CN117388708A - A power battery system and a power battery system thermal runaway monitoring method - Google Patents

Abstract

The invention belongs to the technical field of power batteries, in particular to a power battery system and a thermal runaway monitoring method of the power battery system, which comprises a power battery service model accurate construction end, a power battery service data processing research end and a power battery cyclic aging decay end; the accurate construction end of the service model of the power battery comprises a power battery multi-scale mapping model construction module and a power battery multi-scale digital twin module; the dynamic evolution and coupling action mechanism of multiple physical field parameters in the power battery service period digital twin modeling theory is disclosed, and the power battery cyclic aging decay mechanism under the coupling action of multiple influencing factors is clarified; and a multi-scale performance degradation rule of the power battery under the action of single inconsistency is explored, and a set of active remanufacturing time domain decision method and theory oriented to the service cycle of the power battery are formed, so that the maximization of the remanufacturing production benefit of the power battery is realized.

Description

A power battery system and a power battery system thermal runaway monitoring method

Technical field

The invention belongs to the technical field of power batteries, and specifically relates to a power battery system and a thermal runaway monitoring method of the power battery system.

Background technique

Power batteries are the power sources that provide power for tools, mostly referring to batteries that provide power for electric cars, electric trains, and electric bicycles; the discharge time of lithium iron batteries can be about 6 times that of alkaline manganese batteries, and compared with nickel metal hydride batteries , its discharge voltage is stable and its storage time has significant advantages.

The aging and decay process of power batteries is affected by multiple physical field parameters such as electrochemical field, temperature field and stress field, and the coupling relationship between multiple physical field parameters is dynamic under the cyclic changes of dynamic charging and discharging, constant charging and standing. Evolution causes the power battery capacitance degradation rate to change suddenly in stages, making it difficult to analyze the power battery cycle aging degradation mechanism.

In the current existing technology, the aging and decline process of power batteries is affected by multiple physical field parameters such as electrochemical field, temperature field and stress field, and the coupling relationship between multiple physical field parameters is in three types: dynamic charging and discharging, constant charging and standing. The dynamic evolution under cyclic changes of working conditions causes the power battery capacitance degradation rate to change suddenly in stages, making it difficult to analyze the power battery cycle aging and degradation mechanism. Here we provide a power battery system and a power battery system thermal runaway monitoring method.

Contents of the invention

In order to solve the above problems, the present invention proposes a power battery system and a power battery system thermal runaway monitoring method.

The technical solution adopted by the present invention to solve the technical problem is: a power battery system according to the present invention, including a power battery service model accurate construction end, a power battery service data processing research end and a power battery cycle aging degradation end; The accurate construction end of the power battery service model includes the power battery multi-scale image model building module and the power battery multi-scale digital twin module.

Preferably, the power battery multi-scale image model building module monitors apparent battery characteristics, such as voltage, temperature, and capacity.

Preferably, the accurate construction end of the power battery service model needs to adopt a multi-scale, multi-physics coupling modeling method to achieve all-round abstraction and modeling of the complex system of the power battery.

Preferably, in the process of using the power battery multi-scale image model, the power battery multi-scale digital twin module will inevitably encounter changes in model parameters with changes in application conditions or environment, and a data-driven method is used to perform real-time monitoring data. Information extraction to improve modeling accuracy.

Preferably, the power battery service data processing research end is that the power battery twin data consists of physical layer monitoring data and virtual layer simulation data, which has the characteristics of massiveness, timing, strong correlation and high coupling.

Preferably, the cycle aging and degradation end of the power battery is a deductive reasoning process by constructing an equivalent circuit model, obtaining model characteristic parameters, verifying parameter errors and optimizing the cycle optimization of the equivalent circuit model.

Preferably, the power battery service data processing research end includes a power battery twin data quality assessment module and a power battery twin data recovery model module.

A power battery system thermal runaway monitoring method, suitable for a power battery system described in claim 7, including the following preparation steps:

S1. Accurately build a digital twin model of the power battery service cycle through the accurate construction of the power battery service model, providing a basic model for the study of the cycle aging and decay mechanism of the power battery;

S2. Through the power battery multi-scale digital twin module, according to the timing dependence characteristics and model dependence characteristics of the power battery twin data, and based on the feature fusion theory, the defect repair and data cleaning methods of the battery data are studied to improve the performance of the power battery produced under multi-feature working conditions. Twin data quality;

S3. Comprehensive and in-depth analysis of the multi-scale structure of the power battery from the micro particle level, single body level, module level to the whole package level through the cyclic aging and degradation end of the power battery, and improve the relationship between the equivalent circuit model and the physical entity of the power battery. Adaptability and elucidate the cycle aging and degradation mechanism of power batteries.

The beneficial effects of the present invention are:

The invention provides a power battery system and a power battery system thermal runaway monitoring method, a power battery service cycle digital twin modeling theory, and a power battery service cycle twin data processing method; and reveals the dynamics of multi-physical field parameters in the power battery cycle aging and decay process. Evolution and coupling mechanism, elucidate the cyclic aging and degradation mechanism of power batteries under the coupling effect of multiple influencing factors; explore the multi-scale performance degradation rules of power batteries under the effect of monomer inconsistency, and form a set of active remanufacturing time domains oriented to the service life of power batteries. Decision-making methods and theories to maximize the efficiency of power battery remanufacturing production.

Description of the drawings

The drawings described here are used to provide a further understanding of the present invention and constitute a part of this application. The illustrative embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the attached picture:

Figure 1 is a structural block diagram of the present invention;

Figure 2 is a flow chart of the method of the present invention;

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

Please refer to Figures 1-2. The present invention provides a power battery system, which includes a power battery service model accurate construction end, a power battery service data processing research end and a power battery cycle aging degradation end; the power battery service model accurate construction end It includes a power battery multi-scale image model building module and a power battery multi-scale digital twin module.

Further, the power battery multi-scale image model building module monitors the apparent characteristics of the battery, such as voltage, temperature, and capacity.

When working, the power battery multi-scale image model building module comprehensively analyzes the all-element parameter information of the power battery in the coupled state of multi-physics fields such as electrochemical field, temperature field, stress field, from particles to electrodes, monomers, modules, and entire components. Starting from multi-scale, Bao et al. constructed a multi-scale image model of the power battery, and deeply analyzed the parameter transfer and interaction rules between different physical fields and different-level models of the power battery. On the basis of the multi-scale image model, a data-driven model was added to establish a power battery. The battery digital twin model provides a basic model for the study of the cycle aging and degradation mechanism of power batteries.

Furthermore, the accurate construction of the power battery service model requires the use of multi-scale, multi-physics coupling modeling methods to achieve all-round abstraction and modeling of the complex system of power batteries.

When working, the accurate construction end of the power battery service model is based on the low integrity and high noise twin data generated by the power battery under the multi-characteristic working conditions cycle of its service cycle. By analyzing the timing dependence characteristics in the twin data time series, a method is designed A data quality assessment model based on deep learning algorithms. Based on the timing dependence characteristics and model dependence characteristics of power battery twin data, and based on feature fusion theory, the defect repair and data cleaning methods of battery data are studied to improve the performance of power batteries produced under multi-feature working conditions. Twin data quality.

Furthermore, in the process of using the multi-scale image model of the power battery, the power battery multi-scale digital twin module will inevitably encounter changes in model parameters with changes in application conditions or environment. A data-driven method is used to perform real-time monitoring data. Information extraction to improve modeling accuracy.

When working, the power battery multi-scale digital twin module will inevitably encounter problems such as model parameters changing with changes in application conditions or environment when using the power battery multi-scale image model. A single model is very difficult to solve. It is difficult to deeply mine the collected data and update the model parameters in real time. The multi-scale image model of the power battery contains many partial differential equations and requires a large number of parameters to be input for calculation. Some internal battery parameters cannot be accurately obtained in real time. In order to make the battery Digital twin modeling has higher accuracy. A data-driven model is added to the multi-scale digital twin modeling of the power battery. Data-driven methods are used to extract information from real-time monitoring data, and the extracted data is supplemented into the multi-scale imaging model. , build a multi-scale digital twin model of the power battery to improve modeling accuracy.

Furthermore, the power battery service data processing research end is that the power battery twin data consists of physical layer monitoring data and virtual layer simulation data, which has the characteristics of massiveness, timing, strong correlation and high coupling.

At work, the research end of power battery service data processing is that power battery twin data consists of physical layer monitoring data and virtual layer simulation data. It has the characteristics of massiveness, timing, strong correlation and high coupling, and often has data redundancy. The problem of excess and unevenness has caused many difficulties in the collection, storage, simulation and application of power battery twin data. In order to achieve high-precision expression of power battery twin data, this project builds a power battery twin data time series feature extraction model based on a deep recurrent neural network, uses a denoising autoencoder to extract battery model features from the twin data set, and builds a power battery model based on feature fusion theory. The battery twin data recovery model realizes the repair and cleaning of defective data and improves data quality under complex working conditions.

Furthermore, the cycle aging and degradation end of the power battery is a deductive reasoning process by constructing an equivalent circuit model, obtaining model characteristic parameters, verifying parameter errors and optimizing the cycle optimization of the equivalent circuit model.

During operation, the cyclic aging and degradation end of the power battery is a deductive reasoning process by constructing an equivalent circuit model, obtaining model characteristic parameters, verifying parameter errors and optimizing the cycle optimization of the equivalent circuit model, simulating the power battery from the microscopic particle level to the single body level. , the multi-scale structure from the module level to the whole package level from the inside to the outside, perform model verification and optimization based on the twin data of the power battery, and establish a multi-scale equivalent circuit model of the power battery.

Further, the power battery service data processing research end includes a power battery twin data quality assessment module and a power battery twin data recovery model module.

At work, the research end of power battery service data processing is that power battery twin data consists of physical layer monitoring data and virtual layer simulation data. It has the characteristics of massiveness, timing, strong correlation and high coupling, and often has data redundancy. The problem of uneven residual sums has caused many difficulties in the collection, storage, simulation and application of power battery twin data. In order to achieve high-precision expression of power battery twin data, this project builds a power battery twin data time series based on a deep recurrent neural network. Feature extraction model uses denoising autoencoders to extract battery model features from the twin data set. Based on feature fusion theory, a power battery twin data recovery model is constructed to repair and clean defective data and improve data quality under complex working conditions. ; The power battery twin data quality assessment module extracts external feature variables of monitoring data and simulation data from the power battery twin data as input vectors of the Deep-RNN model; the power battery twin data recovery model module Deep-LSTM model can Extracting and analyzing temporal features in battery data time series is not sufficient for data recovery. Taking into account the timing features and model features in the battery data, a power battery data recovery model is established based on the feature fusion method, and the trained Deep-LSTM is used to extract the time features of the battery external feature data; among them, the front-end part of the network serves as the encoder to extract Model features use a fully connected layer as the decoder to reconstruct and repair bad samples, and use noise-free normal battery data as the output to train the data recovery model.

A power battery system thermal runaway monitoring method, suitable for a power battery system described in claim 7, including the following preparation steps:

S1. Accurately build a digital twin model of the power battery service cycle through the accurate construction of the power battery service model, providing a basic model for the study of the cycle aging and decay mechanism of the power battery;

S2. Through the power battery multi-scale digital twin module, according to the timing dependence characteristics and model dependence characteristics of the power battery twin data, and based on the feature fusion theory, the defect repair and data cleaning methods of the battery data are studied to improve the performance of the power battery produced under multi-feature working conditions. Twin data quality;

S3. Comprehensive and in-depth analysis of the multi-scale structure of the power battery from the micro particle level, single body level, module level to the whole package level through the cyclic aging and degradation end of the power battery, and improve the relationship between the equivalent circuit model and the physical entity of the power battery. Adaptability and elucidate the cycle aging and degradation mechanism of power batteries.

Working principle: The power battery service cycle digital twin model is accurately constructed through the accurate construction of the power battery service model, providing a basic model for the study of the power battery cycle aging and decay mechanism; the power battery multi-scale digital twin module is used according to the time series dependence characteristics of the power battery twin data. and model-dependent features, based on feature fusion theory to study defect repair and data cleaning methods of battery data to improve the quality of power battery twin data generated under multi-feature working conditions; comprehensively and in-depth analysis of the power battery's microscopic The multi-scale structure from the inside to the outside from the particle level, single body level, module level to the whole package level improves the fit between the equivalent circuit model and the physical entity of the power battery, and elucidates the cycle aging and decay mechanism of the power battery.

The basic principles, main features and advantages of the present invention have been shown and described above. Those skilled in the industry should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions only illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will also have other aspects. Various changes and modifications are possible, which fall within the scope of the claimed invention.

Claims (8)

1. A power battery system, characterized by: including a power battery service model accurate construction end, a power battery service data processing research end and a power battery cycle aging degradation end; the power battery service model accurate construction end includes a power battery multi-scale Image model building module and power battery multi-scale digital twin module. 2. A power battery system according to claim 1, characterized in that: the power battery multi-scale image model building module monitors the apparent characteristics of the battery, such as voltage, temperature, and capacity. 3. A power battery system according to claim 2, characterized in that: the accurate construction end of the power battery service model needs to adopt a multi-scale, multi-physics coupling modeling method to realize the complex system of the power battery. Full range of abstraction and modeling. 4. A power battery system according to claim 3, characterized in that: in the process of using the power battery multi-scale image model of the power battery multi-scale digital twin module, it is difficult to avoid encountering model parameters that vary with application conditions. Or changes due to environmental changes, use data-driven methods to extract information from real-time monitoring data to improve modeling accuracy. 5. A power battery system according to claim 4, characterized in that: the power battery service data processing research end is a power battery twin data composed of physical layer monitoring data and virtual layer simulation data, which is massive. , timing, strong correlation and high coupling characteristics. 6. A power battery system according to claim 5, characterized in that: the cycle aging degradation end of the power battery is determined by constructing an equivalent circuit model, obtaining model characteristic parameters, verifying parameter errors and optimizing the equivalent circuit model. Deductive reasoning process for loop optimization. 7. A power battery system according to claim 6, characterized in that: the power battery service data processing research end includes a power battery twin data quality assessment module and a power battery twin data recovery model module. 8. A power battery system thermal runaway monitoring method, suitable for a power battery system as claimed in claim 7, characterized in that it includes the following preparation steps: S1. Accurately build a digital twin model of the power battery service cycle through the accurate construction of the power battery service model, providing a basic model for the study of the cycle aging and decay mechanism of the power battery; S2. Through the power battery multi-scale digital twin module, according to the timing dependence characteristics and model dependence characteristics of the power battery twin data, and based on the feature fusion theory, the defect repair and data cleaning methods of the battery data are studied to improve the performance of the power battery produced under multi-feature working conditions. Twin data quality; S3. Comprehensive and in-depth analysis of the multi-scale structure of the power battery from the micro particle level, single body level, module level to the whole package level through the cyclic aging and degradation end of the power battery, and improve the relationship between the equivalent circuit model and the physical entity of the power battery. Adaptability and elucidate the cycle aging and degradation mechanism of power batteries.