ... ,x N T ] T As input dataset;

[0025] The determination module is used to calculate the discharge capacity of the lithium battery in the i-th charge and discharge cycle by the current curve during the discharge process in the i-th group of performance indicators, and then perform a weighted summation with the health status of the lithium battery measured by the AC impedance analysis method and the chemical analysis method in the i-th charge and discharge cycle within the life cycle of the lithium battery to obtain the weighted health status of the lithium battery in the i-th charge and discharge cycle, expressed as g i =0.3j i +0.3h i +0.4r i / R×100%, where g i represents the weighted health status of the lithium battery in the i-th charge and discharge cycle, j i and h i They represent the health status of the lithium battery measured by AC impedance analysis and chemical analysis in the i-th charge and discharge cycle, r i represents the discharge capacity of the lithium battery in the i-th charge and discharge cycle, R represents the rated capacity of the lithium battery, and the vector [g1,g2,g3, ... ,g N ] as the output data set, where i=1,2,3, ... ,N, where N is the number of charge and discharge cycles in the entire life cycle of the lithium battery;

[0026] A training module is used to build a fully connected neural network model, add input data sets and output data sets to the fully connected neural network model to perform model training, and upload the fully connected neural network model after model training to a cloud server;

[0027] A live module is used to upload the real-time performance indicators of the lithium battery collected by the user end to the cloud server, construct a real-time feature vector of the lithium battery e=[n,t,T,f,F,s,S], add the real-time feature vector of the lithium battery to the fully connected neural network model of the cloud server, output the real-time health status of the lithium battery, and return it to the user end, where n is the number of real-time charge and discharge cycles, t is the real-time charge time, T is the real-time discharge time, f is the voltage variance during the real-time charge process, F is the voltage variance during the real-time discharge process, s is the lithium battery temperature during the real-time charge process, and S is the lithium battery temperature during the real-time discharge process;

[0028] A real-time optimization module is used to continue to obtain performance indicators reflecting the health status of the lithium battery from each charge and discharge cycle throughout the life cycle of the lithium battery under different external environments, use the operation of step 2 to generate an additional input data set, and obtain the health status of the lithium battery in each charge and discharge cycle in this scenario through AC impedance analysis and chemical analysis, use the same operation in step 3 to calculate the weighted health status of the lithium battery, generate an additional output data set, perform real-time gradient descent on the fully connected neural network model of the cloud server through the additional input data set and the additional output data set, optimize the fully connected neural network model in real time, and update the fully connected neural network model of the cloud server to the optimized fully connected neural network model.

[0029] The third aspect disclosed in the present application provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned cloud-based monitoring method for the health status of a lithium battery when executing the computer program.

[0030] The beneficial effects of the present invention are:

[0031] (1) The core computing part of lithium battery health status monitoring is transferred to the cloud server. The user side only needs to focus on uploading the real-time performance indicators of the lithium battery and receiving the real-time health status of the lithium battery. This solves the problem that the computing power and readable storage space on the user side device are limited and the health status of the lithium battery cannot be accurately monitored.

[0032] (2) Continuously obtain the performance indicators of lithium batteries under different external environments, optimize the fully connected neural network model in real time, and dynamically update the fully connected neural network model of the cloud server, so as to reduce the problem of the pre-trained model losing universality due to changes in the external environment of the lithium battery, improve the generalization ability of subsequent lithium battery health status monitoring, and make the monitoring results more and more accurate.

[0033] (3) Two hidden layers are used in the fully connected neural network model, which improves the model's expressiveness and fitting capabilities, can capture more complex and subtle data feature changes, and make lithium battery health status monitoring more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0035] Figure 1 This is the overall flow chart of the cloud-based monitoring method for lithium battery health status.

[0036] Figure 2 This is the overall structure diagram of the fully connected neural network model.

[0037] Figure 3 This is the overall structure diagram of the lithium battery health status cloud monitoring system.

[0038] Explanation of reference numerals: input layer 10 , first hidden layer 20 , second hidden layer 30 , output layer 40 . DETAILED DESCRIPTION

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0040] like Figure 1 As shown, the embodiment of the present application provides a cloud-based monitoring method for the health status of a lithium battery, the method comprising the following steps:

[0041] Step 1: Pre-obtain a set of performance indicators reflecting the health status of the lithium battery from each charge and discharge cycle throughout the life cycle of the lithium battery, and measure the health status of the lithium battery by AC impedance analysis and chemical analysis during each charge and discharge cycle throughout the life cycle of the lithium battery. The performance indicators include the number of charge and discharge cycles, charge time, discharge time, voltage variance during charge, voltage variance during discharge, current curve during discharge, lithium battery temperature during charge, and lithium battery temperature during discharge. The health status of the lithium battery is expressed as a percentage value.

[0042] Step 2: Construct vector x i =[n i ,t i ,T i ,f i ,F i ,s i ,S i ], where n i is the number of charge and discharge cycles in the i-th group of performance indicators, t i is the charging time in the i-th group of performance indicators, T i is the discharge time in the i-th group of performance indicators, f i is the voltage variance during the charging process in the i-th group of performance indicators, F i is the voltage variance during the discharge process in the i-th group of performance indicators, s i is the lithium battery temperature during charging in the i-th group of performance indicators, S i is the temperature of the lithium battery during the discharge process in the i-th group of performance indicators, i=1,2,3, ...,N, N is the number of charge and discharge cycles in the entire life cycle of the lithium battery, and the matrix [x1 T ,x2 T ,x3 T , ...,x N T ] T As input dataset;

[0043] Step 3: Calculate the discharge capacity of the lithium battery in the i-th charge and discharge cycle by the current curve during the discharge process in the i-th group of performance indicators, and then add the weighted sum of the lithium battery health status measured by AC impedance analysis and chemical analysis in the i-th charge and discharge cycle in the entire life cycle of the lithium battery to obtain the weighted lithium battery health status in the i-th charge and discharge cycle, expressed as g i =0.3j i +0.3h i +0.4r i / R×100%, where g i represents the weighted health status of the lithium battery in the i-th charge and discharge cycle, j i and hi They represent the health status of the lithium battery measured by AC impedance analysis and chemical analysis in the i-th charge and discharge cycle, r i represents the discharge capacity of the lithium battery in the i-th charge and discharge cycle, R represents the rated capacity of the lithium battery, and the vector [g1,g2,g3, ... ,g N ] as the output data set, where i=1,2,3, ... ,N, where N is the number of charge and discharge cycles in the entire life cycle of the lithium battery;

[0044] Step 4: Build a fully connected neural network model. The model structure is as follows Figure 2 As shown, the input data set and the output data set are added to the fully connected neural network model to perform model training, and the fully connected neural network model that has completed model training is uploaded to the cloud server;

[0045] Step 5: Upload the real-time performance indicators of the lithium battery collected by the user end to the cloud server, construct the real-time feature vector of the lithium battery e=[n,t,T,f,F,s,S], add the real-time feature vector of the lithium battery to the fully connected neural network model of the cloud server, output the real-time health status of the lithium battery, and return it to the user end, where n is the number of real-time charge and discharge cycles, t is the real-time charge time, T is the real-time discharge time, f is the voltage variance during the real-time charge process, F is the voltage variance during the real-time discharge process, s is the lithium battery temperature during the real-time charge process, and S is the lithium battery temperature during the real-time discharge process;

[0046] Step 6: Continue to obtain performance indicators reflecting the health status of the lithium battery from each charge and discharge cycle throughout the life cycle of the lithium battery under different external environments, use the operation of step 2 to generate an additional input data set, and use AC impedance analysis and chemical analysis to obtain the health status of the lithium battery in each charge and discharge cycle in this scenario, use the same operation in step 3 to calculate the weighted lithium battery health status, generate an additional output data set, perform real-time gradient descent on the fully connected neural network model of the cloud server through the additional input data set and the additional output data set, optimize the fully connected neural network model in real time, and update the fully connected neural network model of the cloud server to the optimized fully connected neural network model.

[0047] Specifically, continuously obtaining the performance indicators of lithium batteries under different external environments, optimizing the fully connected neural network model in real time, and dynamically updating the fully connected neural network model of the cloud server can reduce the problem of pre-trained models losing universality due to changes in the external environment of lithium batteries, improve the generalization ability of subsequent lithium battery health status monitoring, and make the monitoring results more and more accurate.

[0048] Furthermore, the method for calculating the discharge capacity of the lithium battery is as follows: by performing a definite integral calculation on the current curve during the discharge process within the discharge time, the discharge capacity value of the lithium battery can be obtained, which is expressed as , where r represents the discharge capacity of the lithium battery, T represents the discharge time, represents the time variable, Represents the current curve during the discharge process;

[0049] Specifically, since the current in the discharge process changes with the work power of the lithium battery, it is usually not constant, but an irregular function curve that changes with time. The discharge capacity of the lithium battery is the definite integral of the function curve within the discharge time interval.

[0050] Furthermore, the steps for constructing the fully connected neural network model are:

[0051] like Figure 2 As shown, the input layer 10, the first hidden layer 20, the second hidden layer 30 and the output layer 40 are created in sequence, wherein the input layer 10 includes 7 neurons, the first hidden layer 20 includes 12 neurons, the second hidden layer 30 includes 7 neurons, and the output layer 40 is 1 neuron. The connection mode between the neurons in each layer is fully connected, as shown in FIG. Figure 2 As shown;

[0052] Specifically, the fully connected neural network model uses a four-layer structure. In addition to the input layer 10 and the output layer 40, two hidden layers 20 and 30 are used, which improves the model's expressiveness and fitting capabilities and can capture more complex and subtle changes in data features.

[0053] On the other hand, the number of hidden layer neurons is usually determined using an empirical formula, which can be expressed as: the number of hidden layer neurons < 2 × the number of input layer neurons, so the number of neurons in the first hidden layer 20 and the second hidden layer 30 should both be less than 16.

[0054] Randomly sample from a Gaussian distribution with a mathematical expectation of 0 and a standard deviation of 1, and assign initial values ​​to the weight parameters of the full connection of each layer;

[0055] Specifically, randomly sampling from a Gaussian distribution to initialize the weight parameters can ensure that the weight parameters have small initial values, which helps to stabilize the training of the model.

[0056] Set the loss function to mean square error function and the activation function to sigmoid function;

[0057] Specifically, the mean square error function calculates the square of the difference between the weighted lithium battery health state value and the lithium battery health state value predicted by the model, and performs back propagation and gradient descent by performing partial differentiation calculation on the mean square error function. The sigmoid function is a classic activation function with the form f ( x )=1 / (1+ exp (- x )), its output is between 0 and 1, which allows it to be used in the output layer to directly represent the percentage value of the health status of the lithium battery.

[0058] Furthermore, the input data set needs to be normalized before being added to the fully connected neural network model to perform model training.

[0059] Furthermore, the lithium battery actual feature vector needs to be normalized before being added to the fully connected neural network model of the cloud server.

[0060] Furthermore, the normalization operation is maximum normalization, specifically expressed as y m * =y m / max 1:M (y), where y m is the mth value to be normalized, max 1:M (y m ) is the maximum value among a column of M values, y m * y m The value after performing maximum normalization;

[0061] Specifically, maximum normalization scales all data to a value between -1 and 1. This approach is computationally simple and insensitive to outliers, while also avoiding the vanishing gradient problem associated with the sigmoid activation function. While numerical instability can occur when the maximum value in a dataset varies significantly, the performance indicators of lithium-ion battery health remain stable and relatively stable, avoiding this issue.

[0062] In summary, the cloud-based lithium battery health status monitoring method provided by the embodiments of the present application has the following technical effects:

[0063] (1) The core computing part of lithium battery health status monitoring is transferred to the cloud server. The user side only needs to focus on uploading the real-time performance indicators of the lithium battery and receiving the real-time health status of the lithium battery. This solves the problem that the computing power and readable storage space on the user side device are limited and the health status of the lithium battery cannot be accurately monitored.

[0064] (2) Continuously obtain the performance indicators of lithium batteries under different external environments, optimize the fully connected neural network model in real time, and dynamically update the fully connected neural network model of the cloud server, so as to reduce the problem of the pre-trained model losing universality due to changes in the external environment of the lithium battery, improve the generalization ability of subsequent lithium battery health status monitoring, and make the monitoring results more and more accurate.

[0065] (3) Two hidden layers are used in the fully connected neural network model, which improves the model's expressiveness and fitting capabilities, can capture more complex and subtle data feature changes, and make lithium battery health status monitoring more accurate.

[0066] Based on the same inventive concept as the cloud monitoring method for the health status of a lithium battery in the aforementioned embodiment, Figure 3 As shown, the present application provides a lithium battery health status cloud monitoring system, the system comprising:

[0067] An acquisition module, the acquisition module is used to pre-acquire a set of performance indicators reflecting the health status of the lithium battery from each charge and discharge cycle in the entire life cycle of the lithium battery, and to determine the health status of the lithium battery by AC impedance analysis and chemical analysis in each charge and discharge cycle in the entire life cycle of the lithium battery, wherein the performance indicators include the number of charge and discharge cycles, charge time, discharge time, voltage variance during charge, voltage variance during discharge, current curve during discharge, lithium battery temperature during charge, and lithium battery temperature during discharge, and the health status of the lithium battery is expressed as a percentage value;

[0068] Build a vector module, the vector building module is used to build a vector x i =[n i ,t i ,T i ,f i ,F i ,s i ,S i ], where n i is the number of charge and discharge cycles in the i-th group of performance indicators, t i is the charging time in the i-th group of performance indicators, T i is the discharge time in the i-th group of performance indicators, f i is the voltage variance during the charging process in the i-th group of performance indicators, F i is the voltage variance during the discharge process in the i-th group of performance indicators, s i is the lithium battery temperature during charging in the i-th group of performance indicators, S i is the temperature of the lithium battery during the discharge process in the i-th group of performance indicators, i=1,2,3, ...,N, N is the number of charge and discharge cycles in the entire life cycle of the lithium battery, and the matrix [x1 T ,x2T ,x3 T , ... ,x N T ] T As input dataset;

[0069] The determination module is used to calculate the discharge capacity of the lithium battery in the i-th charge and discharge cycle by the current curve during the discharge process in the i-th group of performance indicators, and then perform a weighted summation with the health status of the lithium battery measured by the AC impedance analysis method and the chemical analysis method in the i-th charge and discharge cycle within the life cycle of the lithium battery to obtain the weighted health status of the lithium battery in the i-th charge and discharge cycle, expressed as g i =0.3j i +0.3h i +0.4r i / R×100%, where g i represents the weighted health status of the lithium battery in the i-th charge and discharge cycle, j i and h i They represent the health status of the lithium battery measured by AC impedance analysis and chemical analysis in the i-th charge and discharge cycle, r i represents the discharge capacity of the lithium battery in the i-th charge and discharge cycle, R represents the rated capacity of the lithium battery, and the vector [g1,g2,g3, ... ,g N ] as the output data set, where i=1,2,3, ... ,N, where N is the number of charge and discharge cycles in the entire life cycle of the lithium battery;

[0070] A training module is used to build a fully connected neural network model, add input data sets and output data sets to the fully connected neural network model to perform model training, and upload the fully connected neural network model after model training to a cloud server;

[0071] A live module is used to upload the real-time performance indicators of the lithium battery collected by the user end to the cloud server, construct a real-time feature vector of the lithium battery e=[n,t,T,f,F,s,S], add the real-time feature vector of the lithium battery to the fully connected neural network model of the cloud server, output the real-time health status of the lithium battery, and return it to the user end, where n is the number of real-time charge and discharge cycles, t is the real-time charge time, T is the real-time discharge time, f is the voltage variance during the real-time charge process, F is the voltage variance during the real-time discharge process, s is the lithium battery temperature during the real-time charge process, and S is the lithium battery temperature during the real-time discharge process;

[0072] A real-time optimization module is used to continue to obtain performance indicators reflecting the health status of the lithium battery from each charge and discharge cycle throughout the life cycle of the lithium battery under different external environments, use the operation of step 2 to generate an additional input data set, and obtain the health status of the lithium battery in each charge and discharge cycle in this scenario through AC impedance analysis and chemical analysis, use the same operation in step 3 to calculate the weighted health status of the lithium battery, generate an additional output data set, perform real-time gradient descent on the fully connected neural network model of the cloud server through the additional input data set and the additional output data set, optimize the fully connected neural network model in real time, and update the fully connected neural network model of the cloud server to the optimized fully connected neural network model.

[0073] Through the detailed description of a cloud-based monitoring method for the health status of a lithium battery in the present specification, those skilled in the art can clearly understand a cloud-based monitoring system for the health status of a lithium battery in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For relevant details, please refer to the method section.

[0074] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps of the above-mentioned cloud-based monitoring method for the health status of a lithium battery when executing the computer program.

[0075] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0076] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A cloud-based monitoring method for lithium battery health status, characterized in that: The following steps are involved: Step 1: Pre-obtain a set of performance indicators reflecting the health status of the lithium battery from each charge and discharge cycle throughout the life cycle of the lithium battery, and measure the health status of the lithium battery by AC impedance analysis and chemical analysis during each charge and discharge cycle throughout the life cycle of the lithium battery. The performance indicators include the number of charge and discharge cycles, charge time, discharge time, voltage variance during charge, voltage variance during discharge, current curve during discharge, lithium battery temperature during charge, and lithium battery temperature during discharge. The health status of the lithium battery is expressed as a percentage value. Step 2: Construct vector x i =[n i ,t i ,T i ,f i ,F i ,s i ,S i ], where n i is the number of charge and discharge cycles in the i-th group of performance indicators, t i is the charging time in the i-th group of performance indicators, T i is the discharge time in the i-th group of performance indicators, f i is the voltage variance during the charging process in the i-th group of performance indicators, F i is the voltage variance during the discharge process in the i-th group of performance indicators, s i is the lithium battery temperature during charging in the i-th group of performance indicators, S i is the temperature of the lithium battery during the discharge process in the i-th group of performance indicators, i=1,2,3, ...,N, N is the number of charge and discharge cycles in the entire life cycle of the lithium battery, and the matrix [x1 T ,x2 T ,x3 T , ... ,x N T ] T As input dataset; Step 3: Calculate the discharge capacity of the lithium battery in the i-th charge and discharge cycle by the current curve during the discharge process in the i-th group of performance indicators, and then add the weighted sum of the lithium battery health status measured by AC impedance analysis and chemical analysis in the i-th charge and discharge cycle in the entire life cycle of the lithium battery to obtain the weighted lithium battery health status in the i-th charge and discharge cycle, expressed as g i =0.3j i +0.3h i +0.4r i / R×100%, where g i represents the weighted health status of the lithium battery in the i-th charge and discharge cycle, j i and h i They represent the health status of the lithium battery measured by AC impedance analysis and chemical analysis in the i-th charge and discharge cycle, r i represents the discharge capacity of the lithium battery in the i-th charge and discharge cycle, R represents the rated capacity of the lithium battery, and the vector [g1,g2,g3,... ,g N ] as the output data set, where i=1,2,3, ... ,N, where N is the number of charge and discharge cycles in the entire life cycle of the lithium battery; Step 4: Build a fully connected neural network model, add the input and output datasets to the fully connected neural network model to perform model training, and upload the trained fully connected neural network model to the cloud server; Step 5: Upload the real-time performance indicators of the lithium battery collected by the user end to the cloud server, construct the real-time feature vector of the lithium battery e=[n,t,T,f,F,s,S], add the real-time feature vector of the lithium battery to the fully connected neural network model of the cloud server, output the real-time health status of the lithium battery, and return it to the user end, where n is the number of real-time charge and discharge cycles, t is the real-time charge time, T is the real-time discharge time, f is the voltage variance during the real-time charge process, F is the voltage variance during the real-time discharge process, s is the lithium battery temperature during the real-time charge process, and S is the lithium battery temperature during the real-time discharge process; Step 6: Continue to obtain performance indicators reflecting the health status of the lithium battery from each charge and discharge cycle throughout the life cycle of the lithium battery under different external environments, use the operation of step 2 to generate an additional input data set, and use AC impedance analysis and chemical analysis to obtain the health status of the lithium battery in each charge and discharge cycle under different external environments, use the same operation in step 3 to calculate the weighted lithium battery health status, generate an additional output data set, perform real-time gradient descent on the fully connected neural network model of the cloud server through the additional input data set and the additional output data set, optimize the fully connected neural network model in real time, and update the fully connected neural network model of the cloud server to the optimized fully connected neural network model.

2. A cloud-based monitoring method for lithium battery health status according to claim 1, characterized in that: The calculation method of the lithium battery discharge capacity is as follows: by performing definite integral calculation on the current curve during the discharge process, the value of the lithium battery discharge capacity can be obtained, which is expressed as , where r represents the discharge capacity of the lithium battery, P represents the discharge time, represents the time variable, Represents the current curve during the discharge process.

3. The cloud-based monitoring method for lithium battery health status according to claim 1, wherein: The steps for constructing the fully connected neural network model are: Create the input layer, the first hidden layer, the second hidden layer, and the output layer in sequence. The input layer has 7 neurons, the first hidden layer has 12 neurons, the second hidden layer has 7 neurons, and the output layer has 1 neuron. The connection between neurons in each layer is fully connected. Randomly sample from a Gaussian distribution with a mathematical expectation of 0 and a standard deviation of 1, and assign initial values ​​to the weight parameters of the full connection of each layer; Set the loss function to mean square error function and the activation function to sigmoid function.

4. The cloud-based monitoring method for lithium battery health status according to claim 1, wherein: The input data set also needs to be normalized before being added to the fully connected neural network model to perform model training.

5. The cloud-based monitoring method for lithium battery health status according to claim 1, wherein: The lithium battery actual feature vector needs to be normalized before being added to the fully connected neural network model of the cloud server.

6. A cloud-based monitoring method for lithium battery health status according to claim 4 or 5, characterized in that: The normalization operation is maximum normalization, specifically expressed as y m * =y m / max 1:M (y), where y m is the mth value to be normalized, max 1:M (y m ) is the maximum value among a column of M values, y m * y m The value after maximum normalization is performed.

7. A lithium battery health status cloud monitoring system, the system comprising: An acquisition module, the acquisition module is used to pre-acquire a set of performance indicators reflecting the health status of the lithium battery from each charge and discharge cycle in the entire life cycle of the lithium battery, and to determine the health status of the lithium battery by AC impedance analysis and chemical analysis in each charge and discharge cycle in the entire life cycle of the lithium battery, wherein the performance indicators include the number of charge and discharge cycles, charge time, discharge time, voltage variance during charge, voltage variance during discharge, current curve during discharge, lithium battery temperature during charge, and lithium battery temperature during discharge, and the health status of the lithium battery is expressed as a percentage value; Build a vector module, the vector building module is used to build a vector x i =[n i ,t i ,T i ,f i ,F i ,s i ,S i ], where n i is the number of charge and discharge cycles in the i-th group of performance indicators, t i is the charging time in the i-th group of performance indicators, T i is the discharge time in the i-th group of performance indicators, f i is the voltage variance during the charging process in the i-th group of performance indicators, F i is the voltage variance during the discharge process in the i-th group of performance indicators, s i is the lithium battery temperature during charging in the i-th group of performance indicators, S i is the temperature of the lithium battery during the discharge process in the i-th group of performance indicators, i=1,2,3, ...,N, N is the number of charge and discharge cycles in the entire life cycle of the lithium battery, and the matrix [x1 T ,x2 T ,x3 T , ... ,x N T ] T As input dataset; The determination module is used to calculate the discharge capacity of the lithium battery in the i-th charge and discharge cycle by the current curve during the discharge process in the i-th group of performance indicators, and then perform a weighted summation with the health status of the lithium battery measured by the AC impedance analysis method and the chemical analysis method in the i-th charge and discharge cycle within the life cycle of the lithium battery to obtain the weighted health status of the lithium battery in the i-th charge and discharge cycle, expressed as g i =0.3j i +0.3h i +0.4r i / R×100%, where g i represents the weighted health status of the lithium battery in the i-th charge and discharge cycle, j i and h i They represent the health status of the lithium battery measured by AC impedance analysis and chemical analysis in the i-th charge and discharge cycle, r i represents the discharge capacity of the lithium battery in the i-th charge and discharge cycle, R represents the rated capacity of the lithium battery, and the vector [g1,g2,g3, ... ,g N ] as the output data set, where i=1,2,3, ... ,N, where N is the number of charge and discharge cycles in the entire life cycle of the lithium battery; A training module is used to build a fully connected neural network model, add input data sets and output data sets to the fully connected neural network model to perform model training, and upload the fully connected neural network model after model training to a cloud server; A live module is used to upload the real-time performance indicators of the lithium battery collected by the user end to the cloud server, construct a real-time feature vector of the lithium battery e=[n,t,T,f,F,s,S], add the real-time feature vector of the lithium battery to the fully connected neural network model of the cloud server, output the real-time health status of the lithium battery, and return it to the user end, where n is the number of real-time charge and discharge cycles, t is the real-time charge time, T is the real-time discharge time, f is the voltage variance during the real-time charge process, F is the voltage variance during the real-time discharge process, s is the lithium battery temperature during the real-time charge process, and S is the lithium battery temperature during the real-time discharge process; A real-time optimization module is used to continue to obtain performance indicators reflecting the health status of the lithium battery from each charge and discharge cycle throughout the life cycle of the lithium battery under different external environments, use the operation of step 2 to generate an additional input data set, and obtain the health status of the lithium battery in each charge and discharge cycle under different external environments through AC impedance analysis and chemical analysis, use the same operation in step 3 to calculate the weighted health status of the lithium battery, generate an additional output data set, perform real-time gradient descent on the fully connected neural network model of the cloud server through the additional input data set and the additional output data set, optimize the fully connected neural network model in real time, and update the fully connected neural network model of the cloud server to the optimized fully connected neural network model.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a cloud-based monitoring method for the health status of a lithium battery according to any one of claims 1 to 6 are implemented.

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