Lithium ion battery variable working condition SOH evaluation method based on model-data hybrid driving

Abstract

The invention discloses a model-data hybrid driving-based lithium ion battery variable working condition SOH (state of health) evaluation method, relates to a lithium ion battery state of health evaluation method, and aims to solve the problems of poor applicability and low precision of an existing battery fixed working condition SOH evaluation method. The method comprises the following steps: 1, accelerating aging of a battery under working conditions I and II and acquiring information; 2, aiming at the constant-current discharge curve of the battery under the working condition, performing SP + model parameter identification by using a particle swarm optimization algorithm to obtain a degradation track of the model parameter within the battery health degree of 100%-80%; 3, selecting model parameters with high relevancy with the SOH of the battery as health features, and inputting the health features into a BP neural network algorithm to estimate the SOH of the battery, wherein the first working condition data and the second working condition data are respectively a training set and a test set; 4, according to the BPNN model obtained through data training of the first working condition, after SP + model parameters of the second working condition obtained through simulation are input, the SOH of the battery under the second working condition is output, and evaluation is completed. The invention can be used in battery field.

Description

Technical Field

[0001] The invention relates to a lithium ion battery health status assessment method, and belongs to the technical field of power battery systems. Background Art

[0002] As an important component of electric vehicles, the state of health (SOH) of lithium-ion batteries is closely related to the safety of electric vehicles and their mileage. It is an important indicator to measure the degree of battery aging and failure and to determine whether the battery has reached the end of its life.

[0003] At the same time, the prosperous development of the new energy vehicle industry will also bring a large number of retired batteries. Strengthening the cascade utilization of power batteries is one of the current key tasks. However, the degradation law of retired batteries in cascade utilization is difficult to predict. For retired lithium-ion batteries with available historical data, how to extract health features from massive data and conduct SOH prediction for cascade utilization batteries is an important problem. Existing battery SOH estimation methods are often based on fixed operating conditions. In practical applications, SOH evaluation for cascade utilization of retired batteries has poor applicability and low accuracy. Summary of the invention

[0004] The present invention aims to solve the technical problems that the existing SOH estimation methods for lithium-ion batteries under fixed operating conditions have poor applicability and low accuracy, and cannot effectively carry out SOH evaluation for the cascade utilization of retired batteries, and provide a lithium-ion battery variable operating condition SOH evaluation method based on a model-data hybrid drive.

[0005] The present invention provides a lithium-ion battery variable operating condition SOH evaluation method based on model-data hybrid drive, comprising the following steps:

[0006] Step 1, carry out a lithium-ion battery variable working condition accelerated aging test at 55-65°C, the charge and discharge rate of working condition one is twice that of working condition two, after the battery is aged to SOH of 80% under working condition one, the battery is further aged to SOH of 70% under working condition two, and physical information is collected during the accelerated aging test, wherein the physical information is battery voltage, current and capacity;

[0007] Step 2: For the constant current discharge curve of the battery under working condition 1, the particle swarm optimization algorithm is used to carry out simplified electrochemical (SP+) model parameter identification, and the degradation trajectory of the SP+ model parameters in the battery health range of 100% to 80% is obtained;

[0008] Step 3: Use the Pearson correlation analysis method to select the SP+ model parameters with high correlation with the battery SOH, and input these model parameters as health features into the BP neural network (BPNN) algorithm to estimate the battery SOH. The data of working condition 1 is the training set, and the data of working condition 2 is the test set.

[0009] Step 4: Based on the BPNN model trained with the data from working condition 1, after the input is changed to the SP+model parameters of working condition 2 obtained by simulation, the battery SOH under working condition 2 is output, which is the SOH of the lithium-ion battery under the variable working condition, thus completing the evaluation of the health status of the battery under the variable working condition.

[0010] Furthermore, the parameters in the simplified electrochemical (SP+) model of the lithium-ion battery described in step 2 are c0, P act_n , P act_p , Q n , Q p , x0, y0, D x , D y , τ e , τ n , R ohm , τ p , P con ;

[0011] Where c0 is the initial lithium ion concentration in the electrolyte, in mol·m -3 ;

[0012] P act_n is the negative electrode reaction polarization coefficient, in m -1.5 ·mol 0.5 ·s;

[0013] P act_p is the positive electrode reaction polarization coefficient, in m -1.5 ·mol 0.5 ·s;

[0014] Q n is the capacity of the effective active material of the negative electrode, in C;

[0015] Q p The capacity of the positive electrode active material, in C;

[0016] x0 initial lithium insertion rate of negative electrode, dimensionless;

[0017] y0 initial lithium insertion rate of positive electrode, dimensionless;

[0018] D x The range of variation of the negative electrode lithium insertion rate, dimensionless;

[0019] D y The range of variation of the positive electrode lithium insertion rate, dimensionless;

[0020] τ e Liquid phase diffusion time constant, in seconds;

[0021] τ nThe negative electrode solid phase diffusion time constant, in seconds;

[0022] R ohm Ohm internal resistance, unit is Ω;

[0023] τ p The positive electrode solid phase diffusion time constant, in seconds;

[0024] P con Liquid phase diffusion coefficient, unit is mol·m -3 ·A -1 .

[0025] Furthermore, the SP+ model parameters that need to be identified using the particle swarm optimization algorithm in step 2 are: negative electrode reaction polarization coefficient P act_n , positive electrode reaction polarization coefficient P act_p , the capacity of the negative electrode active material Q n , the capacity of the positive electrode active material Q p , the initial lithium insertion rate of the negative electrode x0, the initial lithium insertion rate of the positive electrode y0, the negative electrode solid phase diffusion time constant τ n 、Ohmic internal resistance R ohm , positive electrode solid phase diffusion time constant τ p and the liquid phase diffusion coefficient P con .

[0026] Furthermore, in step 2, in order to reduce the workload, it is only necessary to perform identification once every 20 to 40 cycles under working condition 1, for a total of 10 identifications, and use the piecewise cubic Hermite interpolation method to simulate the shape of the degradation trajectory to obtain the SP+ model parameters corresponding to each cycle; then, based on the degradation trajectory of the SP+ model parameters under working condition 1, the support vector machine (SVM) algorithm is used to simulate the degradation trajectory of the SP+ model parameters under working condition 2, i.e., the battery health range of 80% to 70%, and the identified parameters are substituted back into the SP+ model to fit the simulated voltage curve to ensure that the error between the simulated voltage curve fitted by the identification parameters and the measured voltage curve is no more than 15mV.

[0027] Furthermore, the calculation formula for the error between the simulated voltage curve fitted by the identification parameters and the measured voltage curve is as follows:

[0028]

[0029] Among them, U actual is the measured voltage value (V), U simulated is the simulated voltage value (V), and N is the total discharge time (s).

[0030] Furthermore, the SP+ model parameters with high correlation with the battery SOH are selected after Pearson correlation analysis in step 3. High correlation means that the Pearson correlation coefficient between the SP+ model parameters and SOH is greater than 0.9. The calculation formula of the Pearson correlation coefficient of the two variables is as follows:

[0031]

[0032] Here, n is the number of data points; x and y are the values ​​of the two variables.

[0033] Furthermore, the SP+ model parameter with a high correlation with the battery SOH selected after Pearson correlation analysis in step 3 is the capacity Q of the negative electrode effective active material. n , the capacity of the positive electrode active material Q p , the initial lithium insertion rate of the negative electrode x0 and the initial lithium insertion rate of the positive electrode y0.

[0034] The present invention adopts a particle swarm optimization algorithm to fragmentarily identify the simplified electrochemical (SP+) model parameters of a part of the working condition, and uses a piecewise cubic Hermite interpolation method (PCHIP) to first simulate the degradation trajectory of the model parameters in working condition one. The advantage of PCHIP is that the shape of the parameter degradation trajectory can be retained to the greatest extent. Then, a support vector machine (SVM) algorithm is used to simulate the degradation trajectory of the model parameters in working condition two. This method of obtaining the degradation trajectory of the model parameters under different working conditions in the entire life cycle significantly reduces the computational complexity and time cost, improves the parameter identification efficiency, reduces the data demand, and ensures the accuracy of the model; the B is obtained by training the working condition one data. The P neural network (BPNN) model can accurately output the battery SOH under the condition of condition 2 after the input is changed to the SP+ model parameters obtained by simulation; therefore, the present invention extracts effective health features based on the historical charge and discharge data of battery condition 1, and the trained BPNN model can complete the health status assessment of battery condition 2 with high accuracy, thereby simulating the degradation law of retired batteries in cascade utilization, and the present invention has achieved good test results on self-test data sets of different magnifications, and the average absolute error between the predicted SOH value and the experimental SOH value is 0.85% to 1.2%, with good prediction effect and good generalization ability. It can be used in the field of battery SOH assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a flow chart of the SOH evaluation method of lithium-ion battery under variable operating conditions based on model-data hybrid drive of the present invention;

[0036] Figure 2 This is a flow chart of the particle swarm algorithm identifying SP+ model parameters in step 2 of Example 1;

[0037] Figure 3 This is a graph showing the change of the initial lithium insertion rate y0 of the positive electrode of the LFP-1 battery in Example 1 with SOH;

[0038] Figure 4 The capacity Q of the LFP-1 battery positive electrode active material in Example 1 p Graph showing changes with SOH;

[0039] Figure 5 The capacity Q of the effective active material of the negative electrode of LFP-1 battery in Example 1 n Graph showing changes with SOH;

[0040] Figure 6 This is a graph showing the change of the initial lithium insertion rate x0 of the negative electrode of the LFP-1 battery with SOH in Example 1;

[0041] Figure 7 This is a comparison diagram of the simulated voltage curve and the measured voltage curve of the LFP-1 battery in Example 1;

[0042] Figure 8 This is a diagram showing the SOH estimation result of the LFP-1 battery in Example 1;

[0043] Fig. 9 This is a diagram showing the SOH estimation results of the LFP-2 battery in Example 2;

[0044] Fig.10 This is the SOH estimation result diagram of the LFP-3 battery in Example 3. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0046] Example 1: A method for evaluating SOH of a lithium-ion battery under variable operating conditions based on a model-data hybrid drive is performed in the following steps:

[0047] Step 1. Carry out a variable-condition accelerated aging test of LFP-1 lithium-ion battery (3 lithium iron phosphate / graphite lithium-ion batteries with a rated capacity of 1.5Ah) at 60°C. The variable-condition accelerated aging test method of LFP-1 battery is as follows: first, place the battery in a 60°C constant temperature box for 8 hours to ensure that the temperature inside and outside the battery is consistent and reaches thermal equilibrium. After the shelf life is over, charge and discharge under the working condition, use a constant current constant current charging method, and charge the battery at a constant current rate of 1C until the voltage reaches 3.65V, and then maintain 3.65V for continuous constant voltage charging until the charging current drops below 75mA; then, place the battery on hold for 30 minutes, and after the shelf life is over, use a constant current discharge method to start discharging the battery at a rate of 1C, and the discharge cut-off voltage is 2.0V; after the discharge is completed, continue to place the battery on hold. 30min, after the standby, the above charge and discharge mode is used for cycle until the health of the battery decays to 80%, and the charge and discharge condition is changed to condition 2. First, the constant current constant current charging method is used to charge the battery at a constant current rate of 0.5C until the voltage reaches 3.65V, and then 3.65V is maintained for continuous constant voltage charging until the charging current drops below 75mA; then the battery is put on hold for 30min, and after the standby, the constant current discharge method is used to discharge the battery at a rate of 0.5C, and the discharge cut-off voltage is 2.0V; after the discharge, the battery is continued to be put on hold for 30min, and after the standby, the above charge and discharge mode is used for cycle until the health of the battery decays to 70%, and the LFP-1 battery variable condition accelerated aging test is completed, and the interval time for collecting battery voltage, current and capacity information is 1s.

[0048] Step 2. For the constant current discharge curve of the battery under working condition 1, the particle swarm optimization algorithm is used to carry out the simplified electrochemical (SP+) model parameter identification. The particle swarm optimization (PSO) algorithm is an optimization algorithm based on swarm intelligence, which can be effectively used for various parameter identification problems, including the parameter identification of battery electrochemical models. The principle of the particle swarm optimization algorithm to identify the parameters of the simplified electrochemical (SP+) model of the battery is as follows: The particle swarm optimization algorithm simulates the foraging behavior of a flock of birds. Particles move in the solution space and continuously update their positions through information exchange with each other to find the optimal solution. Each particle represents a potential solution, and its position is composed of multiple parameters. In the parameter identification scenario, these parameters correspond to the characteristics of the battery electrochemical model. The flow chart of the particle swarm algorithm identifying the SP+ model parameters is as follows. Figure 2 As shown, the specific steps are as follows:

[0049] (1) Initialize the particle swarm:

[0050] N particles are randomly generated. In this embodiment, the value of N is 70. The dimension of each particle is the same as the number of model parameters to be identified. The SP+ model parameters to be identified in this embodiment are: negative electrode reaction polarization coefficient P act_n , positive electrode reaction polarization coefficient P act_p , the capacity of the negative electrode active material Q n , the capacity of the positive electrode active material Q p , the initial lithium insertion rate of the negative electrode x0, the initial lithium insertion rate of the positive electrode y0, the negative electrode solid phase diffusion time constant τ n 、Ohmic internal resistance R ohm , positive electrode solid phase diffusion time constant τ p and the liquid phase diffusion coefficient P con , so the dimension of each particle in this embodiment is 10; a position and speed are randomly initialized for each particle, where the values ​​of the position and speed are affected by the manually set model parameter range and the identification speed range. The parameter range of LFP-1 lithium iron phosphate battery identified in this embodiment and the identification range set by the optimization algorithm parameter identification are shown in Table 1;

[0051] Table 1 SP+ model parameter identification intervals

[0052]

[0053] (2) Define the objective function:

[0054] An objective function is determined to evaluate the fitness of each particle. For the simplified electrochemical model parameters identified in this embodiment, the objective function used is the root mean square error between the simulated voltage fitted by the identification parameters and the measured voltage.

[0055] (3) Evaluate particle fitness:

[0056] For each particle, that is, the identified model parameters, the simulated voltage is fitted by back-substitution of the simplified electrochemical model, and compared with the measured voltage, and the fitness value is calculated by the objective function. For this embodiment, the fitness value is calculated as follows:

[0057]

[0058] Among them, U actual is the measured voltage value (V), U simulated is the simulation voltage value (V), and n is the total discharge time (s).

[0059] (4) Update individual optimum and global optimum:

[0060] Each particle maintains an individual optimal position and records the position with the best fitness. In each round of iteration, the global optimal position is updated, that is, the position with the best fitness among all particles.

[0061] (5) Update particle speed and position:

[0062] Update the particle's velocity and position according to the following formula:

[0063] Speed ​​Update:

[0064] v i =ω×v i +c1×r1×(p i -x i )+c2×r2×(g i -x i )

[0065] Among them, ω is the inertia weight, c1 and c2 are individual and group learning factors, r1 and r2 are random numbers, and p i is the optimal position of the individual, g i is the global optimal position, x i is the current position of the particle, v i is the current velocity of the particle.

[0066] Location Updates:

[0067] x i =x i +v i

[0068] (6) Iteration and termination:

[0069] Repeat steps (3) to (5) until the termination condition is met. The termination condition in this embodiment is that the number of iterations reaches 120.

[0070] (7) Parameter output:

[0071] Finally, the parameter value corresponding to the global optimal position is output, which is the optimal value of the battery simplified electrochemical model parameter to be identified. The particle swarm optimization algorithm simulates the group behavior in nature to find the optimal parameters of the battery SP+ model. Its advantages are that it is easy to implement, can handle complex multi-dimensional search spaces, and is insensitive to initial conditions.

[0072] In order to reduce the workload, it is only necessary to identify once every 25 laps under working condition one, for a total of 10 times, to obtain 10 model parameter sets under working condition one, and each parameter set includes 10 SP+ model parameters; the piecewise cubic Hermite interpolation method is used to simulate the shape of the degradation trajectory of each model parameter, and the degradation trajectory of the SP+ model parameters in the battery health range of 100% to 80% is obtained. Then, based on the degradation trajectory of the SP+ model parameters under working condition one, the support vector machine (SVM) algorithm is used to simulate the degradation trajectory of the SP+ model parameters under working condition two, that is, the battery health range of 80% to 70%.

[0073] In step 2, the calculation formula for the error between the simulated voltage curve fitted by the identification parameters and the measured voltage curve is as follows:

[0074]

[0075] Among them, U actual is the measured voltage value, in V; U simulated is the simulation voltage value, in V; N is the total discharge time, in s.

[0076] The identification parameters are substituted back into the SP+ model to fit the simulated voltage curve, ensuring that the error between the simulated voltage curve fitted by the identification parameters and the measured voltage curve is no more than 15mV, thus ensuring the accuracy of the model.

[0077] Step 3. Pearson correlation analysis is a statistical method used to measure the linear correlation between two variables. The core of this method is to calculate the correlation coefficient (Pearson correlation coefficient), whose value range is between -1 and 1. The specific meaning is: 1 means perfect positive correlation, and the two variables increase as one increases while the other increases; -1 means perfect negative correlation, and the two variables increase as one increases while the other decreases; 0 means no correlation, and there is no linear relationship between the two variables. The calculation formula of Pearson correlation coefficient (usually represented by r) is as follows:

[0078]

[0079] Wherein: N is the number of samples, x and y are two variables; for this embodiment, x is the SP+ model parameter as a health feature, and y is the battery health state SOH.

[0080] In this step, the various parameter spaces in the simplified electrochemical SP+ model of LFP-1 lithium iron phosphate battery are: initial lithium ion concentration c0 in the electrolyte, negative electrode reaction polarization coefficient P act_n , positive electrode reaction polarization coefficient P act_p , the capacity Q of the negative electrode effective active material n , the capacity Q of the positive electrode active materialp , the initial lithium insertion rate of the negative electrode x0, the initial lithium insertion rate of the positive electrode y0, and the range of change of the negative electrode lithium insertion rate D x , the range of change of positive electrode lithium insertion rate D y , liquid phase diffusion time constant τ e , negative electrode solid phase diffusion time constant τ n 、Ohmic internal resistance R ohm , positive electrode solid phase diffusion time constant τ p and the liquid phase diffusion coefficient P con .

[0081] Specifically, the mathematical representation of the model is shown in Table 2, and the definitions of parameters and variables in the SP+ model are shown in Tables 3 and 4, respectively.

[0082] Table 2 SP+ model mathematical representation

[0083]

[0084]

[0085] Table 3 SP+ model parameter definition

[0086]

[0087]

[0088] Table 4 SP+ model variable definitions

[0089]

[0090] Battery open circuit potential U OCV It is only affected by the lithium ion concentration fractions y and x in the positive and negative electrodes. After the battery is formed (after the battery is manufactured, the internal positive and negative electrode materials are activated through a certain charging and discharging method to improve the battery's charging and discharging performance and self-discharge, storage and other comprehensive performance. The process is called formation) and the loss of lithium ions in a single charge and discharge cycle is ignored. The total lithium content in the positive and negative electrodes is fixed and is only transferred from one electrode to the other. The lithium concentration fraction of the electrode at the end of charging is taken as the initial state, corresponding to y0 and x0. The range of change of y and x during discharge is D y and D x , Among them, Q all is the total capacity of the model. It should be noted that, considering the physical meaning of the model parameters themselves and the empirical law of the actual parameter acquisition results, D x The valid range for is x0 minus 0 to 0.04.

[0091] The calculation method of the lithium ion concentration fraction of the positive and negative electrodes using the total ion amount is:

[0092]

[0093] During the discharge process, the battery U OCV The calculation formula is:

[0094]

[0095] When current flows through the battery, the ohmic polarization potential η ohm , reaction polarization potential η act It will be generated immediately, and the concentration of solid and liquid lithium ions in the battery will begin to change, but the change in concentration is a slow process. Therefore, the corresponding overpotential also changes gradually, which is described by a time constant in the model.

[0096] η con , η act , η ohm The total polarization voltage U polar :

[0097] U polar =η con +η act+ η ohm

[0098] The Pearson correlation coefficient between the SP+ model parameters and the battery SOH of LFP-1 lithium iron phosphate battery, and the correlation between the model parameters and the battery SOH are shown in Table 5;

[0099] Table 5 Pearson correlation analysis between SP+ model parameters and battery SOH

[0100]

[0101]

[0102] From the Pearson correlation analysis, we can see that the capacity Q of the negative electrode active material is n , the capacity of the positive electrode active material Q p , the initial lithium insertion rate x0 of the negative electrode is strongly positively correlated with the battery SOH, and the initial lithium insertion rate y0 of the positive electrode is strongly negatively correlated with the battery SOH. Therefore, after Pearson correlation analysis, these model parameters are selected as health features to input the BP neural network (BPNN) algorithm to estimate the battery SOH. The data of working condition 1 is the training set, and the data of working condition 2 is the test set;

[0103] Step 4: The BPNN model trained with the operating condition 1 data can accurately output the battery SOH under the operating condition 2 after the input is changed to the operating condition 2 SP+ model parameters obtained by simulation, thus completing the battery health status assessment under variable operating conditions.

[0104] The error of SOH prediction of LFP-1 lithium iron phosphate battery under variable operating conditions in the test set is shown in Table 6.

[0105] Table 6 Errors in SOH prediction under variable operating conditions for the LFP-1 battery test set

[0106]

[0107] From the results in Table 6, it can be seen that the BP neural network (BPNN) model trained with the data from operating condition 1 is used to predict the battery SOH under operating condition 2. Taking the mean absolute error as an indicator, the accuracy can reach more than 99%. The maximum absolute error of the SOH predicted under variable operating conditions is less than 3%, which verifies that this method can be used to predict the SOH of lithium-ion batteries under variable operating conditions.

[0108] In step 3 of this embodiment 1, the initial lithium insertion rate y0 of the positive electrode of the LFP-1 battery changes with SOH as shown in the figure Figure 3 As shown by Figure 3 It can be seen that the initial lithium insertion rate y0 of the battery positive electrode has a strong negative correlation with SOH. Among them, the parameters of operating condition 1 (SOH: 100%~80%) were identified 10 times in total, and then the piecewise cubic Hermite interpolation method was used to fit the parameter degradation trajectory, so as to simulate the degradation trajectory of the parameters in the historical data of retired batteries through a few parameter identifications. The parameters of operating condition 2 (SOH: 80%~70%) were predicted using the support vector machine (SVM) algorithm to simulate and predict the parameter degradation trajectory of the future cascade utilization scenario of retired batteries.

[0109] In step 3 of this embodiment 1, the capacity Q of the LFP-1 battery positive electrode effective active material is p The change of SOH is shown in the figure Figure 4 As shown by Figure 4 It can be seen that the capacity Q of the effective active material of the battery positive electrode p It has a strong positive correlation with SOH. The parameters of operating condition 1 (SOH: 100%~80%) were identified 10 times in total, and then the piecewise cubic Hermite interpolation method was used to fit the parameter degradation trajectory, so as to simulate the degradation trajectory of the parameters in the historical data of retired batteries through a small number of parameter identifications. The parameters of operating condition 2 (SOH: 80%~70%) were predicted using the support vector machine (SVM) algorithm to simulate and predict the parameter degradation trajectory of the future cascade utilization scenario of retired batteries.

[0110] In step 3 of this embodiment 1, the capacity Q of the effective active material of the negative electrode of the LFP-1 battery is n The change of SOH is shown in the figure Figure 5 As shown, Figure 5 It shows the capacity Q of the effective active material in the negative electrode of the battery nIt has a strong positive correlation with SOH. The parameters of operating condition 1 (SOH: 100%~80%) were identified 10 times in total, and then the piecewise cubic Hermite interpolation method was used to fit the parameter degradation trajectory, so as to simulate the degradation trajectory of the parameters in the historical data of retired batteries through a small number of parameter identifications. The parameters of operating condition 2 (SOH: 80%~70%) were predicted using the support vector machine (SVM) algorithm to simulate and predict the parameter degradation trajectory of the future cascade utilization scenario of retired batteries.

[0111] In step 3 of this embodiment 1, the initial lithium insertion rate x0 of the negative electrode of the LFP-1 battery changes with SOH as shown in the figure Figure 6 As shown, Figure 6 It shows that the initial lithium insertion rate x0 of the negative electrode of the battery has a strong positive correlation with SOH. The parameters of operating condition 1 (SOH: 100%~80%) were identified 10 times in total, and then the piecewise cubic Hermite interpolation method was used to fit the parameter degradation trajectory, so as to simulate the degradation trajectory of the parameters in the historical data of retired batteries through a few parameter identifications. The parameters of operating condition 2 (SOH: 80%~70%) were predicted using the support vector machine (SVM) algorithm to simulate and predict the parameter degradation trajectory of the future cascade utilization scenario of retired batteries.

[0112] Figure 7 This is a comparison diagram of the simulated voltage curve and the measured voltage curve of the LFP-1 battery in Example 1. Figure 7 The first cycle of the battery parameter identification was shown. The parameter identification effect was measured by the mean absolute error between the simulated voltage curve and the measured voltage curve. The error was 5.9mV, indicating a good identification effect.

[0113] Figure 8 This is the SOH estimation result diagram of the LFP-1 battery in Example 1. Figure 8 The mapping relationship between model parameters and battery SOH is demonstrated by using BP neural network (BPNN) model to establish the mapping relationship between model parameters and battery SOH for LFP-1 battery. The training set is the data of operating condition 1 (SOH: 100%~80%), and the test set is the data of operating condition 2 (SOH: 80%~70%). The charge and discharge rate of operating condition 1 of LFP-1 battery is 1C, and the charge and discharge rate of operating condition 2 is 0.5C. The average absolute error between the predicted SOH value and the experimental SOH value is used to measure the prediction effect of variable operating condition SOH. The error is 0.8491%, and the prediction effect is good.

[0114] Example 2: The difference between this example and Example 1 is that in step 1, three lithium iron phosphate / graphite lithium-ion batteries with a rated capacity of 1.5Ah are recorded as LFP-2 batteries, and the charging and discharging condition 1 of the LFP-2 batteries is to use a 2C charge and discharge rate to age to a SOH of 80%, and the second condition is to use a 1C charge and discharge rate to age to 70%; the other steps and parameters are the same as in Example 1.

[0115] Fig. 9 This is the SOH estimation result diagram of LFP-2 battery. Fig. 9 The mapping relationship between the model parameters and the battery SOH was demonstrated by using the BP neural network (BPNN) model to establish the LFP-2 battery. The training set was the data of working condition 1 (SOH: 100% to 80%), and the test set was the data of working condition 2 (SOH: 80% to 70%). The charge and discharge rate of working condition 1 of the LFP-2 battery was 2C, and the charge and discharge rate of working condition 2 was 1C. The average absolute error between the predicted SOH value and the experimental SOH value was used to measure the prediction effect of the variable working condition SOH. The error was 0.8611%, and the prediction effect was good.

[0116] Embodiment 3: This embodiment is different from Embodiment 1 in that in step 1, three lithium iron phosphate / graphite lithium ion batteries with a rated capacity of 1.5Ah are recorded as LFP-3 batteries, and the LFP-3 batteries are charged and discharged under the first condition of aging at a 3C charge and discharge rate to 80% SOH, and the second condition is aging at a 1.5C charge and discharge rate to 70%. The other steps and parameters are the same as those in Embodiment 1.

[0117] Fig.10 This is the SOH estimation result diagram of LFP-3 battery. Fig.10 It shows that the mapping relationship between model parameters and battery SOH is established by using BP neural network (BPNN) model for LFP-3 battery. The training set is the data of working condition 1 (SOH: 100%~80%), and the test set is the data of working condition 2 (SOH: 80%~70%). The charge and discharge rate of working condition 1 of LFP-3 battery is 3C, and the charge and discharge rate of working condition 2 is 1.5C. The average absolute error between the predicted SOH value and the experimental SOH value is used to measure the prediction effect of variable working condition SOH. The error is 1.1717%, and the prediction effect is good.

[0118] Although the present invention is described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the present invention. It should therefore be understood that many modifications may be made to the exemplary embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that the various dependent claims and features described herein may be combined in a manner different from that described in the original claims. It should also be understood that features described in conjunction with individual embodiments may be used in other described embodiments.

Claims

1. A lithium-ion battery variable operating condition SOH evaluation method based on a model-data hybrid drive, characterized by The method comprises the following steps: Step 1, carry out a lithium-ion battery variable working condition accelerated aging test at 55-65°C, the charge and discharge rate of working condition one is twice that of working condition two, after the battery is aged to SOH of 80% under working condition one, the battery is further aged to SOH of 70% under working condition two, and physical information is collected during the accelerated aging test, wherein the physical information is battery voltage, current and capacity; Step 2: For the constant current discharge curve of the battery under working condition 1, the particle swarm optimization algorithm is used to carry out the simplified electrochemical SP+ model parameter identification, and the degradation trajectory of the SP+ model parameters in the battery health range of 100% to 80% is obtained; Step 3: Use the Pearson correlation analysis method to select the SP+ model parameters with high correlation with the battery SOH, and input these model parameters as health features into the BP neural network algorithm to estimate the battery SOH. The data of working condition 1 is the training set, and the data of working condition 2 is the test set. Step 4: Based on the BPNN model trained with the data from working condition 1, after the input is changed to the SP+model parameters of working condition 2 obtained by simulation, the battery SOH under working condition 2 is output, which is the SOH of the lithium-ion battery under the variable working condition, thus completing the evaluation of the health status of the battery under the variable working condition.

2. The method for evaluating SOH of lithium-ion batteries under variable operating conditions based on model-data hybrid drive according to claim 1 is characterized in that: The parameters in the simplified electrochemical (SP+) model of the lithium-ion battery described in step 2 are c0, P act_n , P act_p , Q n , Q p , x0, y0, D x , D y , τ e , τ n , R ohm , τ p , P con ; Where c0 is the initial lithium ion concentration in the electrolyte, in mol·m -3 ; P act_n is the negative electrode reaction polarization coefficient, in m -1.5 ·mol 0.5 ·s; P act_p is the positive electrode reaction polarization coefficient, in m -1.5 ·mol 0.5 ·s; Q n is the capacity of the effective active material of the negative electrode, in C; Q p The capacity of the positive electrode’s effective active material, in C; x0 initial lithium insertion rate of negative electrode, dimensionless; y0 initial lithium insertion rate of positive electrode, dimensionless; D x The range of variation of the negative electrode lithium insertion rate, dimensionless; D y The range of variation of the positive electrode lithium insertion rate is dimensionless; τ e Liquid phase diffusion time constant, in seconds; τ n The negative electrode solid phase diffusion time constant, in seconds; R ohm Ohm internal resistance, unit is Ω; τ p The positive electrode solid phase diffusion time constant, in seconds; P con Liquid phase diffusion coefficient, unit is mol·m -3 ·A -1 .

3. The method for evaluating SOH of lithium-ion batteries under variable operating conditions based on model-data hybrid drive according to claim 1 or 2, characterized in that: The SP+ model parameters that need to be identified using the particle swarm optimization algorithm described in step 2 are: negative electrode reaction polarization coefficient P act_n , positive electrode reaction polarization coefficient P act_p , the capacity of the negative electrode active material Q n , the capacity of the positive electrode active material Q p , the initial lithium insertion rate of the negative electrode x0, the initial lithium insertion rate of the positive electrode y0, the negative electrode solid phase diffusion time constant τ n 、Ohmic internal resistance R ohm , positive electrode solid phase diffusion time constant τ p and the liquid phase diffusion coefficient P con .

4. The method for evaluating SOH of a lithium-ion battery under variable operating conditions based on a model-data hybrid drive according to claim 1 or 2, characterized in that: In step 2, under working condition 1, identification is performed once every 20 to 40 cycles, for a total of 10 times, and the shape of the degradation trajectory is simulated using the piecewise cubic Hermite interpolation method to obtain the SP+ model parameters corresponding to each cycle; then, based on the degradation trajectory of the SP+ model parameters under working condition 1, the support vector machine algorithm is used to simulate the degradation trajectory of the SP+ model parameters under working condition 2, i.e., the battery health range of 80% to 70%, and the identified parameters are substituted back into the SP+ model to fit the simulated voltage curve to ensure that the error between the simulated voltage curve fitted by the identification parameters and the measured voltage curve is no more than 15mV.

5. The method for evaluating SOH of lithium-ion batteries under variable operating conditions based on model-data hybrid drive according to claim 4 is characterized in that: The calculation formula for the error between the simulated voltage curve fitted by the identification parameters and the measured voltage curve is as follows: Among them, U actual is the measured voltage value, in V; U simulated is the simulation voltage value, in V; N is the total discharge time, in s.

6. The method for evaluating SOH of lithium-ion batteries under variable operating conditions based on model-data hybrid drive according to claim 1 or 2, characterized in that: The SP+ model parameters with high correlation with the battery SOH are selected after Pearson correlation analysis in step 3. High correlation means that the Pearson correlation coefficient between the SP+ model parameters and SOH is greater than 0.

9. The calculation formula of the Pearson correlation coefficient of the two variables is as follows: Here, n is the number of data points; x and y are the values ​​of the two variables.

7. The method for evaluating SOH of a lithium-ion battery under variable operating conditions based on a model-data hybrid drive according to claim 1 or 2, characterized in that: The SP+ model parameter with a high correlation with the battery SOH selected after Pearson correlation analysis in step 3 is the capacity Q of the negative electrode effective active material. n , the capacity of the positive electrode active material Q p , the initial lithium insertion rate of the negative electrode x0 and the initial lithium insertion rate of the positive electrode y0.

Images