Improved Archimedean algorithm combined with BiGRU for lithium battery SOH estimation and RUL prediction

Abstract

The present invention discloses a method for estimating the state of health (SOH) and predicting the mileage (RUL) of a lithium battery by combining an improved Archimedes algorithm with a BiGRU. The method relates to the fields of new energy vehicles and grid energy storage technology, and includes the following steps: studying the operating principle and aging mechanism of lithium batteries, extracting characteristic parameters containing battery aging status information as input to a prediction model through battery modeling and data-driven methods; performing correlation analysis on the extracted characteristic parameters, retaining highly correlated feature quantities, and performing data preprocessing; inputting the screened feature quantities into a BiGRU model for SOH estimation and RUL prediction; and optimizing the BiGRU model using an improved Archimedes algorithm. The present invention adopts the above-mentioned method for estimating the state of health (SOH) and predicting the mileage (RUL) of a lithium battery by combining an improved Archimedes algorithm with a BiGRU. In terms of model solution, the present invention proposes a lithium battery SOH prediction and RUL estimation model based on an improved Archimedes algorithm to optimize the BiGRU neural network, so that the model can better fit the training data, thereby increasing the model's learning ability and improving prediction accuracy.

Description

Technical Field

[0001] The present invention relates to the field of new energy vehicles and power grid energy storage technology, and in particular to a lithium battery SOH estimation and RUL prediction method combining an improved Archimedean algorithm with a BiGRU. Background Art

[0002] With economic development, global energy consumption continues to rise. However, traditional energy sources such as coal, oil, and natural gas are non-renewable, and their use leads to increasingly severe environmental problems, seriously hindering the sustainable development of human society. Electricity is the representative of new energy, and batteries, as a carrier of electricity, are experiencing technological advancements that can help alleviate energy scarcity and environmental pollution. Therefore, the replacement of traditional fossil fuels with new energy sources has become an inevitable trend in future development. The shift to battery energy storage has also led to a significant increase in the scale of lithium-ion batteries used for energy storage. However, even the best lithium batteries are subject to failure and aging.

[0003] As lithium battery usage increases, concerns are growing about potential safety hazards associated with their use. Battery status monitoring and assessment, which guides battery operation and maintenance, can prevent accidents caused by overcharging and voltage increases, leading to internal short circuits. This is crucial for ensuring the safe, stable, and reliable operation of battery management systems. Summary of the Invention

[0004] The purpose of the present invention is to provide a lithium battery SOH estimation and RUL prediction method combining an improved Archimedes algorithm and BiGRU. Aiming at the problem of incomplete screening of key influencing factors of the health status of lithium batteries, the working principle and aging mechanism of lithium batteries are studied. The SOH and RUL of lithium batteries cannot be directly obtained during actual use. The health status and remaining service life indicators are quantified through battery modeling and data-driven methods. Multiple characteristic parameters such as capacity increment peak, differential voltage inflection point, constant current charging time, etc. containing battery aging status information are extracted as inputs of the prediction model, and an SOH estimation and RUL prediction model based on the improved Archimedes algorithm and BiGRU neural network is established.

[0005] To achieve the above objectives, the present invention provides a lithium battery SOH estimation and RUL prediction method combining an improved Archimedean algorithm with BiGRU, comprising the following steps:

[0006] Step S1: Study the working principle and aging mechanism of lithium batteries, and extract characteristic parameters containing battery aging status information as input of the prediction model through battery modeling and data-driven methods;

[0007] Step S2: perform correlation analysis on the extracted feature parameters, retain the feature quantities with high correlation, and perform data preprocessing to improve the availability of the data;

[0008] Step S3: Input the selected feature quantities into the BiGRU model for SOH estimation and RUL prediction;

[0009] Step S4: Use the improved Archimedean algorithm to optimize the BiGRU model in order to minimize the loss function and improve the accuracy of the prediction results.

[0010] Preferably, the specific steps of step S1 are:

[0011] Step S11: To address the issue of incomplete screening of key factors affecting the health status of lithium batteries, the main factors leading to battery aging and capacity decay mechanisms are summarized: the electrochemical reactions occurring during the operation of lithium batteries include not only redox reactions of lithium ion insertion and deinsertion, but also side reactions. As the number of battery cycles increases, internal aging and capacity decay of the lithium battery are caused;

[0012] Step S12: extracting characteristic parameters for estimating the SOH of the lithium battery from the lithium battery experimental data, with the voltage, current, temperature parameter curves, capacity increment curve, and differential voltage curve as analysis objects;

[0013] Step S13: The capacity increment analysis method studies the IC curve to understand the changes in the internal state of the battery during aging. The IC curve is plotted to convert the slowly changing charging voltage platform into an IC peak.

[0014] Step S14: Using the data of the measured voltage not rising to the charge cut-off voltage, a voltage-capacity curve is drawn, and the battery capacity during charging is obtained by the ampere-hour integration method;

[0015] Step S15: The parameters in the data set are discrete data, and the integral operation in the ampere-hour integration method is converted into a superposition operation;

[0016] Step S16: The IC curve describes the relationship between dQ / dV-V, and the voltage-capacity curve is fitted and derived using the python software functions np.polyfit and np.poly1d.

[0017] Preferably, in step S14, the battery capacity during charging is obtained by the ampere-hour integration method, and the expression is as follows:

[0018]

[0019] Where T represents the time when discharge ends, and i represents the discharge current at time t.

[0020] Preferably, in step S15, the integral operation in the ampere-hour integration method is converted into a superposition operation, and the expression is as follows:

[0021]

[0022] Where I(t) represents the current value at the tth time point, T(t) represents the time at time t, T(t-1) represents the time at time t-1, and I(0) represents the current value at the initial time.

[0023] Preferably, the specific steps of step S2 are as follows:

[0024] Step S21, using the Z-score standardization method to process features;

[0025] Step S22: testing the correlation between continuous variables using the Pearson correlation coefficient, extracting and retaining features with high correlation;

[0026] Step S23: using a box plot method to remove outliers, removing outliers, and treating the removed data locations as missing values;

[0027] Step S24: Use the KNN interpolation algorithm to measure and identify adjacent points, and use adjacent observations to estimate missing values.

[0028] Preferably, in step S21, the Z-score normalization method is used to process the features, and the expression is as follows:

[0029]

[0030] In the formula, z represents the value after Z-score standardization, Represents the original data value to be standardized, μ represents the mean of the data, and σ represents the standard deviation of the data;

[0031] In step S22, the correlation between continuous variables is tested by the Pearson correlation coefficient, which is expressed as follows:

[0032]

[0033] Where, represents the mean of variable x, x i represents the i-th observation in the sample data set x, represents the mean of variable y, y i represents the i-th observation value in the sample data set y, and n represents the total number of samples;

[0034] In step S23, the box plot method is used to eliminate outliers, and the expression is as follows:

[0035] IQR = Q3 - Q1;

[0036] UpLimit=Q3+IQR×1.5;

[0037] LowLimit=Q1-IQR×1.5;

[0038] x>UpLimit&x<LowLimit;

[0039] In the formula, IQR represents, Q3 is the 75% quantile, Q1 is the 25% quantile, x is the data in the data set, UpLimit and LowLimit are the upper and lower limits of the box plot, and data exceeding the upper and lower limits are considered outliers;

[0040] In step S24, adjacent observations are used to estimate missing values, and the expression is as follows:

[0041]

[0042] Where L2(x1,x2) represents the Euclidean distance between vectors x1 and x2; 1n Represents the nth component in vector x1, x 2n Represents the nth component in vector x2, where N represents the number of dimensions of the vector.

[0043] Preferably, the specific steps of step S3 are as follows:

[0044] Step S31, calculating the GRU neural network update gate, reset gate and new candidate state;

[0045] Step S32: BiGRU combines the output results of the input sequences in the two directions to generate the final output result.

[0046] Preferably, the calculation of the GRU unit in step S31 is represented by the following formula:

[0047] Update Gate:

[0048] z t =σ(W Z ·[h t-1 ,x t ]);

[0049] Where σ represents the activation function, W Z represents the weight parameter of the update gate, x t and h t-1 denote the input and hidden states at time step t respectively;

[0050] Reset the gate:

[0051] r t =σ(W r ·[h t-1 ,x t ]);

[0052] Where W r Represents the weight parameter of the reset gate;

[0053] New candidate status:

[0054]

[0055] Where: W h represents the weight parameter of the candidate hidden state, and ⊙ represents the corresponding position multiplication of the matrix;

[0056] Update hidden state:

[0057]

[0058] The final output result in step S32 is expressed as follows:

[0059]

[0060] Where, and are the weights of the forward and backward hidden layers, respectively. Both are constant values ​​and sum to 1.

[0061] Preferably, the specific steps of step S4 include:

[0062] Step S41, population initialization, setting the initial parameters of the optimization algorithm, using the Sin chaos model to generate the initial population, and improving the population traversal ability;

[0063] Step S42: Calculate the transfer operator TF and the density factor d;

[0064] Step S43: If TF≤0.5, it is the initial exploration stage of the population, and the acceleration of the object is obtained;

[0065] Step S44: If TF>0.5, it is the initial development stage of the population, and the acceleration of the object is obtained;

[0066] Step S45: Select the current individual and the global optimal individual for arithmetic crossover to generate a new offspring individual closer to the current optimal solution, thereby accelerating the group to move closer to the global optimal area;

[0067] Step S46: Using the Golden Levy guidance mechanism, when searching within an unknown range, a larger range can be reached, thereby allowing the algorithm to escape from the local optimum;

[0068] Step S47: Introduce the relationship between the sine function and the unit circle so that the population can traverse all points on the sine function;

[0069] Step S48: Add a greedy mechanism after the guiding mechanism to decide whether to update the target position by comparing the individual fitness before and after the individual position update, so as to retain the individuals with better fitness;

[0070] Step S49: Determine whether the maximum number of iterations has been reached or the optimal solution has been found. If the optimal solution has been found, output the decision variable position. If the above conditions are not met, return to step S42 until the optimal solution is found.

[0071] Step S410: Using multiple characteristic parameters including the capacity increment peak, differential voltage inflection point, constant current charging time, etc. containing battery aging status information as inputs of the prediction model, an improved BiGRU-based SOH estimation and RUL prediction model is established.

[0072] Preferably, the Sin chaotic one-dimensional mapping expression in step S41 is as follows:

[0073]

[0074] Where, X n+1 represents the result of the function at time step n+1, X n Represents the value at the current moment n, and N represents the number of iterations;

[0075] In step S42, the expression of the transfer operator TF is as follows:

[0076]

[0077] In the formula, t represents the current time, t max Indicates the maximum value of time;

[0078] The expression of density factor d is as follows:

[0079]

[0080] In step S43, during the initial exploration phase of the population, the acceleration update formula of the object is as follows:

[0081]

[0082] Where, den mr Indicates the density value at the current moment, vol mr Indicates the volume value at the current moment, acc mr Indicates the acceleration value at the current moment, represents the density value at time t+1;

[0083] In step S44, during the initial population development phase, the acceleration update formula of the object is as follows:

[0084]

[0085] Where, den best represents the optimal density, vol best represents the optimal volume, acc bestrepresents the optimal acceleration;

[0086] In step S45, the arithmetic crossover operator expression is as follows:

[0087]

[0088] In the formula, λ represents the weight parameter, represents the position of object i at time t+1, X best Indicates the best position;

[0089] In step S46, the calculation formula of step length s is:

[0090] s=μ / |ν| 1 / β ;

[0091] Where μ is a constant, v represents velocity, and β is an exponential constant;

[0092] In step S47, the individual position update formula is:

[0093]

[0094] Where, X t represents the position of the vector, represents the optimal position vector, represents the position of the current vector, θ1 and θ2 represent the golden section coefficient τ, dis represents the distance between the individual and the optimal position, R1 and R2 represent random values, and s represents the step size;

[0095] In step S48, the greedy mechanism expression is as follows:

[0096]

[0097] Where fit(.) represents the fitness function.

[0098] Therefore, the present invention adopts the lithium battery SOH estimation and RUL prediction method combining the above-mentioned improved Archimedean algorithm with BiGRU, which has the following beneficial effects:

[0099] (1) This method quantifies the health status and remaining service life indicators through battery modeling and data-driven methods, extracts multiple characteristic parameters such as capacity increment peak, differential voltage inflection point, constant current charging time, etc. that contain battery aging status information as input to the prediction model, and uses Z-score, box plot and KNN interpolation algorithm to process characteristic data to improve data availability.

[0100] (2) In terms of model solution, this method proposes a lithium battery SOH prediction and RUL estimation model based on the improved Archimedean algorithm to optimize the BiGRU neural network, so that the model can better fit the training data, increase the learning ability of the model, and improve the prediction accuracy.

[0101] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0102] Figure 1 This is a flowchart of an embodiment of a lithium battery SOH estimation and RUL prediction method combining an improved Archimedean algorithm and BiGRU according to the present invention;

[0103] Figure 2 This is a flowchart of an improved Archimedes algorithm for an embodiment of a lithium battery SOH estimation and RUL prediction method combining an improved Archimedes algorithm with BiGRU according to the present invention. DETAILED DESCRIPTION

[0104] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0105] Unless otherwise defined, the technical or scientific terms used in the present invention shall have the usual meanings understood by persons of ordinary skill in the field to which the present invention belongs. The words "first", "second" and similar terms used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. Words such as "include" or "comprise" mean that the elements or objects preceding the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Words such as "connect" or "connected" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0106] Example

[0107] See also Figure 1-2 , a lithium battery SOH estimation and RUL prediction method combining an improved Archimedes algorithm and BiGRU is proposed, which is characterized by comprising the following steps:

[0108] Step S1: Study the working principle and aging mechanism of lithium batteries, and extract characteristic parameters containing battery aging status information as input of the prediction model through battery modeling and data-driven methods. The specific steps are as follows:

[0109] Step S11: To address the issue of incomplete screening of key factors affecting the health status of lithium batteries, the main factors leading to battery aging and capacity decay mechanisms are summarized: the electrochemical reactions occurring during the operation of lithium batteries include not only redox reactions of lithium ion insertion and deinsertion, but also side reactions. As the number of battery cycles increases, irreversible aging and capacity decay occur within the lithium battery.

[0110] Step S12: extracting characteristic parameters for estimating the SOH of the lithium battery from the lithium battery experimental data, with the voltage, current, temperature parameter curves, capacity increment curve, and differential voltage curve as analysis objects;

[0111] Step S13: The capacity increment analysis method studies the IC curve to understand the changes in the internal state of the battery during aging. The IC curve is plotted to convert the slowly changing charging voltage platform into an IC peak.

[0112] Step S14: When the battery is charged for a period of time and the rising rate of the charge measurement voltage decreases, a voltage-capacity curve is drawn using the data that the measured voltage does not rise to the charge cut-off voltage, and the battery capacity during charging is obtained by the ampere-hour integration method, which is expressed as follows:

[0113]

[0114] Where T represents the time when discharge ends, and i represents the discharge current at time t.

[0115] Step S15: The parameters in the data set are discrete data, and the integral operation in the ampere-hour integration method is converted into a superposition operation; the expression is as follows:

[0116]

[0117] Where I(t) represents the current value at the tth time point, T(t) represents the time at time t, T(t-1) represents the time at time t-1, and I(0) represents the current value at the initial time.

[0118] Step S16: The IC curve describes the relationship between dQ / dV-V, and the voltage-capacity curve is fitted and derived using the python software functions np.polyfit and np.poly1d.

[0119] Step S2: Perform correlation analysis on the extracted feature parameters, retain the features with high correlation, and perform data preprocessing to improve the availability of the data. The specific steps are as follows:

[0120] Step S21: Process the features using the Z-score standardization method. In step S21, the features are processed using the Z-score standardization method, and the expression is as follows:

[0121]

[0122] In the formula, z represents the value after Z-score standardization, Represents the original data value to be standardized, μ represents the mean of the data, and σ represents the standard deviation of the data;

[0123] Step S22: The correlation between continuous variables is tested by the Pearson correlation coefficient, with a value range of [-1, 1]. The features with high correlation are extracted and retained. The Pearson correlation coefficient between samples is obtained by estimating the covariance and standard deviation of the samples. The expression is as follows:

[0124]

[0125] Where, represents the mean of variable x, x i represents the i-th observation in the sample data set x, represents the mean of variable y, y i represents the i-th observation value in the sample data set y, and n represents the total number of samples;

[0126] Step S23: Use the box plot method to eliminate outliers. The outliers are eliminated and the eliminated data positions are regarded as missing values. The upper and lower limits of the box plot are calculated as follows:

[0127] IQR = Q3 - Q1;

[0128] UpLimit=Q3+IQR×1.5;

[0129] LowLimit=Q1-IQR×1.5;

[0130] x>UpLimit&x<LowLimit;

[0131] In the formula, IQR represents, Q3 is the 75% quantile, Q1 is the 25% quantile, x is the data in the data set, UpLimit and LowLimit are the upper and lower limits of the box plot, and data exceeding the upper and lower limits are considered outliers;

[0132] Step S24: Use the KNN interpolation algorithm to measure and identify adjacent points, and use adjacent observations to estimate missing values. In the previous feature analysis section, the feature dataset has been Z-score standardized, so the KNN interpolation algorithm using Euclidean distance as the metric can be directly used. Assuming the multidimensional space dimension is N, and x1 and x2 are two sample points, then the Euclidean distance L2 between x1 and x2 is, and the expression of the KNN interpolation algorithm is as follows:

[0133]

[0134] Where L2(x1,x2) represents the Euclidean distance between vectors x1 and x2; 1n Represents the nth component in vector x1, x 2n Represents the nth component in vector x2, where N represents the number of dimensions of the vector.

[0135] Step S3: Input the filtered feature quantity into the BiGRU model for SOH estimation and RUL prediction. The GRU neural network memorizes valuable information based on the output state of the previous node to learn the time dependency in the sequence. It is a recurrent neural network with state memory and multi-layer chain structure. The GRU neural network only contains two gate control structures: update gate z t and reset gate r t , these gates control the flow of information within the GRU and help capture relevant temporal patterns. The specific steps are as follows:

[0136] Step S31: Calculate the GRU neural network update gate, reset gate, and new candidate state. The calculation of the GRU unit is expressed by the following formula:

[0137] Update Gate:

[0138] z t =σ(W Z ·[h t-1 ,x t ]);

[0139] Where σ represents the activation function, W Z represents the weight parameter of the update gate, x t and h t-1 denote the input and hidden states at time step t respectively;

[0140] Reset the gate:

[0141] r t =σ(W r ·[h t-1 ,x t ]);

[0142] Where W r Represents the weight parameter of the reset gate;

[0143] New candidate status:

[0144]

[0145] Where: W h represents the weight parameter of the candidate hidden state, and ⊙ represents the corresponding position multiplication of the matrix;

[0146] Update hidden state:

[0147]

[0148] The final output result in step S32

[0149] Step S32, BiGRU combines the forward and backward information of the gated recurrent unit (GRU) neural network and connects the corresponding states together to obtain the final output, which increases the learning ability of the model and improves the prediction accuracy. BiGRU uses two GRUs to calculate the hidden vectors in the GRU units of forward propagation and backward propagation. and BiGRU combines the output results of the input sequences in two directions to produce the final output result y t , the expression is as follows:

[0150]

[0151] Where, and are the weights of the forward and backward hidden layers, respectively. Both are constant values ​​and sum to 1.

[0152] Step S4: Use the improved Archimedean algorithm to optimize the BiGRU model to minimize the loss function and improve the accuracy of the prediction results. The specific steps are as follows:

[0153] BiGRU requires an optimization algorithm to adjust its parameters to minimize the loss function and improve prediction accuracy. The BiGRU model contains a large number of learnable parameters, such as the weight matrix, bias terms, and parameters of the gating mechanism, which determine how the model processes inputs and generates outputs. The optimization algorithm calculates the gradient of the loss function with respect to each parameter through backpropagation and uses optimization methods such as gradient descent to adjust the parameters so that the model better fits the training data.

[0154] The basic principle of the Archimedean algorithm is to search for the optimal solution by simulating a spiral. The basic steps are as follows:

[0155] Population initialization:

[0156] O i =lb i +rand*(ub i -lb i ),i=1,2,...,N;

[0157] Where, O i represents the initial position of the i-th particle or solution, lb i The lower bound of the i-th parameter, ub i represents the upper bound of the i-th parameter, and rand represents a random number between 0 and 1, which is used to randomly initialize the solution within the specified upper and lower bounds;

[0158] Initialize the volume (vol) and density (den) of each i-th object:

[0159]

[0160] Initialize the acceleration of the i-th object:

[0161] acc i =lb i +rand*(ub i -lb i );

[0162] Where ub and lb are the upper and lower boundaries of the variable.

[0163] Update volume and density:

[0164]

[0165] In the formula, rand represents a random number between 0 and 1;

[0166] Step S42: Calculate the transfer operator TF and the density factor d. In step S42, the expression of the transfer operator TF is as follows:

[0167]

[0168] In the formula, t represents the current time, t max Indicates the maximum value of time;

[0169] The expression of density factor d is as follows:

[0170]

[0171] Step S43: If TF≤0.5, it is the initial exploration stage of the population. Collisions occur between objects. A random material is selected and the acceleration of the object at iteration t+1 is updated:

[0172]

[0173] Where, den mr Indicates the density at the current moment, vol mr Indicates the volume at the current moment, acc mr Indicates the acceleration parameter at the current moment, represents the density at time t+1;

[0174] Step S44: If TF>0.5, it is the initial development stage of the population, and the acceleration of the object at iteration t+1 is updated:

[0175]

[0176] Where, den best represents the optimal density, vol best represents the optimal volume, acc best represents the optimal acceleration;

[0177] Normalization processing:

[0178]

[0179] Where, It represents the normalized acceleration of the i-th object at time t+1, u represents the upper bound of the normalization, which is usually 1, and l represents the lower bound of the normalization, which is usually 0.

[0180] Object position update:

[0181] If TF≤0.5, the object position is updated at the i-th t+1 iteration using:

[0182]

[0183] Where, represents the position vector of the i-th individual at time t, C1 is the control coefficient, rand represents a random number between 0 and 1, acc is the acceleration factor, and d is the distance metric;

[0184] If TF>0.5, the object position is updated at the i-th t+1 iteration using:

[0185]

[0186] Where F is the control factor, C2 is the control coefficient, d is the distance metric, and T is the correction factor;

[0187] in,

[0188]

[0189] In the formula, P represents the probability factor, which determines the value of F.

[0190] In the original Archimedean algorithm, the population is initialized using a random distribution, resulting in a certain degree of blindness in the initial individual search, which slows the algorithm's convergence. During the global development phase, the Archimedean algorithm relies solely on random individuals to guide the population toward the optimal region in search of the optimal solution, leading to low accuracy. During the local development phase, when the optimal individual falls into a local extremum, the population also falls into a local optimum, causing the algorithm to stagnate. Therefore, this algorithm is improved by employing the Sin chaos model, arithmetic crossover operator, and the Golden Levy guidance mechanism.

[0191] Step S41, population initialization, setting the initial parameters of the optimization algorithm, using the Sin chaos model to generate the initial population, and improving the population traversal capability.

[0192] The Sin chaos model is a chaotic model with good ergodicity and randomness and an infinite number of mapping folds. Its basic principle is to generate a chaotic sequence between the chaotic variable space [0,1] through a mapping relationship and then transform it into the individual optimization variable space. The one-dimensional Sin chaos mapping expression is as follows:

[0193] The expression of Sin chaotic one-dimensional mapping is as follows:

[0194]

[0195] Where, X n+1 Indicates the value of step n+1, X n Represents the value of the nth step, and N represents the total number of steps in the sequence;

[0196] Arithmetic crossover operator: Select the current individual and the global optimal individual for arithmetic crossover, generating new offspring individuals closer to the current optimal solution, thereby accelerating the group's approach to the global optimal area. At the same time, the arithmetic crossover operator gives the current individual the ability to learn from excellent individuals, enhances the population's information sharing ability, and thus increases population diversity. The expression of the arithmetic crossover operator is as follows:

[0197]

[0198] In the formula, λ represents the weight factor, represents the solution of the i-th individual at step t+1, X best represents the global optimal solution among all current individuals;

[0199] Golden Levy Guidance Mechanism: Levy step is a random walk that follows the Levy distribution. Its alternating characteristics of short and long step lengths enable it to reach a wider range when searching within an unknown range, thus helping the algorithm escape from local optimality. The calculation formula for step length s is:

[0200] s=μ / |ν| 1 / β ;

[0201] Where μ is a constant, v represents velocity, and β is an exponential constant;

[0202] Where μ and ν follow a normal distribution:

[0203]

[0204] Where, σ μ It is usually taken as 1, and Γ(β) is the Gamma function.

[0205] Step S47: Introduce the relationship between the sine function and the unit circle so that the population can traverse all points on the sine function. The individual position update formula is:

[0206]

[0207] Where, X t represents the position of the vector, represents the optimal position vector, Represents the position of the current vector, dis represents the distance between the individual and the optimal position, θ1 and θ2 represent the golden section coefficients τ, the purpose of which is to narrow the search space so that the algorithm can fully search the area that can produce excellent solutions in each iteration, thereby accelerating the convergence of the algorithm; R1 represents a random number, and R1∈[0,2π], R1 and Levy step size s jointly determine the search radius; R2 represents a random number, and R2∈[0,π], R2 determines the direction of individual position update.

[0208] The specific parameter expressions in the formula are as follows:

[0209]

[0210] Step S48: Add a greedy mechanism after the guidance mechanism to decide whether to update the target position by comparing the individual fitness before and after the individual position update, so as to retain the individual with better fitness. In step S48, the greedy mechanism is expressed as follows:

[0211]

[0212] Where fit(.) represents the fitness function.

[0213] Step S49: Determine whether the maximum number of iterations has been reached or the optimal solution has been found. If the optimal solution has been found, output the decision variable position. If the above conditions are not met, return to step S42 until the optimal solution is found.

[0214] Step S410: Using multiple characteristic parameters including the capacity increment peak, differential voltage inflection point, constant current charging time, etc. containing battery aging status information as inputs of the prediction model, an improved BiGRU-based SOH estimation and RUL prediction model is established.

[0215] An SOH prediction model based on the improved BiGRU is established. In order to ensure the effectiveness and accuracy of SOH prediction, the first 35%-40% of the data are selected as training data to ensure that the model can learn the intrinsic relationship between degradation characteristics.

[0216] NASA's lithium battery aging test stipulates that a battery's lifespan ends when its capacity reaches 70% of its rated capacity, or when its SOH reaches 0.7. Based on the mapping relationship between battery SOH and RUL, this method uses battery SOH data to predict RUL and establishes a RUL prediction method based on an improved Archimedean algorithm and optimized GRU fusion model.

[0217] Therefore, the present invention adopts the lithium battery SOH estimation and RUL prediction method combining the above-mentioned improved Archimedes algorithm and BiGRU, and proposes a lithium battery SOH prediction and RUL estimation model based on the improved Archimedes algorithm to optimize the BiGRU neural network in terms of model solving, so that the model can better fit the training data, increase the learning ability of the model, and improve the prediction accuracy.

[0218] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. Improved Archimedes algorithm combined with BiGRU lithium battery SOH estimation and RUL prediction method, characterized by: The following steps are involved: Step S1: Study the working principle and aging mechanism of lithium batteries, and extract characteristic parameters containing battery aging status information as input of the prediction model through battery modeling and data-driven methods; Step S2: perform correlation analysis on the extracted feature parameters, retain the feature quantities with high correlation, and perform data preprocessing to improve the availability of the data; Step S3: Input the selected feature quantities into the BiGRU model for SOH estimation and RUL prediction; Step S4: Use the improved Archimedean algorithm to optimize the BiGRU model in order to minimize the loss function and improve the accuracy of the prediction results.

2. The lithium battery SOH estimation and RUL prediction method combining the improved Archimedean algorithm and BiGRU according to claim 1 is characterized in that: The specific steps of step S1 are: Step S11: To address the issue of incomplete screening of key factors affecting the health status of lithium batteries, the main factors leading to battery aging and capacity decay mechanisms are summarized: the electrochemical reactions occurring during the operation of lithium batteries include not only redox reactions of lithium ion insertion and deinsertion, but also side reactions. As the number of battery cycles increases, internal aging and capacity decay of the lithium battery are caused; Step S12: extracting characteristic parameters for estimating the SOH of the lithium battery from the lithium battery experimental data, with the voltage, current, temperature parameter curves, capacity increment curve, and differential voltage curve as analysis objects; Step S13: The capacity increment analysis method studies the IC curve to understand the changes in the internal state of the battery during aging. The IC curve is plotted to convert the slowly changing charging voltage platform into an IC peak. Step S14: Using the data of the measured voltage not rising to the charge cut-off voltage, a voltage-capacity curve is drawn, and the battery capacity during charging is obtained by the ampere-hour integration method; Step S15: The parameters in the data set are discrete data, and the integral operation in the ampere-hour integration method is converted into a superposition operation; Step S16: The IC curve describes the relationship between dQ / dV-V, and the voltage-capacity curve is fitted and derived using the python software functions np.polyfit and np.poly1d.

3. The lithium battery SOH estimation and RUL prediction method combining the improved Archimedean algorithm and BiGRU according to claim 2 is characterized in that: In step S14, the battery capacity during charging is obtained by the ampere-hour integration method, and the expression is as follows: ； Where, Indicates the time to end discharge, Indicates time The discharge current at that moment.

4. The lithium battery SOH estimation and RUL prediction method combining the improved Archimedean algorithm and BiGRU according to claim 3 is characterized in that: In step S15, the integral operation in the ampere-hour integration method is converted into a superposition operation, and the expression is as follows: ； Where, Indicates the The current value at a time point, Indicates time time, Indicates time -1 time, Indicates the current value at the initial moment.

5. The lithium battery SOH estimation and RUL prediction method combining the improved Archimedean algorithm and BiGRU according to claim 4 is characterized in that: The specific steps of step S2 are as follows: Step S21, using the Z-score standardization method to process features; Step S22: testing the correlation between continuous variables using the Pearson correlation coefficient, extracting and retaining features with high correlation; Step S23: using a box plot method to remove outliers, removing outliers, and treating the removed data locations as missing values; Step S24: Use the KNN interpolation algorithm to measure and identify adjacent points, and use adjacent observations to estimate missing values.

6. The lithium battery SOH estimation and RUL prediction method combining the improved Archimedean algorithm and BiGRU according to claim 5 is characterized in that: In step S21, the Z-score normalization method is used to process the features, and the expression is as follows: ； Where, Indicates the value after Z-score standardization, represents the original data value to be standardized, represents the mean of the data, represents the standard deviation of the data; In step S22, the correlation between continuous variables is tested by the Pearson correlation coefficient, which is expressed as follows: ； Where, Representing variables x The mean of Represents a sample dataset The observations, Representing variables y The mean of Represents a sample dataset The observations, Indicates the total number of samples; In step S23, the box plot method is used to eliminate outliers, and the expression is as follows: ； ； ； ； Where, Represents the interquartile range, which is the center of the data set. and The difference between is the 75% quantile, is the 25% quantile, For the data in the dataset, and The data are the upper and lower limits of the box plot. When the upper and lower limits are exceeded, they are considered as outlier data. In step S24, adjacent observations are used to estimate missing values, and the expression is as follows: ； Where, Represents a vector and The Euclidean distance between Represents a vector The A quantity, Represents a vector The A quantity, Indicates the number of dimensions of the vector.

7. The lithium battery SOH estimation and RUL prediction method combining the improved Archimedean algorithm and BiGRU according to claim 6 is characterized in that: The specific steps of step S3 are as follows: Step S31, calculating the GRU neural network update gate, reset gate and new candidate state; Step S32: BiGRU combines the output results of the input sequences in the two directions to generate the final output result.

8. The lithium battery SOH estimation and RUL prediction method combining the improved Archimedean algorithm and BiGRU according to claim 7 is characterized in that: In step S31, the calculation of the GRU unit is represented by the following formula: Update Gate: ； Where, represents the activation function, represents the weight parameter of the update gate, and Represents the time step The input and hidden states at ; Reset the gate: ； Where, Represents the weight parameter of the reset gate; New candidate status: ； Where: represents the weight parameter of the candidate hidden state, Represents the corresponding positional multiplication of matrices; Update hidden state: ； The final output result in step S32 is expressed as follows: ； Where, and are the weights of the forward and backward hidden layers, respectively. Both are constant values ​​and sum to 1.

9. The lithium battery SOH estimation and RUL prediction method combining the improved Archimedean algorithm and BiGRU according to claim 8 is characterized in that: The specific steps of step S4 include: Step S41, population initialization, setting the initial parameters of the optimization algorithm, using the Sin chaos model to generate the initial population, and improving the population traversal ability; Step S42: Calculate the transfer operator TF and the density factor d; Step S43: If TF≤0.5, it is the initial exploration stage of the population, and the acceleration of the object is obtained; Step S44: If TF>0.5, it is the initial development stage of the population, and the acceleration of the object is obtained; Step S45: Select the current individual and the global optimal individual for arithmetic crossover to generate a new offspring individual closer to the current optimal solution, thereby accelerating the group to move closer to the global optimal area; Step S46: Using the Golden Levy guidance mechanism, when searching within an unknown range, a larger range can be reached, thereby allowing the algorithm to escape from the local optimum; Step S47: Introduce the relationship between the sine function and the unit circle so that the population can traverse all points on the sine function; Step S48: Add a greedy mechanism after the guiding mechanism to decide whether to update the target position by comparing the individual fitness before and after the individual position update, so as to retain the individuals with better fitness; Step S49: Determine whether the maximum number of iterations has been reached or the optimal solution has been found. If the optimal solution has been found, output the decision variable position. If the above conditions are not met, return to step S42 until the optimal solution is found. Step S410: Using the capacity increment peak, differential voltage inflection point, and constant current charging time, which contain battery aging status information, as inputs to the prediction model, a SOH estimation and RUL prediction model based on the improved BiGRU is established.

10. The lithium battery SOH estimation and RUL prediction method combining the improved Archimedean algorithm and BiGRU according to claim 9 is characterized in that: The Sin chaotic one-dimensional mapping expression in step S41 is as follows: ； Where, Represents the function at time step The result of Indicates the current time The value of Indicates the number of iterations; In step S42, the transfer operator The expression is as follows: ； Where, Indicates the current moment, Indicates the maximum value of time; Density Factor The expression is as follows: ； In step S43, during the initial exploration phase of the population, the acceleration update formula of the object is as follows: ； Where, Indicates the density value at the current moment, Indicates the volume value at the current moment, Indicates the acceleration value at the current moment, Indicates at time +1 density value; In step S44, during the initial population development phase, the acceleration update formula of the object is as follows: ； Where, represents the optimal density, represents the optimal volume, represents the optimal acceleration; In step S45, the arithmetic crossover operator expression is as follows: ； Where, represents the weight parameter, Indicates time +1 down object location, Indicates the best position; In step S46, the calculation formula of step length s is: ； Where, is a constant, Indicates speed, is the exponential constant; In step S47, the individual position update formula is: ； ； Where, represents the position of the vector, represents the optimal position vector, Represents the current vector position, and represents the angle coefficient, represents the distance between the individual and the optimal position, and represents a random value, Indicates the step length; In step S48, the greedy mechanism expression is as follows: ； Where, represents the fitness function.

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