Dynamic charging optimization method and system for lithium battery

Abstract

The invention discloses a dynamic charging optimization method and system for a lithium battery, and relates to the technical field of lithium battery charging. A dynamic charging optimization system for a lithium battery comprises an initial charging strategy setting module and a dynamic charging strategy optimization module. By combining the charging use data and the battery consumption data of the specific user, a personalized charging strategy can be generated according to the charging behavior, the environment change and the battery state of each user, and the optimization process not only ensures the charging efficiency, but also considers the long-term health of the battery and prolongs the service life of the battery; according to the method, a plurality of charging environments are considered, so that the charging strategy can be adjusted according to the change of different environments; through comprehensive consideration of environmental parameters and battery performance, the charging strategy can keep efficient and safe charging in various environments, and has strong adaptability.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium battery charging, and in particular to a dynamic charging optimization method and system for lithium batteries. Background Art

[0002] Existing technical solutions for lithium battery charging optimization typically rely on static, fixed charging strategies, without fully considering the dynamic changes in the charging environment and real-time feedback on the battery status. This approach often leads to inaccurate initial charging strategy settings and an inability to adapt to different charging conditions and the usage needs of different users. In addition, existing solutions do not fully consider factors such as lithium battery expansion, gas release, or battery noise during the charging process, and fail to adjust the charging strategy in a timely manner, which can easily lead to battery overheating, excessive consumption, or even damage during the charging process, affecting the battery's service life and safety.

[0003] Therefore, traditional methods have obvious deficiencies in adaptability and charging efficiency in complex environments and cannot meet the growing demand for intelligent battery management. Summary of the Invention

[0004] The present invention aims to provide a dynamic charging optimization method and system for lithium batteries, which is triggered from the perspective of adapting to different charging environments and explores specific universal lithium battery charging strategies.

[0005] A dynamic charging optimization method for a lithium battery comprises the following steps: Repeated rapid charge and discharge tests were performed on several groups of lithium batteries under different charging environments. Test data from the rapid charge and discharge tests was collected to obtain several dynamic charging triples. The dynamic charging triples consisted of charging environment, extreme current segment, and auxiliary information. The extreme fault period was defined as the period from M seconds before a fault occurred to the time the fault occurred and N seconds after the fault occurred during the rapid charge and discharge tests. The charging environment was the specific charging environment information during the rapid charge and discharge tests; the extreme current segment was the current range during the extreme fault period; and the auxiliary information included the gas phase information sequence, acoustic information sequence, and lithium battery expansion force sequence during the extreme fault period. Identify safe current boundaries and auxiliary safety boundaries based on several dynamic charging triples; A dynamic charging optimization model is established based on the dynamic charging triplet, safety current boundary and auxiliary safety boundary, and the initial lithium battery charging strategy is output; For a specific user, the initial lithium battery charging strategy of the specific user is optimized based on the dynamic charging optimization model to generate a specific lithium battery charging strategy; the specific lithium battery charging strategy is optimized based on the charging usage data and battery consumption data of the specific user.

[0006] As a preferred technical solution of the present invention, the specific steps for identifying the safe current boundary based on a number of dynamic charging triples include: Extract and identify the waveform changes of the troughs and peaks of the extreme current segments in all dynamic charging triples to obtain several sets of extreme current peak value sets; perform function fitting based on the several sets of extreme current peak value sets to obtain an extreme current fitting function; divide according to the extreme current fitting function and the several sets of extreme current peak value sets, and divide the extreme current peak values higher than the extreme current fitting function into the extreme current high peak value set, and divide the extreme current peak values lower than the extreme current fitting function into the extreme current low peak value set; Extract the extreme current high peak extreme value based on the extreme current high peak value set; extract the extreme current low peak extreme value based on the extreme current low peak value set; delimit the stable safe current range according to the extreme current high peak extreme value and the extreme current low peak extreme value; In the dynamic charging triples in the same charging environment, perform cluster analysis on all corresponding extreme current segments to obtain K sets of environmental extreme current value sets and the corresponding environmental extreme current value cluster centers; select the environmental safe current range based on all environmental extreme current value cluster centers and the quartile method; Traverse all charging environments for analysis to obtain the corresponding environmental safe current ranges under different charging environments; Combine the stable safe current range and the corresponding environmental safe current ranges under different charging environments to obtain the safe current boundary.

[0007] As a preferred technical solution of the present invention, the specific steps for identifying the auxiliary safety boundary based on a number of dynamic charging triples include: Extract the correlation of the auxiliary information in all dynamic charging triples based on the stable safe current range to obtain the relevant stable auxiliary information threshold; Extract the correlation of the auxiliary information in all dynamic charging triples based on the environmental safe current ranges corresponding to different charging environments to obtain the relevant environmental auxiliary information threshold; Identify the abnormal auxiliary information sequence segment when the extreme current segment in the dynamic charging triple is normal but the auxiliary information is abnormal; perform feature analysis based on the abnormal auxiliary information sequence segment and the corresponding charging environment to obtain the environment-abnormal auxiliary information threshold; Combine the relevant stable auxiliary information threshold, the relevant environmental auxiliary information threshold and the environment-abnormal auxiliary information threshold to obtain the auxiliary safety boundary.

[0008] As a preferred technical solution of the present invention, the specific steps for establishing a dynamic charging optimization model based on dynamic charging triples, a safe current boundary and an auxiliary safety boundary and outputting an initial lithium battery charging strategy include: The dynamic charging optimization model includes a data input layer and a strategy optimization layer; The data input layer is used to extract the safe charging triplets of I groups of specific users under the initial lithium battery charging strategy; at the same time, extract the corresponding charging usage data and battery consumption data; the safe charging triplet consists of user charging environment - complete charging current data - complete charging auxiliary information; The strategy optimization layer is used to optimize the initial lithium battery charging strategy according to the safe charging triplets, charging usage data and battery consumption data to obtain a specific lithium battery charging strategy; The specific steps to determine the initial lithium battery charging strategy include: for different charging environments, within the range of the safe current boundary and the auxiliary safety boundary, set the highest safe charging rate as the initial lithium battery charging strategy for different charging environments.

[0009] As a preferred technical solution of the present invention, the specific steps to optimize the initial lithium battery charging strategy of a specific user based on the dynamic charging optimization model include: In the strategy optimization layer, the charging usage data includes the battery usage time of the user within I charging intervals; the battery consumption data includes the battery consumption range value of the user within I charging intervals; Use the swarm optimization algorithm to optimize the initial lithium battery charging strategy to obtain a specific lithium battery charging strategy.

[0010] As a preferred technical solution of the present invention, different charging environments include but are not limited to high temperature environment, low temperature environment, high humidity environment, low humidity environment, slow heating or cooling environment, fast heating or cooling environment, normal temperature closed environment, normal temperature ventilation environment, stable load environment, unstable load environment.

[0011] A dynamic charging optimization system for lithium batteries includes: Initial charging strategy setting module, including a test analysis unit and a model construction unit; the test analysis unit is used to repeatedly perform fast charge and discharge tests on several groups of lithium batteries under different charging environments, collect test data in the fast charge and discharge tests, and obtain several dynamic charging triples; the composition of the dynamic charging triple is charging environment - extreme current segment - auxiliary information; record the process from M seconds before the fault occurs, during the fault occurrence to N seconds after the fault occurs in the fast charge and discharge test of the lithium battery as the extreme fault period; wherein, the charging environment is the specific charging environment information in the fast charge and discharge test; the extreme current segment is the current range segment in the extreme fault period; the auxiliary information is the gas phase information sequence, acoustic information sequence and lithium battery expansion force sequence in the extreme fault period; identify the safe current boundary and auxiliary safety boundary based on several dynamic charging triples; the model construction unit is used to establish a dynamic charging optimization model based on the dynamic charging triples, safe current boundary and auxiliary safety boundary, and output the initial lithium battery charging strategy; Dynamic charging strategy optimization module, including a strategy optimization unit; the strategy optimization unit is used to optimize the initial lithium battery charging strategy of a specific user based on the dynamic charging optimization model for a specific user, and generate a specific lithium battery charging strategy; the specific lithium battery charging strategy is optimized based on the charging usage data and battery consumption data of the specific user.

[0012] The present invention has the following advantages: 1. By combining the charging usage data and battery consumption data of a specific user, the present invention can generate personalized charging strategies according to each user's charging behavior, environmental changes and battery status. This optimization process not only ensures the charging efficiency, but also takes into account the long-term health of the battery, extending the service life of the battery; the method considers multiple charging environments, enabling the charging strategy to be adjusted according to changes in different environments; by comprehensively considering environmental parameters and battery performance, the charging strategy can maintain efficient and safe charging in various environments, with strong adaptability.

[0013] 2. By identifying and demarcating the safe current boundary and auxiliary safety boundary, the method ensures that the battery will not exceed the safe range during the charging process, preventing battery damage or safety hazards caused by overcharging, overheating, etc.; at the same time, using an anomaly detection mechanism to timely discover and warn of potential fault risks, improving the safety of the battery; by providing specific charging strategies for each user and optimizing the charging process, users can not only enjoy more efficient charging services when charging, but also ensure the long-term health of the battery. This personalized service can enhance the user's charging experience and increase the user's trust in the charging device and battery management system. Description of the Drawings

[0014] Figure 1Schematic diagram of a dynamic charging optimization system for lithium batteries adopted in the embodiments of the present invention. Detailed implementation manners

[0015] In order to enable those skilled in the art to better understand the technical solutions in the present invention, 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.

[0016] Embodiment 1, A dynamic charging optimization method for lithium batteries, comprising the following steps: Repeatedly perform fast charge and discharge tests on several groups of lithium batteries under different charging environments, collect the test data in the fast charge and discharge tests, and obtain several dynamic charging triples; the composition of the dynamic charging triple is charging environment - extreme current segment - auxiliary information; record the process of the lithium battery from M seconds before the fault occurs, during the fault occurrence to N seconds after the fault occurs in the fast charge and discharge test as the extreme fault period; wherein, the charging environment is the specific charging environment information in the fast charge and discharge test; the extreme current segment is the current range segment in the extreme fault period; the auxiliary information is the gas phase information sequence, acoustic information sequence and lithium battery expansion force sequence in the extreme fault period; The fault here represents any fault situation and early warning fault situation. When a fault occurs or the conditions of the early warning fault situation are reached, immediately record and trace back the process from M seconds before the fault occurs, during the fault occurrence to N seconds after the fault occurs; the specific values of M and N are set manually by professional technicians; the early warning fault situation usually refers to the abnormal or abnormal state shown by the battery before a complete fault occurs; in order to ensure the safety of the battery, these abnormal situations can be identified in advance for early warning; The setting and monitoring of the early warning fault situation can be realized by real-time monitoring of multiple key indicators of the lithium battery, such as temperature, voltage, current, expansion force, gas concentration and acoustic signal, etc.; when any monitoring indicator exceeds the preset threshold, an early warning will be triggered in the fast charge and discharge test, indicating a possible fault risk; for example, when the battery temperature exceeds 45°C, the voltage is lower than 2.5V or higher than 4.3V, the expansion force exceeds the standard, the gas concentration increases abnormally or the battery makes abnormal noises, etc., all can be used as the triggering conditions for the early warning fault; when the early warning is triggered, the data from M seconds before the fault to N seconds after the fault will be traced back to help analyze the precursors and subsequent performances of the battery fault.

[0017] The different charging environments include but are not limited to high temperature environment, low temperature environment, high humidity environment, low humidity environment, slow heating or cooling environment, fast heating or cooling environment, normal temperature closed environment, normal temperature ventilation environment, stable load environment, unstable load environment; When performing repeated fast charge and discharge tests, it is necessary to ensure the accurate recording of the charging environment, which is directly related to the performance of the battery under different environmental conditions; during the test, multiple charging environments are selected to simulate different actual working conditions.

[0018] For different charging environments, the following details are provided: The high-temperature environment is usually when the temperature exceeds 35°C. In this case, the high temperature of the lithium battery may cause the internal chemical reaction of the battery to accelerate, increasing the risks of expansion, leakage, and thermal runaway; the low-temperature environment is usually when the temperature is below 5°C. In a low-temperature environment, the viscosity of the electrolyte of the lithium battery increases, and the lithium-ion conductivity is poor, which may lead to a decrease in the battery charging efficiency and even lithium metal deposition in extreme cases, resulting in a short circuit; the high-humidity environment is usually when the humidity exceeds 80%. Excessive humidity may cause the battery to get damp inside, leading to battery short circuits, internal corrosion, and even battery case rupture; the low-humidity environment is usually when the humidity is too low, which may cause the evaporation of the battery electrolyte, increasing the internal resistance of the battery and affecting the charge and discharge performance; in a slow heating or cooling environment, the slow temperature change usually does not have an obvious direct impact on the battery, but long-term exposure to an environment with large temperature changes may affect the battery life; in a fast heating or cooling environment, the rapid temperature change is likely to cause problems such as battery expansion and explosion, especially under conditions of extremely large temperature differences; in a normal-temperature closed environment, the temperature is usually about 20°C. The closed environment may cause gas accumulation, which may lead to an increase in internal pressure during extreme failure periods, resulting in the rupture of the outer shell; in a normal-temperature ventilated environment, it is an ideal environment that can effectively remove heat, but excessive ventilation will cause uneven air flow, increasing the possibility of local overheating; in a stable load environment, it means that the charging current is stable, which can avoid overcharging or over-discharging of the battery caused by fluctuating current and reduce battery damage; in an unstable load environment, the load fluctuates greatly, which may cause overcharging or over-discharging of the battery, increasing the probability of battery failure.

[0019] When performing fast charge and discharge tests, the following steps must be followed: Use a precision charger to charge the lithium battery under the set charging environment, record data such as the current, voltage, and temperature of the battery, and monitor the charging status of the battery in real time, especially whether the battery enters the high-voltage or excessive charging current area; during the charge and discharge process, monitor whether the battery shows abnormal current fluctuations or temperature increases to warn of possible battery failures; during the fast charge and discharge test process, record and save the following auxiliary information: The gas phase information sequence is the data of the internal gas change of the battery recorded by the gas sensor. Abnormal gas concentrations can be used as early warnings of battery failures; the acoustic information sequence is the sound change of the battery during charge and discharge recorded by the acoustic sensor. If there is expansion, foreign object friction, etc. inside the battery, it usually generates noise; the expansion force sequence monitors the expansion of the lithium battery through a force sensor. An increase in the expansion force usually means an increase in the internal pressure of the battery, which may lead to explosion or leakage.

[0020] Identify the safe current boundary and the auxiliary safety boundary based on a number of dynamic charging triples; The specific steps for identifying the safe current boundary based on a number of dynamic charging triples include: Extract and identify the waveform changes of the troughs and peaks of the extreme current segments in all dynamic charging triples to obtain several sets of extreme current peak sets; Extract each extreme current segment from all dynamic charging triples and focus on analyzing the waveform changes of its troughs and peaks; The extreme current segment refers to the current range during a charging process when a fault occurs or is approaching, and the waveform changes of these troughs and peaks usually reflect the stress state of the battery; The extraction method is to use a peak detection algorithm (such as peak filtering, local maximum search) to identify extreme current peaks within each period of the waveform; Perform function fitting based on several sets of extreme current peak sets to obtain an extreme current fitting function; Perform function fitting on the extracted extreme current peak sets to obtain the fitting function of the extreme current; The goal of this step is to describe the law of current change through a mathematical model; Methods such as polynomial fitting, exponential fitting, and piecewise linear fitting can be used. Select a suitable fitting method based on actual data (such as the least squares method) to obtain the fitting function; This function should match the current peak set as much as possible and reflect the extreme current behavior of the battery under different charging environments; Based on the extreme current fitting function and several sets of extreme current peak sets, divide the extreme current peaks with values higher than the extreme current fitting function into the extreme current high peak set, and divide the extreme current peaks with values lower than the extreme current fitting function into the extreme current low peak set; Compare the extreme current peaks with the fitting function and divide the extreme current peaks into "high peaks" or "low peaks" according to the value of the fitting function; This division helps to identify the boundary regions of battery performance in subsequent analysis, and thus define the current ranges for normal and abnormal operations; Extract the extreme current high peak extreme value based on the extreme current high peak set; The extracted extreme current high peak extreme value can reflect the maximum current intensity that the battery may withstand in an extreme fault situation, thus providing data support for battery safety analysis; Extract the extreme current low peak extreme value based on the extreme current low peak set; The extreme current low peak extreme value reflects the minimum current of the battery under the most unfavorable conditions, thus helping to define the lower bound of the safe current range; Define the stable safe current range according to the extreme current high peak extreme value and the extreme current low peak extreme value; Define the stable safe current range of the battery according to the extracted high peak extreme value and low peak extreme value. This range is the current range that the battery can withstand under all charging environmental conditions; In a dynamic charging triple under the same charging environment, clustering analysis is performed on all corresponding extreme current segments to obtain K sets of environmental extreme current values and their corresponding clustering centers of environmental extreme current values; the DBSCAN clustering algorithm is used to analyze the current data under different charging environments and calculate the clustering centers of extreme current values in each environment; clustering analysis can identify the current patterns under different charging environments, which helps to establish the corresponding current safety boundaries for different environments; Based on all the clustering centers of environmental extreme current values and the quartile method, select the environmental safe current range; use the quartile method to select the environmental safe current range from the clustering centers of environmental extreme current values. First, calculate the quartiles of the clustering centers, and then define the safe current range according to the distribution of the quartiles. This method can flexibly adjust the safe current range according to the distribution of the current data; through the quartile method, the safe current range of the current can be accurately selected under different environmental conditions to ensure the safe operation of the battery in extreme environments; Traverse all charging environments for analysis to obtain the corresponding environmental safe current ranges under different charging environments; traverse all charging environments and perform the above steps to obtain the safe current range for each environment; Combine the stable safe current range and the corresponding environmental safe current ranges under different charging environments to obtain the current safety boundary; the obtained current safety boundary can ensure that the battery maintains stable and safe performance in all possible working environments, thereby improving the service life and safety of the battery.

[0021] By analyzing the extreme current segment data extracted from the dynamic charging triple, the safe current boundary of the lithium battery is identified and delimited; through multiple steps of processing, from the extraction of extreme current peaks to the clustering of environmental extreme current values, and then to the combination of safe current ranges in different environments, a comprehensive safe current boundary is finally formed; this method can effectively predict and control the charging state of the battery and prevent failures caused by abnormal current during actual use.

[0022] The specific steps for identifying the auxiliary safety boundary based on several dynamic charging triples include: Extract the correlation of the auxiliary information in all dynamic charging triples based on the stable and safe current range to obtain the relevant stable auxiliary information threshold. This step aims to extract the relevant features in the auxiliary information through the stable and safe current range, and then provide a theoretical basis for the subsequent identification of the auxiliary safety boundary. For the auxiliary information in all dynamic charging triples, first screen out those charging triples within the stable and safe current range, and calculate the correlation between the auxiliary information in these charging triples and the stable and safe current range. Correlation calculation methods can use the Pearson correlation coefficient, Spearman rank correlation coefficient, etc. to evaluate the relationship between current changes and auxiliary information. This step can quantify the change pattern of the auxiliary information within the stable current range, thus providing a benchmark and standard for subsequent anomaly detection and helping to determine the normal range of the auxiliary information. Extract the correlation of the auxiliary information in all dynamic charging triples based on the environmental safety current range corresponding to different charging environments to obtain the relevant environmental auxiliary information threshold. This step extracts the correlation between the auxiliary information and the charging environment based on the safety current range under different charging environments to further define the safety boundary. For each charging environment, use the corresponding environmental safety current range to screen out all dynamic charging triples. Among these charging triples, extract the correlation between each auxiliary information sequence (gas phase, acoustics, expansion force, etc.) and the safety current range in its corresponding environment. Similarly, use correlation analysis methods (such as the Pearson correlation coefficient, mutual information, etc.) to calculate the correlation between the auxiliary information and the charging environment current range to obtain the relevant environmental auxiliary information threshold. This step can help understand how the auxiliary information interacts with the current range change under different charging environments and provide data support for the auxiliary safety boundary specific to the environment. Identify the abnormal auxiliary information sequence segment when the extreme current segment in the dynamic charging triple is normal but the auxiliary information is abnormal. Based on the abnormal auxiliary information sequence segment and the corresponding charging environment, conduct feature analysis to obtain the environment-abnormal auxiliary information threshold. The purpose of this step is to identify the sequence segment in the extreme current segment where, although the current is still within the normal range, the auxiliary information shows abnormalities. This is usually a manifestation before the battery has a potential failure and belongs to a warning signal. By analyzing the extreme current segments in all dynamic charging triples, identify those sequence segments where the current change is within the stable range but the auxiliary information has abnormal changes. The specific steps include: identifying the extreme current segments where the current is within the normal range; in these segments, the abnormal change parts of the corresponding auxiliary information (such as a sharp change in gas phase concentration, a sudden increase in expansion force, etc.) will be extracted to form an abnormal auxiliary information sequence segment. By capturing these abnormal auxiliary information sequence segments, potential problems that may occur in the battery can be predicted in advance, potential failure risks can be identified, and thus a basis can be provided for subsequent safety protection measures. For each abnormal auxiliary information sequence segment, extract corresponding features according to the charging environment it belongs to; determine the change pattern when the auxiliary information is abnormal under a specific charging environment through statistical analysis (such as mean, variance, skewness, kurtosis, etc.), so as to obtain the environment-abnormal auxiliary information threshold; Based on the combination of the relevant stable auxiliary information threshold, the relevant environmental auxiliary information threshold, and the environment-abnormal auxiliary information threshold, obtain the auxiliary safety boundary; under different charging environments, by comprehensively considering the above three thresholds, find the safe range of the auxiliary information. Through this step, a comprehensive safety boundary applicable to different charging environments can be obtained, ensuring that the auxiliary information of the battery under various working conditions can be monitored in real time, so as to early warn of potential safety risks.

[0023] Establish a dynamic charging optimization model based on the dynamic charging triple, the safe current boundary, and the auxiliary safety boundary, and output the initial lithium battery charging strategy; The specific steps include: The dynamic charging optimization model includes a data input layer and a strategy optimization layer; The data input layer is used to extract the safe charging triples of I groups of specific users under the initial lithium battery charging strategy; at the same time, extract the corresponding charging usage data and battery consumption data; the safe charging triple consists of user charging environment-complete charging current data-complete charging auxiliary information; The strategy optimization layer is used to optimize the initial lithium battery charging strategy according to the safe charging triples, charging usage data, and battery consumption data to obtain a specific lithium battery charging strategy; The specific steps to determine the initial lithium battery charging strategy include: for different charging environments, within the range of the safe current boundary and the auxiliary safety boundary, set the highest safe charging rate as the initial lithium battery charging strategy under different charging environments; The dynamic charging optimization model consists of two main levels: the data input layer and the strategy optimization layer; these two levels cooperate together to extract the input data and optimize the charging strategy; the data input layer is responsible for extracting and processing the input data to ensure the efficiency of the subsequent optimization process; the strategy optimization layer adjusts the initial charging strategy according to the input data and the optimization goal to enable the battery to achieve the best charging efficiency and safety under different charging environments; The main task of the data input layer is to extract the secure charging triples and other relevant data of a specific user, which will serve as the basis for subsequent policy optimization; according to the charging environments and battery usage of different users, extract the secure charging triples involved in the charging process; each secure charging triple includes the following parts: the user charging environment is used to describe the environmental parameters when the user is currently charging; the complete charging current data includes all current data during the charging process, especially the current values within the secure current boundary and the auxiliary secure boundary; the complete charging auxiliary information represents the auxiliary information recorded during the charging process. I represents the data collected during I charging intervals experienced by a specific user. One charging interval represents all the data before the previous charging to all the data before the next charging. The setting of the initial lithium battery charging policy is based on the requirements of the charging environment, the secure current boundary, and the auxiliary secure boundary, ensuring that the current, temperature, and other indicators during the charging process of any user remain within the safe range, regardless of the specific charging habits and lithium battery usage habits of the user. Different charging environments will affect the charging performance of the battery. Therefore, it is necessary to set the charging policy according to the characteristics of different environments; set the maximum charging rate according to the safe current range and auxiliary information of the battery; this means that during the charging process, the charging current will not exceed the secure current boundary, and the auxiliary information (such as gas concentration, expansion force, etc.) also remains within the safe range; the setting of the initial charging policy should not only consider the charging efficiency (i.e., charging as fast as possible), but also ensure that the battery will not overheat or be damaged under high current; therefore, the maximum charging rate needs to be set on the premise of ensuring the safety of the battery.

[0024] The setting of the initial lithium battery charging policy does not depend on the charging habits or lithium battery usage habits of a specific user, but is optimized according to the charging environment, the secure current boundary, and the auxiliary secure boundary. This setting based on environmental and battery safety standards can adapt to different users and environmental conditions, ensuring the safety of battery charging in various usage scenarios; since the setting of the charging policy is based on a widely applicable safe current range and auxiliary secure boundary, it can adapt to a variety of charging environments (such as high temperature, low temperature, high humidity, low humidity, etc.). Therefore, this policy is applicable to most users, regardless of their specific charging needs or environments; by ensuring that the current, temperature, and other indicators during the charging process remain within the safe range, this policy can widely meet the basic charging needs of the vast majority of users, while avoiding battery damage or safety problems caused by overcharging or overheating.

[0025] For a specific user, optimize the initial lithium battery charging policy of the specific user based on the dynamic charging optimization model to generate a specific lithium battery charging policy; the specific lithium battery charging policy is optimized based on the charging usage data and battery consumption data of the specific user. The specific steps for optimizing the initial lithium battery charging strategy for a specific user based on a dynamic charging optimization model include: In the policy optimization layer, the charging usage data contains the battery usage time of the user within I charging intervals; the battery consumption data contains the battery consumption range values of the user within I charging intervals. Use a swarm optimization algorithm to optimize the initial lithium battery charging strategy to obtain a specific lithium battery charging strategy. The optimization objective for optimizing the initial lithium battery charging strategy is: to find the charging strategy that best suits the user's usage data and battery consumption data. Specific steps: Construct several charging optimization individuals, and build a charging optimization population based on several charging optimization individuals; Extract the characteristics of the charging usage data and battery consumption data to obtain the user charging usage data characteristics and user battery consumption data characteristics; By extracting these characteristics, a comprehensive understanding of the user's charging habits and battery consumption patterns can be obtained, providing data support for the subsequent optimization process and ensuring that the optimization algorithm adjusts the charging strategy according to the actual usage situation; Based on the user charging usage data characteristics and user battery consumption data characteristics, combined with the initial lithium battery charging strategy, set the charging optimization individuals. In each charging optimization individual, there is an improved lithium battery charging strategy; Specifically, the charging strategy in the charging optimization individual will be adjusted according to the user's charging time, charging frequency, battery consumption pattern, etc.; Manually set the maximum number of iterations; Define a fitness function to evaluate the performance of the charging strategy of each charging optimization individual; The fitness function is usually designed according to the optimization objective, such as maximizing the charging efficiency, minimizing the battery loss, avoiding battery overheating, etc.; For example: The strategy can be evaluated by calculating the balance value of the battery charging efficiency and battery life, or the strategy can be optimized by maximizing the matching degree of the charging time and battery consumption range; Select the most excellent individuals according to the fitness function values, and these individuals will be retained in the subsequent iterations; During the iteration process, perform population update, use the crossover operation to combine the strategies of two or more individuals to generate new individuals; Crossover can be achieved by randomly selecting some parameters for exchange or combination to explore the charging strategy space; Use the mutation operation to slightly adjust the charging strategies of some individuals to increase the diversity of the population and avoid the optimization process falling into a local optimum; After each generation of optimization, re-evaluate the fitness of each individual in the population and adjust the population structure according to the fitness values; Through multiple generations of iteration process, the charging strategies of the individuals in the population will gradually approach the optimal strategy; The optimization algorithm will continuously adjust the charging strategies of the individuals until the charging scheme that best suits the user's needs is found.

[0026] By combining the charging usage data and battery consumption data of specific users, personalized charging strategies can be generated according to each user's charging behavior, environmental changes, and battery status. This optimization process not only ensures charging efficiency but also takes into account the long-term health of the battery, extending its service life. The method considers multiple charging environments, enabling the charging strategy to be adjusted according to changes in different environments. By comprehensively considering environmental parameters and battery performance, the charging strategy can maintain efficient and safe charging in various environments, showing strong adaptability. By identifying and demarcating the safe current boundary and auxiliary safety boundary, the method ensures that the battery does not exceed the safe range during charging, preventing battery damage or safety hazards caused by overcharging, overheating, etc. At the same time, using an anomaly detection mechanism, potential fault risks are promptly discovered and warned, improving battery safety. By providing specific charging strategies for each user and optimizing the charging process, users can not only enjoy more efficient charging services when charging but also ensure the long-term health of the battery. This personalized service can enhance the user's charging experience and increase the user's trust in the charging device and battery management system.

[0027] Embodiment 2, a dynamic charging optimization system for lithium batteries, see Figure 1 as shown, including: An initial charging strategy setting module, including a test analysis unit and a model construction unit; the test analysis unit is used to repeatedly perform fast charge and discharge tests on several groups of lithium batteries in different charging environments, collect test data in the fast charge and discharge tests, and obtain several dynamic charging triples; the composition of the dynamic charging triple is charging environment - extreme current segment - auxiliary information; record the process from M seconds before the fault occurs, during the fault occurrence to N seconds after the fault occurs in the fast charge and discharge test of the lithium battery as the extreme fault period; among them, the charging environment is the specific charging environment information in the fast charge and discharge test; the extreme current segment is the current range segment during the extreme fault period; the auxiliary information is the gas phase information sequence, acoustic information sequence, and lithium battery expansion force sequence during the extreme fault period; identify the safe current boundary and auxiliary safety boundary based on several dynamic charging triples; the model construction unit is used to establish a dynamic charging optimization model based on the dynamic charging triples, safe current boundary, and auxiliary safety boundary, and output the initial lithium battery charging strategy; A dynamic charging strategy optimization module, including a strategy optimization unit; the strategy optimization unit is used to optimize the initial lithium battery charging strategy of a specific user based on the dynamic charging optimization model for a specific user, and generate a specific lithium battery charging strategy; the specific lithium battery charging strategy is optimized based on the charging usage data and battery consumption data of the specific user.

[0028] It should be understood that those of ordinary skill in the art can make improvements or transformations based on the above description, and all such improvements and transformations shall fall within the protection scope of the appended claims of the present invention. The parts not described in detail in this specification belong to the prior art well-known to those of ordinary skill in the art.

Claims

1. A dynamic charging optimization method for lithium batteries, characterized in that, The steps include: Repeatedly perform fast charge and discharge tests on several groups of lithium batteries under different charging environments, collect the test data in the fast charge and discharge tests to obtain several dynamic charging triples; the composition of the dynamic charging triple is charging environment - extreme current segment - auxiliary information; record the process of the lithium battery from M seconds before the fault occurs, during the fault occurrence to N seconds after the fault occurrence in the fast charge and discharge test as the extreme fault period; among them, the charging environment is the specific charging environment information in the fast charge and discharge test; the extreme current segment is the current range segment in the extreme fault period; the auxiliary information is the gas phase information sequence, acoustic information sequence and lithium battery expansion force sequence in the extreme fault period; Identify the safe current boundary and auxiliary safety boundary based on several dynamic charging triples; Establish a dynamic charging optimization model based on the dynamic charging triples, safe current boundary and auxiliary safety boundary, and output the initial lithium battery charging strategy; For a specific user, optimize the initial lithium battery charging strategy of the specific user based on the dynamic charging optimization model to generate a specific lithium battery charging strategy; the specific lithium battery charging strategy is optimized based on the charging usage data and battery consumption data of the specific user.

2. The dynamic charging optimization method for a lithium battery according to claim 1, wherein The specific steps for identifying the safe current boundary based on several dynamic charging triples include: Extract and identify the waveform changes of the troughs and peaks of the extreme current segments in all dynamic charging triples to obtain several sets of extreme current peak value sets; perform function fitting based on several sets of extreme current peak value sets to obtain an extreme current fitting function; divide according to the extreme current fitting function and several sets of extreme current peak value sets, and divide the extreme current peak values higher than the extreme current fitting function into the extreme current high peak value set, and divide the extreme current peak values lower than the extreme current fitting function into the extreme current low peak value set; Extract the extreme current high peak extreme value based on the extreme current high peak value set; extract the extreme current low peak extreme value based on the extreme current low peak value set; delimit the stable safe current range according to the extreme current high peak extreme value and the extreme current low peak extreme value; In the dynamic charging triples under the same charging environment, perform clustering analysis on all corresponding extreme current segments to obtain K environmental extreme current value sets and corresponding environmental extreme current value clustering centers; select the environmental safe current range based on all environmental extreme current value clustering centers and the quartile method; Traverse all charging environments for analysis to obtain the corresponding environmental safe current ranges under different charging environments; Combine the stable safe current range and the corresponding environmental safe current ranges under different charging environments to obtain the safe current boundary.

3. The dynamic charging optimization method for a lithium battery according to claim 2, wherein, The specific steps for identifying the auxiliary safety boundary based on several dynamic charging triples include: Extract the correlation of the auxiliary information in all dynamic charging triples based on the stable safe current range to obtain the relevant stable auxiliary information threshold; Extract the correlation of the auxiliary information in all dynamic charging triples based on the environmental safe current ranges corresponding to different charging environments to obtain the relevant environmental auxiliary information threshold; Identify the abnormal auxiliary information sequence segment when the extreme current segment in the dynamic charging triple is normal but the auxiliary information is abnormal; based on the abnormal auxiliary information sequence segment and the corresponding charging environment, perform feature analysis to obtain the environment-abnormal auxiliary information threshold. Based on the combination of the relevant stable auxiliary information threshold, the relevant environmental auxiliary information threshold, and the environment-abnormal auxiliary information threshold, obtain the auxiliary safety boundary.

4. The dynamic charging optimization method for a lithium battery according to claim 3, characterized in that, The specific steps for establishing a dynamic charging optimization model based on the dynamic charging triple, the safety current boundary, and the auxiliary safety boundary and outputting the initial lithium battery charging strategy include: The dynamic charging optimization model includes a data input layer and a strategy optimization layer. The data input layer is used to extract the safe charging triples of I groups of specific users under the initial lithium battery charging strategy; at the same time, extract the corresponding charging usage data and battery consumption data; the safe charging triple consists of user charging environment-complete charging current data-complete charging auxiliary information. The strategy optimization layer is used to optimize the initial lithium battery charging strategy according to the safe charging triples, the charging usage data, and the battery consumption data to obtain a specific lithium battery charging strategy. The specific steps for determining the initial lithium battery charging strategy include: for different charging environments, within the range of the safe current boundary and the auxiliary safety boundary, set the highest safe charging rate as the initial lithium battery charging strategy for different charging environments.

5. A dynamic charging optimization method for a lithium battery according to claim 4, characterized in that The specific steps for optimizing the initial lithium battery charging strategy of a specific user based on the dynamic charging optimization model include: In the strategy optimization layer, the charging usage data includes the battery usage time of the user within I charging intervals; the battery consumption data includes the battery consumption range value of the user within I charging intervals. Use the swarm optimization algorithm to optimize the initial lithium battery charging strategy to obtain a specific lithium battery charging strategy.

6. The dynamic charging optimization method for a lithium battery according to claim 5, characterized in that Different charging environments include but are not limited to high-temperature environment, low-temperature environment, high-humidity environment, low-humidity environment, slow heating or cooling environment, fast heating or cooling environment, normal-temperature closed environment, normal-temperature ventilated environment, stable load environment, unstable load environment.

7. A dynamic charging optimization system for a lithium battery, characterized in that The system is a dynamic charging optimization method for a lithium battery as described in any one of the above claims 1-6, including: The initial charging strategy setting module includes a test analysis unit and a model construction unit; the test analysis unit is used to repeatedly perform fast charge and discharge tests on several groups of lithium batteries under different charging environments, collect test data in the fast charge and discharge tests, and obtain several dynamic charging triples; the composition of the dynamic charging triple is charging environment - extreme current segment - auxiliary information; record the process from M seconds before the fault occurs, the occurrence of the fault to N seconds after the fault occurs in the fast charge and discharge test as the extreme fault period; among them, the charging environment is the specific charging environment information in the fast charge and discharge test; the extreme current segment is the current range segment in the extreme fault period; the auxiliary information is the gas phase information sequence, acoustic information sequence and lithium battery expansion force sequence in the extreme fault period; identify the safe current boundary and auxiliary safety boundary based on several dynamic charging triples; the model construction unit is used to establish a dynamic charging optimization model based on the dynamic charging triples, safe current boundary and auxiliary safety boundary, and output the initial lithium battery charging strategy; The dynamic charging strategy optimization module includes a strategy optimization unit; the strategy optimization unit is used to optimize the initial lithium battery charging strategy of a specific user based on the dynamic charging optimization model for a specific user, and generate a specific lithium battery charging strategy; the specific lithium battery charging strategy is optimized based on the charging usage data and battery consumption data of the specific user.

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