Battery capacity regeneration capacity short cycle prediction method and system

Abstract

The application provides a battery capacity regeneration capacity short cycle prediction method and system. The method is as follows: obtaining capacity data sets of multiple reference batteries, constructing capacity regeneration domain degradation models of the reference batteries; obtaining the attenuation factor of each capacity regeneration of each reference battery and the capacity before each capacity regeneration, and constructing a degradation parameter database corresponding to the attenuation factor and the capacity before the capacity regeneration; obtaining the attenuation factor of the current capacity regeneration of the battery to be predicted and the capacity before the current capacity regeneration; then, according to the degradation parameter database, the capacity regeneration domain degradation model of the battery to be predicted is selected from the capacity regeneration domain degradation models of the reference batteries; and the capacity regeneration domain of the battery to be predicted is predicted by using the capacity regeneration domain degradation model of the battery to be predicted. The battery capacity regeneration capacity short cycle prediction method has good prediction effect, high accuracy and wide application prospect.

Description

A method and system for short-cycle capacity prediction after battery capacity regeneration Technical Field

[0001] This invention relates to the field of batteries, and specifically to a method and system for short-cycle capacity prediction after battery capacity regeneration. Background Technology

[0002] Estimating the capacity of power batteries for new energy vehicles is a necessary condition for estimating driving range and is also a core function of state estimation in battery management systems.

[0003] Estimating battery capacity based on factors such as battery charging capacity, discharge characteristics, and internal resistance is a common method. However, due to the high degree of randomness in car owners' driving and the capacity regeneration characteristics of power batteries, it is difficult to estimate the capacity of vehicles that have been idle for many days, and existing methods are ineffective.

[0004] Furthermore, since the capacity declines rapidly in the short term after battery capacity is regenerated, and the different stages and magnitudes of capacity regeneration will affect the capacity decline pattern after regeneration. Summary of the Invention

[0005] In order to overcome the defects existing in the prior art, the purpose of this invention is to provide a method and system for short-cycle prediction of battery capacity after capacity regeneration.

[0006] To achieve the above-mentioned objectives of the present invention, the present invention provides a method for short-cycle capacity prediction after battery capacity regeneration, comprising the following steps:

[0007] Obtain the capacity dataset of multiple reference batteries of the same model as the battery to be predicted, and construct a capacity regeneration domain degradation model for each reference battery based on the capacity dataset; obtain the degradation factor of each reference battery at each capacity regeneration and the capacity before each capacity regeneration, and construct a degradation parameter database with a one-to-one correspondence between the degradation factor and the capacity before capacity regeneration according to capacity regeneration.

[0008] Based on the capacity data of the battery to be predicted, obtain the degradation factor when the current capacity of the battery to be predicted is regenerated, as well as the capacity before the current capacity is regenerated.

[0009] Based on the degradation factor during the current capacity regeneration of the battery to be predicted, the capacity before the current capacity regeneration, and the degradation parameter database, the capacity regeneration domain degradation model of the battery to be predicted is selected from the capacity regeneration domain degradation models of each reference battery.

[0010] This capacity regeneration domain degradation model is used to predict the capacity regeneration domain of the battery under test in a short-term cycle.

[0011] The method for short-cycle capacity prediction after battery capacity regeneration has good prediction effect and high accuracy, and has broad application prospects.

[0012] In one alternative scheme of the short-cycle capacity prediction method after battery capacity regeneration, the steps for constructing a capacity regeneration domain degradation model for the reference battery are as follows:

[0013] Identify the capacity regeneration and capacity regeneration domain of each reference cell in the capacity dataset;

[0014] The difference between the capacity at the time of capacity regeneration and the capacity before capacity regeneration of each reference battery in the capacity dataset is taken as the capacity regeneration amplitude. The capacity regeneration degradation sequence of each reference battery is constructed by the capacity regeneration amplitude corresponding to each capacity regeneration domain.

[0015] Capacity regeneration degradation sequences of each reference cell are fitted separately to construct capacity regeneration domain degradation models for each reference cell.

[0016] The alternative approach constructs a capacity regeneration domain decay model with simple parameters and a mature algorithm, which improves the computational speed of the method.

[0017] Optionally, the capacity regeneration degradation sequence is fitted with f(x)=exp(α·k+β)+γ to construct a capacity regeneration domain degradation model for the reference battery, where α, β, and γ are model parameters, and k is the charge-discharge cycle; each reference battery corresponds to a set of model parameters α, β, and γ.

[0018] Optionally, the current charge / discharge cycle of the battery to be predicted can be substituted into the capacity regeneration domain degradation model of the battery to be predicted to perform short-cycle capacity prediction of the capacity regeneration domain of the battery to be predicted.

[0019] In one alternative scheme of the short-cycle capacity prediction method after battery capacity regeneration, the capacity sequence of each battery is defined as: C = [c1, c2, c3, ..., c n ], where c n Given the capacity of the nth charge / discharge cycle, calculate the decay factor for each charge / discharge cycle. The decay factor of the nth charge / discharge cycle is the ratio of the capacity of the nth charge / discharge cycle to the capacity of the (n-1)th charge / discharge cycle.

[0020] Filter all degradation factors of each battery that exceed the range of [μ-3σ, μ+3σ], where μ is the average of all degradation factors and σ is the standard deviation of μ. If any exist, identify that the battery has capacity regeneration. Calculate or read the degradation factor of the battery at each capacity regeneration and the capacity before each capacity regeneration.

[0021] For each capacity regeneration, the data from the capacity regeneration to the point where the capacity declines again to the level before regeneration is used as the capacity regeneration domain for that capacity regeneration.

[0022] This alternative solution can effectively and quickly extract the capacity regeneration amplitude and capacity degradation factor, and accurately identify the capacity regeneration domain.

[0023] In one option of the short-cycle capacity prediction method after battery capacity regeneration, the steps for selecting prediction model parameters are as follows:

[0024] Calculate the degradation factor of the battery under test when regenerating its current capacity, the capacity before regenerating its current capacity, and the comparison distance between the degradation factor and the capacity before regenerating its current capacity and all corresponding degradation factors and capacities in the degradation parameter database. Use the degradation factor and capacity before regenerating its current capacity corresponding to the smallest comparison distance in the degradation parameter database as the query parameter. Find the reference battery corresponding to the query parameter and use the capacity regeneration domain degradation model of the reference battery as the capacity regeneration domain degradation model of the battery under test.

[0025] This alternative approach allows for quick retrieval of query parameters, improving prediction speed.

[0026] Optionally, the comparison distance is calculated according to the formula. The calculation is performed, where d is the comparison distance and c is the pre-regeneration capacity from the degradation parameter database. c is the decay factor in the decay parameter database. d The current capacity of the battery to be predicted is the capacity before regeneration. The current degradation factor of the battery to be predicted.

[0027] The present invention also proposes a short-cycle capacity prediction system after battery capacity regeneration, including a data acquisition module, a processing module and a storage module, wherein the data acquisition module is connected to the processing module and the processing module and the storage module are communicatively connected.

[0028] The data acquisition module is used to acquire battery capacity data, which includes the generated capacity data of the battery to be predicted and the capacity dataset of batteries of the same model as the battery to be tested. The data acquisition module sends the acquired battery capacity data to the processing module. The storage module is used to store at least one executable instruction. The executable instruction causes the processing module to perform the operation corresponding to the short-cycle capacity prediction method after battery capacity regeneration as described above, based on the battery capacity data.

[0029] The beneficial effects of this invention are: this invention neither predicts / estimates the state of charge (SOC) nor estimates the current cycle capacity, but rather predicts the abnormally decaying capacity during the capacity regeneration phase, that is, from the start of capacity regeneration, it predicts the capacity for the next few cycles within the capacity regeneration domain, and the prediction results can support the estimation of the state of charge (SOC) and the prediction of the remaining lifetime (RUL) for the next few cycles.

[0030] This invention achieves short-cycle prediction of battery capacity after regeneration by extracting the impact of capacity regeneration amplitude and capacity regeneration stage on the capacity decay after regeneration. This solves the problem of poor capacity estimation after power battery capacity regeneration and improves the accuracy of capacity estimation. The algorithm is mature and highly feasible. With the continuous increase in the number of electric vehicle power batteries, it has broad application prospects.

[0031] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0032] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0033] Figure 1 is a flowchart of this method;

[0034] Figure 2 is a schematic diagram of capacity regeneration identification based on the 3σ principle;

[0035] Figure 3 is a schematic diagram of the capacity regeneration domain;

[0036] Figure 4 is a schematic diagram of selecting a decay model based on distance. Detailed Implementation

[0037] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0038] In the description of this invention, unless otherwise specified and limited, it should be noted that the terms "installation", "connection" and "linking" should be interpreted broadly. For example, they can refer to mechanical or electrical connections, or internal connections between two components. They can be direct connections or indirect connections through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms according to the specific circumstances.

[0039] As shown in Figure 1, this invention provides a method for short-cycle capacity prediction after battery capacity regeneration, comprising the following steps:

[0040] Obtain a capacity dataset of multiple reference batteries of the same model as the battery under test. The capacity data of each reference battery in the capacity dataset includes all its charge and discharge cycles and the capacity of each charge and discharge cycle. This data can be obtained from the pre-market testing database of the battery.

[0041] Based on this capacity dataset, a capacity regeneration domain degradation model and a degradation parameter database for each reference battery are constructed.

[0042] In this embodiment, the capacity regeneration and capacity regeneration domain of each reference cell in the capacity dataset are first identified. Specifically:

[0043] Define the capacity sequence of each reference cell: C = [c1, c2, c3, ..., c n ], where c n For the capacity of the nth charge / discharge cycle, calculate the degradation factor for each charge / discharge cycle. The decay factor of the nth charge-discharge cycle is the ratio of the capacity of the nth charge-discharge cycle to the capacity of the (n-1)th charge-discharge cycle, i.e.

[0044] All degradation factors of each battery were screened based on the 3σ principle. Attenuation factor outside the range [μ-3σ, μ+3σ] Where μ is all attenuation factors The average value, σ is the standard deviation of μ, if such a decay factor exists. It is then assumed that capacity regeneration has been identified. The degradation factor of the battery during each capacity regeneration and the capacity before each capacity regeneration are calculated or read. The data of each battery from capacity regeneration to capacity degradation back to the level before regeneration is taken as the capacity regeneration domain, as shown in Figure 2. This can be obtained directly from the capacity data in the capacity dataset.

[0045] The capacity regeneration domain of each reference battery in the capacity dataset is extracted. The difference between the capacity at the time of capacity regeneration and the capacity before capacity regeneration of each reference battery in the capacity dataset is taken as the capacity regeneration amplitude, as shown in Figure 3. The capacity regeneration degradation sequence of each reference battery is constructed by the capacity regeneration amplitude corresponding to each capacity regeneration domain of each reference battery.

[0046] Capacity regeneration degradation sequences of each reference cell are fitted separately to construct capacity regeneration domain degradation models for each reference cell.

[0047] The degradation factor of each reference cell during each capacity regeneration and the capacity before each capacity regeneration are calculated or read, and a degradation parameter database is constructed according to the capacity regeneration, with a one-to-one correspondence between the degradation factor and the capacity before capacity regeneration.

[0048] In this embodiment, the capacity regeneration degradation sequence is fitted with f(x)=exp(α·k+β)+γ to construct a capacity regeneration domain degradation model, where α, β, and γ are model parameters. After fitting, the model parameters α, β, and γ are determined, and k is the charge and discharge cycle. Each reference battery corresponds to a set of model parameters α, β, and γ.

[0049] Then, based on the capacity data of the battery to be predicted, the degradation factor during the regeneration of the current capacity of the battery to be predicted and the capacity before the current capacity is regenerated are obtained.

[0050] The identification method is the same as the method described above for obtaining the attenuation factor during capacity regeneration and the capacity before capacity regeneration of each reference battery. The only difference is that the target is the battery to be predicted. The capacity value of each charge-discharge cycle in the capacity sequence of the battery to be predicted is obtained from the capacity data already generated by the battery to be predicted.

[0051] Then, based on the degradation factor during the current capacity regeneration of the battery to be predicted, the capacity before the current capacity regeneration, and the degradation parameter database, the capacity regeneration domain degradation model of the battery to be predicted is selected from the capacity regeneration domain degradation models of each reference battery.

[0052] Specifically, in this embodiment, the degradation factor of the battery to be predicted during current capacity regeneration is calculated, and the comparison distance between the current capacity before regeneration and all corresponding degradation factors and capacities before capacity regeneration in the degradation parameter database is calculated. The formula for calculating the comparison distance in this embodiment is as follows: Where c represents the pre-regeneration capacity in the decay parameter database. c is the decay factor in the decay parameter database. d The capacity to be predicted is the capacity before regeneration from the current capacity of the battery. As shown in Figure 4, the minimum comparison distance is selected. The degradation factor and the capacity before capacity regeneration in the degradation parameter database corresponding to the minimum comparison distance are used as query parameters. The reference battery corresponding to the query parameter is found, and the capacity regeneration domain degradation model of the reference battery is used as the capacity regeneration domain degradation model of the battery to be predicted.

[0053] By substituting the current charge / discharge cycle k of the battery to be predicted into the capacity regeneration domain degradation model of the battery to be predicted, the short-cycle capacity prediction of the capacity regeneration domain of the battery to be predicted can be achieved.

[0054] The present invention also provides an embodiment of a short-cycle capacity prediction system after battery capacity regeneration. In this embodiment, the system includes a data acquisition module, a processing module, and a storage module. The data acquisition module is connected to the processing module, and the processing module and the storage module are communicatively connected.

[0055] The data acquisition module is used to acquire battery capacity data, which includes the generated capacity data of the battery to be predicted and the capacity dataset of batteries of the same model as the battery to be tested. The data acquisition module sends the acquired battery capacity data to the processing module. The storage module is used to store at least one executable instruction. The executable instruction causes the processing module to perform the operation corresponding to the above-described short-cycle capacity prediction method after battery capacity regeneration based on the battery capacity data, thereby realizing the short-cycle prediction of the capacity after battery capacity regeneration.

[0056] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0057] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for short-cycle capacity prediction after battery capacity regeneration, characterized in that, Includes the following steps: Obtain capacity datasets of multiple reference batteries of the same model as the battery to be predicted, and construct capacity regeneration domain degradation models for each reference battery based on these datasets. The steps for constructing the capacity regeneration domain degradation models for the reference batteries are as follows: Identify the capacity regeneration and capacity regeneration domain of each reference battery in the capacity dataset, specifically: Define the capacity sequence of each battery: C=[c1,c2,c3,……,c n ], where c n To determine the capacity of the nth charge-discharge cycle, calculate the degradation factor for each charge-discharge cycle. The degradation factor for the nth charge-discharge cycle is the ratio of the capacity of the nth charge-discharge cycle to the capacity of the (n-1)th charge-discharge cycle. For each battery, filter all degradation factors that exceed... The attenuation factor for the interval, where μ is the average of all attenuation factors. If the standard deviation of μ is present, it indicates that the battery is undergoing capacity regeneration. The degradation factor during each capacity regeneration and the capacity before each regeneration are calculated or read. For each capacity regeneration, the data from capacity regeneration to the point where capacity decays again back to its original value is taken as the capacity regeneration domain for that regeneration. The difference between the capacity during regeneration and the capacity before regeneration for each reference battery in the capacity dataset is taken as the capacity regeneration amplitude. The capacity regeneration amplitude corresponding to each capacity regeneration domain of each reference battery is used to construct the capacity regeneration degradation sequence for each reference battery. The capacity regeneration degradation sequence for each reference battery is fitted to construct the capacity regeneration domain degradation model for each reference battery. The process involves: obtaining the degradation factor and capacity before each capacity regeneration for each reference battery; constructing a degradation parameter database with a one-to-one correspondence between the degradation factor and the capacity before capacity regeneration; obtaining the degradation factor and capacity before current capacity regeneration for the battery to be predicted based on the capacity data already generated by the battery to be predicted; selecting the capacity regeneration domain degradation model for the battery to be predicted from the capacity regeneration domain degradation models of each reference battery based on the degradation factor, capacity before current capacity regeneration, and degradation parameter database; and using this capacity regeneration domain degradation model to perform short-cycle capacity prediction of the capacity regeneration domain of the battery to be predicted.

2. The method for short-cycle capacity prediction after battery capacity regeneration according to claim 1, characterized in that, A capacity regeneration domain degradation model for a reference battery is constructed by fitting the capacity regeneration degradation sequence with f(x)=exp(α·k+β)+γ, where α, β, and γ are model parameters and k is the charge-discharge cycle; each reference battery corresponds to a set of model parameters α, β, and γ.

3. The method for short-cycle capacity prediction after battery capacity regeneration according to claim 1, characterized in that, The steps for selecting prediction model parameters are as follows: calculate the degradation factor of the battery under test when the current capacity is regenerated, the comparison distance between the current capacity before regeneration and all the corresponding degradation factors and capacities before regeneration in the degradation parameter database, take the degradation factor and capacities before regeneration in the degradation parameter database corresponding to the smallest comparison distance as query parameters, find the reference battery corresponding to the query parameter, and take the capacity regeneration domain degradation model of the reference battery as the capacity regeneration domain degradation model of the battery under test.

4. The method for short-cycle capacity prediction after battery capacity regeneration according to claim 3, characterized in that, The comparison distance is calculated according to the formula. The calculation is performed, where d is the comparison distance and c is the pre-regeneration capacity from the degradation parameter database. c is the decay factor in the decay parameter database. d The current capacity of the battery to be predicted is the capacity before regeneration. The current degradation factor of the battery to be predicted.

5. The method for short-cycle capacity prediction after battery capacity regeneration according to claim 2, characterized in that, The current charge / discharge cycle of the battery to be predicted is substituted into the capacity regeneration domain degradation model of the battery to be predicted, and the capacity regeneration domain of the battery to be predicted is predicted in a short-cycle manner.

6. A short-cycle capacity prediction system after battery capacity regeneration, characterized in that, The system includes a data acquisition module, a processing module, and a storage module. The data acquisition module is connected to the processing module, and the processing module and the storage module are communicatively connected. The data acquisition module is used to acquire battery capacity data, which includes the generated capacity data of the battery to be predicted and a capacity dataset of batteries of the same model as the battery to be tested. The data acquisition module sends the acquired battery capacity data to the processing module. The storage module is used to store at least one executable instruction, which causes the processing module to perform the operation corresponding to the short-cycle capacity prediction method after battery capacity regeneration as described in any one of claims 1-5 based on the battery capacity data.

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