A method for deriving and predicting the cycle life of any topological structure of a battery pack

Abstract

The present invention provides a method for predicting the cycle life of any topological structure of a battery pack, simulating the aging trajectory models of all typical structures of the battery pack, generating a training set describing the topological structure, and using the training set to train the cycle life prediction neural network of the series-then-parallel topological structure and the cycle life prediction neural network of the parallel-then-series topological structure; for a battery pack with given design requirements, all possible topological structures of the battery pack are respectively input into the trained cycle life prediction neural network of the series-then-parallel topological structure and the cycle life prediction neural network of the parallel-then-series topological structure to predict the cycle life of the topological structure battery pack. The present invention can maximize the overall performance of the battery pack and reduce the operating cost under the unit standard cycle condition.

Description

Technical Field

[0001] The present invention relates to the technical field of batteries, and particularly to a method for deriving and predicting the cycle life of any topological structure of a battery pack. Background Art

[0002] Under the current battery manufacturing technology conditions, when single cells are connected in series and parallel to form a group for use, due to the inconsistent battery parameters that occur in the production, screening, use, maintenance and other links of the single cells, the battery module formed often fails to reach the original level of the single cell in use. This inconsistency phenomenon will not only reduce the use efficiency and service life of the entire battery pack, but may even bring uncontrollable safety problems if not properly managed. How to optimize the best series-parallel connection method of the battery under the existing conditions to achieve a balance between the management cost and the service life of the battery module, so as to maximize the overall cycle life of the battery pack on the premise of ensuring battery safety, reduce the operating cost under the unit standard cycle conditions, and realize the maximum benefit of the energy storage system has important theoretical and engineering value.

[0003] In order to obtain the cycle life of battery packs with all possible topological structures under the same battery array conditions, by establishing simulation models of battery packs with all possible topological structures, simulating all topological structure battery packs, and then realizing the calibration of the optimal topological structure. However, building all possible topological structures will consume a lot of manpower and has poor universality. Summary of the Invention

[0004] Aiming at the deficiencies in the prior art, the present invention provides a method for deriving and predicting the cycle life of any topological structure of a battery pack, which solves the problem that the topological structure affects the cycle life of the battery pack.

[0005] The present invention achieves the above technical objectives through the following technical means.

[0006] A method for deriving and predicting the cycle life of any topological structure of a battery pack is specifically as follows:

[0007] Construct a neural network for predicting the cycle life of the series-then-parallel topological structure of the battery pack and a neural network for predicting the cycle life of the parallel-then-series topological structure. The input of the neural network is: a 1×4 vector composed of the number of series and parallel connections of the battery pack topological structure, and the output of the neural network is: the cycle life index;

[0008] Simulate the aging trajectory models of all typical structures of the battery pack, obtain the number of cycles when the capacity of the typical structure battery pack decays to 80% of its rated capacity, and generate a training set with a 1×4 vector describing the topological structure as the input and the number of cycles as the output; the training set is used to train the neural network for predicting the cycle life of the series-then-parallel topological structure and the neural network for predicting the cycle life of the parallel-then-series topological structure;

[0009] For a battery pack given the design requirements, all possible topologies of the battery pack are respectively input into the trained series - then - parallel topology cycle life prediction neural network and the parallel - then - series topology cycle life prediction neural network to predict the cycle life of the battery pack with the corresponding topology.

[0010] Further, the aging trajectory model is obtained through the following method:

[0011] The battery model parameters and their distribution are respectively input into the aging trajectory models of each single - cell battery. Continuously calculate the current and voltage of each single - cell battery at different times. Utilize the relationship between each model parameter and the capacity loss amount to update the capacity and model parameter values of all single - cell batteries in real - time. Combine the monitoring of the extreme voltage in the battery pack and the continuous recursive calculation of the state of health (SOH) based on the loss of active lithium to complete the charge - discharge simulation of the overall model, and then finally establish the aging trajectory model of the battery pack.

[0012] Even further, the single - cell battery aging trajectory model is based on the analysis of the battery capacity loss and its impact on the single - cell model parameters. The single - cell model parameters include the increase in battery internal resistance and the open - circuit voltage.

[0013] Even further, the relationship between the battery capacity loss and the increase in battery internal resistance is: where R o is the ohmic internal resistance of the battery, α1 and α2 are model parameters that need to be determined through experiments, C loss is the capacity loss of the battery, and θ1, θ2, and θ3 are model coefficients that need to be determined through experiments, T k , T std are the working temperature and the standard working temperature respectively, and Δt chg,k is the actual charging time between two monitors.

[0014] Even further, the relationship between Δt chg,k and is a linear relationship, specifically: where E a,SEI is the activation energy for the diffusion of the solvent in the solid electrolyte interphase (SEI), and R is the gas constant.

[0015] Even further, the relationship between the open - circuit voltage and the battery capacity loss is: perform interpolation calculations on the open - circuit voltage OCV under different states of charge (SOC) and different states of health (SOH).

[0016] Even further, the continuous recursive calculation of the SOH based on the loss of active lithium is specifically:

[0017]

[0018] Among them, Q is the initial rated capacity of the battery, and SOH k is the state of health of the battery.

[0019] Further, the typical structure of the battery pack includes a series-then-parallel structure, a parallel-then-series structure, and a hybrid structure combining the two.

[0020] Further, the cycle life index is the number of cycles when the capacity of the battery pack decays to 80% of its rated capacity.

[0021] Further, it includes a 1×4 vector composed of the number of series and parallel connections, specifically:

[0022] The 1×4 vector of the series-then-parallel sequential topology structure is: [number of series connections from single cell to module, number of parallel connections from single cell to module, number of series connections from module to overall, number of parallel connections from module to overall];

[0023] The 1×4 vector of the parallel-then-series sequential topology structure is: [number of parallel connections from single cell to module, number of series connections from single cell to module, number of parallel connections from module to overall, number of series connections from module to overall];

[0024] If there is no series or parallel connection from the module to the overall in the topology structure, the values of the third and fourth positions in the vector, namely the number of series connections from module to overall or the number of parallel connections from module to overall, are default values of 1.

[0025] The beneficial effects of the present invention are as follows: By configuring a BP neural network for regression prediction, selecting the typical structure of the battery pack and establishing an aging trajectory model of the typical structure based on the mechanism of recyclable lithium loss, simulating the aging trajectory model of the typical structure under given temperature and current rate conditions to obtain the cycle life of the typical structure battery pack, thereby generating a training set for the BP neural network, training the cycle life prediction neural network of the series-then-parallel topology structure and the cycle life prediction neural network of the parallel-then-series topology structure respectively through this training set, and finally realizing the derivation and prediction of the cycle life of any topology structure of the battery pack; By deriving and predicting the cycle life of any topology structure of the battery pack, taking the cycle life as the standard, selecting the battery pack topology structure with the optimal cycle life, maximizing the overall performance of the battery pack, reducing the operating cost under the unit standard cycle condition, and being beneficial to maximizing the benefits of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flowchart of the method for deriving and predicting the cycle life of any topology structure of the battery pack according to the present invention;

[0027] Figure 2 It is a schematic diagram of the neural network structure according to the present invention;

[0028] Figure 3 It is a schematic diagram of the first-order R-RC equivalent circuit model according to the present invention;

[0029] Figure 4 It is a process diagram for constructing the battery pack aging trajectory model provided by an embodiment of the present invention;

[0030] Figure 5 It is a comparison schematic diagram of the number of cycles of the actual topology structure, the number of cycles predicted by the present method, and the number of cycles obtained by the simulation of the modeling method provided by an embodiment of the present invention. Specific embodiments

[0031] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, but the protection scope of the present invention is not limited thereto.

[0032] As Figure 1 shown, a method for predicting the cycle life of any topology structure of a battery pack according to the present invention specifically includes the following steps:

[0033] In order to analyze the prediction situation of the method of the present invention, a non - typical array with a series number of 6 and a parallel number of 2 is taken as an example. The present invention uses the widely used lithium iron phosphate / graphite (LFP / GIC) battery as an experimental sample, and the working condition is 1C current rate / 25 °C. For the derivation and prediction of the cycle life of the topology structure, a method for deriving and predicting the cycle life of any topology structure based on the continuous recursive calculation of the loss of active lithium and the simulation of the cycle life of the typical topology structure specifically includes the following steps:

[0034] Step (1), construct a neural network framework for predicting the cycle life of the battery pack topology structure

[0035] Step (1.1), determine the input and output of the neural network

[0036] The battery grouping topology structure mainly includes the series - first - then - parallel structure, the parallel - first - then - series structure, and the hybrid structure combining the two, which can be specifically reduced to two sequential cases of series - first - then - parallel and parallel - first - then - series. For industrial applications, the battery grouping method is at most from the module to the whole, and there is no case of multiple sub - module grouping. Therefore, the present invention uses a 1×4 vector composed of the series number and the parallel number (for example, [2 2 2 2]) to describe the topology structure, takes the 1×4 vector as the feature input quantity of the neural network, and the cycle life index (the number of cycles when the battery pack capacity decays to 80% of its rated capacity) as the feature output quantity of the neural network. As Figure 2 shown.

[0037] Among them, the corresponding positions of the series and parallel values in the 1×4 vector describing the series-then-parallel sequential topology structure of the battery pack are: the number of series connections from the single cell to the module - the number of parallel connections from the single cell to the module - the number of series connections from the module to the whole - the number of parallel connections from the module to the whole. The vector form is [the number of series connections from the single cell to the module, the number of parallel connections from the single cell to the module, the number of series connections from the module to the whole, the number of parallel connections from the module to the whole]. The corresponding positions of the series and parallel values in the 1×4 vector describing the parallel-then-series sequential topology structure of the battery pack are: the number of parallel connections from the single cell to the module - the number of series connections from the single cell to the module - the number of parallel connections from the module to the whole - the number of series connections from the module to the whole. The vector form is [the number of parallel connections from the single cell to the module, the number of series connections from the single cell to the module, the number of parallel connections from the module to the whole, the number of series connections from the module to the whole]. If there is no series or parallel connection from the module to the whole in the topology structure, the values of the number of series connections from the module to the whole or the number of parallel connections from the module to the whole in the third and fourth positions of the vector are default values of 1. Through the vector description method in the above form, the digital expression of the topology structure form can be realized, which is beneficial to the cycle life of the battery pack topology structure to be predicted through a neural network.

[0038] To avoid ambiguity in predicting the cycle life of battery packs with series-then-parallel and parallel-then-series sequential structures by a neural network, the present invention respectively constructs: a neural network for predicting the cycle life of a series-then-parallel topology structure, and a neural network for predicting the cycle life of a parallel-then-series topology structure.

[0039] Step (1.2), design and configure the neural network

[0040] Since the BP neural network model is superior in dealing with complex and fuzzy mapping relationships and does not require knowledge of the data distribution form and the relationship between variables, the present invention adopts the BP neural network simulation algorithm for training and prediction.

[0041] The problem of predicting the cycle life of the topology structure proposed by the present invention is a function fitting problem. Therefore, the 'trainlm' training function is selected for neural network training; where the network target error 'net.trainparam.goal' is 1e-3, the learning rate 'net.trainparam.lr' is 0.03, and the maximum number of training steps 'net.trainparam.epochs' is 1000; according to the above parameter settings of the BP neural network, a neural network framework for predicting the cycle life of the battery pack topology structure is constructed.

[0042] Step (2), generate the training set of the neural network

[0043] Step (2.1), select typical structures of the battery pack as training samples

[0044] The selection of typical structures needs to cover all types of structures (series-parallel structures, parallel-series structures, and hybrid structures combining the two); and since the sample inputs of the neural network should be as evenly distributed as possible, the probability of poor prediction ability caused by excessive training in areas with dense samples and less obvious training traces in areas with fewer samples is reduced. Based on the above principles, the present invention selects different topological structures with the total number of batteries in the battery pack being 16, 32, 64, and 128 as typical simulation structures, which can be specifically divided into typical series-parallel structures and typical parallel-series structures. Among them, the typical series-parallel structures include: 4 series 4 parallel, first 2 series 2 parallel and then 2 series 2 parallel, first 2 series 4 parallel and then 2 series, 16 series 2 parallel, first 4 series 2 parallel and then 4 series, first 2 series 2 parallel and then 4 series 2 parallel, 32 series 2 parallel, first 4 series 2 parallel and then 8 series, first 2 series 16 parallel and then 2 series, first 4 series 2 parallel and then 4 series 2 parallel, first 2 series 4 parallel and then 4 series 2 parallel, 2 series 64 parallel, first 4 series 4 parallel and then 8 series, first 4 series 4 parallel and then 2 series 4 parallel, 8 series 16 parallel, first 2 series 8 parallel and then 2 series 4 parallel; the typical parallel-series structures include: 4 parallel 4 series, first 2 parallel 2 series and then 2 parallel 2 series, first 2 parallel 4 series and then 2 parallel, 16 parallel 2 series, first 4 parallel 2 series and then 4 parallel, first 2 parallel 2 series and then 4 parallel 2 series, 32 parallel 2 series, first 4 parallel 2 series and then 8 parallel, first 2 parallel 16 series and then 2 parallel, first 4 parallel 2 series and then 4 parallel 2 series, first 2 parallel 4 series and then 4 parallel 2 series, 2 parallel 64 series, first 4 parallel 4 series and then 8 parallel, first 4 parallel 4 series and then 2 parallel 4 series, 8 parallel 16 series, first 2 parallel 8 series and then 2 parallel 4 series.

[0045] Step (2.2), constructing a typical battery pack aging trajectory model using simulation software

[0046] In order to accurately characterize the cycle life of a typical battery pack, the present invention starts from constructing the aging mechanism of a single battery, considers the influence of inconsistent parameters within the battery pack, and uses simulation software to construct an aging trajectory model of a typical structure battery pack.

[0047] In terms of a single battery, based on the analysis of the time constants corresponding to each link inside the battery, the present invention selects a first-order R-RC equivalent circuit model as the modeling benchmark, and its specific model form and the mathematical expression of the battery terminal voltage are respectively as Figure 3 shown in Equation (1). In the figure, U bat is the battery terminal voltage (V), I bat is the total current flowing through the battery (A), OCV is the open-circuit voltage of the battery, R o , R d are the ohmic and diffusion internal resistances of the battery (Ω) respectively, C d is the diffusion capacitance, η o , η d are the ohmic overpotential and polarization overpotential (V) respectively, and I d is the diffusion current of the battery (A).

[0048]

[0049] On the basis of the selected equivalent circuit model, the present invention will establish the mechanistic relationship between each model parameter of the equivalent circuit model and the capacity loss amount on the basis of realizing the battery capacity loss calculation.

[0050] During the normal use of the battery, its capacity loss is mainly caused by the consumption of circulatory lithium in the battery during the formation and thickening of the solid electrolyte film (SEI film) on the surface of the negative electrode. To this end, the present invention starts from the conventional aging mechanism of single cells and is based on the classic active lithium loss model (as shown in formula (2)). a The battery capacity loss is mainly caused by the formation and growth of SEI film, and its mathematical expression is a relationship between the battery charging time t chg If the battery is placed under a certain standard stress condition (i.e. operating temperature T = standard operating temperature T std ) of battery capacity loss LLI a,surf Standard charging time As a standard curve, it can be considered that the change of T will have a significant effect on this standard curve. It plays the role of acceleration (or deceleration). Therefore, the active lithium capacity loss LLI under any working temperature T can be a Rewritten as standard charging time The acceleration (or deceleration) expression is shown in formula (3):

[0051]

[0052]

[0053] In the above formula, C loss is the capacity loss of the battery (Ah), LLI a is the active lithium capacity loss of the battery during aging (Ah), LLI0 is the initial active lithium capacity loss of the battery after factory formation (Ah); t chg 、 are charging time and equivalent charging time under standard conditions (s); T, T std are working temperature and standard working temperature (K), respectively. Here T std =303K;k SEI Generates a reaction correlation coefficient for the SEI film, E a,SEI is the activation energy of solvent diffusion in SEI (J / mol), and R is the gas constant (8.314 J / (mol·K)).

[0054] Furthermore, the battery charging time t under any conditions can be chg Converted to its standard temperature T = Tstd The standard charging time under As shown in Equation (4):

[0055]

[0056] Since is linearly related to t chg , it can be discretized for . As shown in Equation (4), where and Δt chg,k represent the standard charging time and the actual charging time between two monitors respectively; meanwhile, the definitions of all physical quantities remain unchanged, and only rewritten into their respective discretized forms at the k-th moment, k = 1, 2, ….

[0057]

[0058] By accumulating , the cumulative total amount of the equivalent charging time corresponding to the dynamic working condition can be obtained; finally, the recurrence expressions of the battery capacity loss and its corresponding equivalent charging time under the time-varying working condition are obtained, as shown in Equation (6):

[0059]

[0060] After organizing Equation (6), the lumped parameter model for calculating the capacity loss recurrence is obtained as shown in Equation (7), where θ1~θ3 are model coefficients to be determined by experiments.

[0061]

[0062] Using the capacity loss recurrence calculation to characterize SOH, it is:

[0063]

[0064] where Q is the initial rated capacity of the battery, and SOH k is the battery health state under the time-varying working condition.

[0065] In addition, based on the theoretical analysis of the interaction relationship between the loss of recyclable lithium (LLI) and the increment of the battery ohmic internal resistance, the functional relationship between the increase of the battery internal resistance and the battery capacity loss is obtained, as shown in Equation (9). In Equation (9), α1 and α2 are model parameters to be determined by experiments.

[0066]

[0067] Through experiments on the battery at different temperatures and different aging degrees, parameter identification is carried out by the least square method, and it is determined that parameter θ1 is 8.43*10 6 and θ2 is 7.02*103 and θ3 is 0.59, α1 is 4.15×10 -3 and α2 is 2.62×10 -4 .

[0068] Meanwhile, while the loss of active lithium (LLI) causes the overall capacity attenuation of the battery, it will also have a corresponding impact on the open circuit voltage curve OCV of the battery. Based on the analysis of the variation law of the battery in the combined coordinate system of electrode potential, it can be known that the OCV curve of the battery in the LLI stage will show a clear variation law: as the battery ages, the overall correspondence between the OCV curve of the battery and its state of charge (SOC) will not change, but only manifests as the gradual disappearance of the high SOC voltage platform. Therefore, by interpolating and calculating the open circuit voltage (OCV) at different states of charge (SOC) and different states of health (SOH), the open circuit voltage (OCV) of the battery under all SOC and SOH battery states can be obtained. The three-dimensional mechanism surface of the OCV-SOC-SOH interpolation result is as shown in Figure 4 the partial OCV parameters. Among them, the SOC estimation method is the ampere-hour integration method.

[0069] In addition, for the RC link in the single-cell model, since its value mainly depends on the diffusion rate and particle radius of the battery material itself, and both are mainly affected by the loss of active material, so in the conventional aging stage of the battery, the change value of the RC link with the loss of battery capacity can be ignored.

[0070] Based on the above mechanism analysis and data characterization of battery capacity loss and its impact on the single-cell model parameters, a single-cell battery aging trajectory model is constructed using simulation software. On the basis of the single-cell battery aging trajectory model, the embodiment of the present invention takes into account the inconsistent factors of the model parameters within the battery pack and builds a battery pack aging trajectory model, and its specific form is as shown in Figure 4 . It takes the battery model parameters (R o , R d , C d , θ1, θ2, θ3, α1, α2) and their distribution as inputs, continuously calculates the current and voltage of each single cell at different times under the overall framework of the battery pack, and on this basis, uses the mechanism relationship established between each model parameter and the capacity loss amount to realize the real-time update of the capacity and parameter values of all single-cell batteries. Furthermore, by combining the monitoring of the extreme voltage within the group (which is the prior art) and the continuous recursive calculation of the loss of active lithium to measure the SOH, the reciprocating charge and discharge simulation of the overall model is completed, and finally a battery pack aging trajectory model is established.

[0071] By using the above modeling method to model the battery packs with typical structures respectively, the aging trajectory models of all typical structures of battery packs are obtained.

[0072] Step (2.3): Generate a training set based on the aging trajectory model of the typical structure

[0073] Under the given current rate and temperature conditions, simulate the aging trajectory models of all typical structures of the battery packs, obtain the number of cycles when the capacity of the typical structure battery pack decays to 80% of its rated capacity, and generate a training set with a 1×4 vector describing the topological structure as the input and the number of cycles as the output.

[0074] Step (3): Train a neural network with the training set, and then predict the cycle life of any topological structure through the trained neural network

[0075] Train the neural network for predicting the cycle life of the series-then-parallel topological structure and the neural network for predicting the cycle life of the parallel-then-series topological structure respectively based on the obtained training set.

[0076] Under the given rated voltage, rated capacity of the battery pack and battery array conditions of the design requirements, list all possible topological structures by the permutation and combination method, describe the above all possible topological structures by a 1×4 vector respectively, and input the described topological structure vectors into the trained neural network for predicting the cycle life of the series-then-parallel topological structure and the neural network for predicting the cycle life of the parallel-then-series topological structure respectively to predict the cycle life of the topological structure battery pack. Since the number of training samples is small and the single regression prediction error is large, to reduce the regression prediction error, the median of 100 prediction results is taken as the final prediction result in the present invention; in the embodiment of the present invention, that is, obtain the predicted values of the number of cycles of the 2-parallel-6-series and 6-series-2-parallel topological structure battery packs.

[0077] To discuss the accuracy of the battery pack aging trajectory model modeling method mentioned in step (2), in the embodiment of the present invention, construct the battery pack models of the 2-parallel-6-series and 6-series-2-parallel topological structures by the battery pack aging trajectory model modeling method mentioned in step (2), perform simulation according to the conditions and parameters described in the embodiment of the present invention, and obtain the simulation values of the target topological cycle number.

[0078] Conduct a cyclic aging life experiment on the non-typical topological structures (2-parallel-6-series and 6-series-2-parallel) under the working conditions described in the embodiment of the present invention, and obtain the measured values of the cyclic aging cycle number of the non-typical topological structures under the working conditions.

[0079] Compare the predicted values, simulation values and measured values, and the comparison results are as Figure 5 shown. It can be seen that the relative errors of the prediction method and the modeling and simulation method proposed in the present invention are both controlled within 5%, verifying the effectiveness of the method for predicting the cycle life of any topological structure proposed in the present invention in predicting the cycle life of any topological structure. Through processes such as typical structure simulation and neural network prediction, the present invention can accurately predict the cycle life of any topological structure, which is beneficial to maximizing the benefits of the energy storage system.

[0080] In summary, the method proposed in the embodiments of the present invention constructs a typical battery pack cycle life simulation model by using a lumped parameter model for recursive calculation of capacity loss, and uses the simulation results for neural network training to reduce the workload of constructing the topology structure model. Based on the trained network, the cycle life of all possible topology structures under the target battery array is predicted, thereby realizing the calibration of the optimal topology structure. Compared with the prior art, the method proposed in the present invention has high efficiency, high prediction accuracy, and strong universality of the topology cycle life derivation method.

[0081] The above embodiments are the preferred embodiments of the present invention, but the present invention is not limited to the above embodiments. Without departing from the essence of the present invention, any obvious improvements, substitutions or variations that those skilled in the art can make all fall within the protection scope of the present invention.

Claims

1. A method for predicting the cycle life of any topology of a battery pack, characterized in that: Construct a BP neural network for predicting the cycle life of a series - then - parallel topology of the battery pack and a BP neural network for predicting the cycle life of a parallel - then - series topology of the battery pack. The input of the BP neural network is: a 1×4 vector composed of the number of series and parallel connections in the battery pack topology, and the output of the BP neural network is: the cycle life index; Simulate the aging trajectory models of all typical structures of the battery pack to obtain the number of cycles when the capacity of the typical - structure battery pack decays to 80% of its rated capacity, and generate a training set with a 1×4 vector describing the topology as the input and the number of cycles as the output. The training set is used to train the BP neural network for predicting the cycle life of the series - then - parallel topology and the BP neural network for predicting the cycle life of the parallel - then - series topology; For a battery pack with given design requirements, input all possible topologies of the battery pack into the trained BP neural network for predicting the cycle life of the series - then - parallel topology and the trained BP neural network for predicting the cycle life of the parallel - then - series topology respectively to predict the cycle life of the topology battery pack; The aging trajectory model is obtained through the following method: The battery model parameters and their distribution are respectively input into the aging trajectory models of each single - cell battery. Continuously calculate the current and voltage of each single - cell battery at different times, use the relationship between each model parameter and the capacity loss amount to update the capacity and model parameter values of all single - cell batteries in real - time, combine the monitoring of the extreme voltage in the battery pack and the continuous recursive calculation of the state of health (SOH) based on the loss of active lithium, complete the reciprocating charge - discharge simulation of the overall model, and finally establish the aging trajectory model of the battery pack; The aging trajectory model of the single - cell battery is based on the analysis of the battery capacity loss and its influence on the single - cell model parameters. The single - cell model parameters include the increase in battery internal resistance and the open - circuit voltage.

2. The method for predicting the cycle life of any topological structure of the battery pack according to claim 1, characterized in that The relationship between the battery capacity loss and the increase in battery internal resistance is as follows: where R o is the ohmic internal resistance of the battery, α1 and α2 are model parameters to be determined through experiments, C loss is the battery capacity loss, and θ1, θ2, and θ3 are model coefficients to be determined through experiments, T k , T std are the working temperature and the standard working temperature respectively, Δt chg,k is the actual charging time between two monitors, and k represents the k-th moment.

3. The method for predicting the cycle life derivation of any topology of the battery pack according to claim 2, wherein The said Δt chg,k is linearly related to specifically as follows: where E a,SEI is the activation energy for the diffusion of the solvent in the SEI, R is the gas constant, represents the standard charging time between two monitors.

4. The method for predicting the cycle life of any topology of the battery pack according to claim 1, characterized in that The relationship between the open - circuit voltage and the battery capacity loss is: Interpolation calculation is performed on the open - circuit voltage (OCV) under different states of charge (SOC) and different states of health (SOH).

5. The method for predicting the cycle life derivation of any topological structure of the battery pack according to claim 2, wherein The continuous recursive calculation of the SOH based on the loss of active lithium is specifically as follows: Among them, Q is the initial rated capacity of the battery, and SOH k is the state of health of the battery.

6. The method for predicting the cycle life of any topological structure of a battery pack according to claim 1, characterized in that The typical structures of the battery pack include a series - then - parallel structure, a parallel - then - series structure, and a hybrid structure combining the two.

7. The method for predicting the cycle life derivation of any topological structure of the battery pack according to claim 1, characterized in that, The cycle life index is the number of cycles when the capacity of the battery pack decays to 80% of its rated capacity.

8. The method for predicting the cycle life derivation of any topological structure of the battery pack according to claim 1, characterized in that The 1×4 vector composed of the number of series and parallel connections specifically is: For the 1×4 vector of the series - then - parallel sequential topology: [Number of series connections from single - cell to module, Number of parallel connections from single - cell to module, Number of series connections from module to overall, Number of parallel connections from module to overall]; For the 1×4 vector of the parallel - then - series sequential topology: [Number of parallel connections from single - cell to module, Number of series connections from single - cell to module, Number of parallel connections from module to overall, Number of series connections from module to overall]; If there is no series or parallel connection from module to overall in the topology, the values of the third and fourth positions of the vector, namely the number of series connections from module to overall or the number of parallel connections from module to overall, are default values of 1.

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