A lithium battery working status monitoring and management method and system based on BMS

Abstract

The present invention relates to a lithium battery working state monitoring and management method and system based on BMS, belonging to the field of battery operation management technology, obtaining several groups of lithium battery working state parameters of lithium batteries in various charging and discharging plans, aligning and analyzing the several groups of lithium battery working state parameters in a radar chart distribution framework, and obtaining a lithium ion free dynamic matrix chain of lithium batteries in all charging and discharging plans; analyzing the hidden Markov chain model of the lithium ion free dynamic matrix chain based on the charging and discharging priority scheduling order of each battery group to change and optimize the internal resistance of the battery group; by calculating the capacity loss drift rate between the standard periodic capacity loss characteristics and the actual periodic capacity loss characteristics, the periodic capacity calibration strategy is optimized and managed based on the capacity loss drift rate. The present invention can further efficiently and reasonably monitor and manage the working state of lithium batteries based on BMS to improve the charging and discharging performance and safety of lithium batteries.

Description

Technical Field

[0001] The present invention relates to the technical field of battery operation management, and in particular to a method and system for monitoring and managing the working status of a lithium battery based on a BMS. Background Art

[0002] Lithium battery is a kind of rechargeable battery, which is favored by users because of its advantages such as being relatively light, high energy density, fast charging speed and no memory effect. With the advancement of technology, existing lithium batteries are designed with BMS battery management system to monitor and manage the charging and discharging behavior of lithium batteries. However, the current BMS has a slow assessment rate for the health status of lithium batteries, and there are certain assessment errors, which is not conducive to the subsequent management and optimization of lithium batteries by BMS. As the use time and frequency of lithium batteries increase, the motor materials, electrolytes, diaphragms and other materials inside them will change, which will cause the internal resistance and capacity of lithium batteries to decrease and change, resulting in faster heating rate, lower charging and discharging performance and shorter cycle life of lithium batteries, which is prone to lithium battery charging and discharging accidents and reduces the accuracy of BMS monitoring and management of lithium batteries. Summary of the invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a method and system for monitoring and managing the working status of a lithium battery based on a BMS.

[0004] To achieve the above object, the technical solution adopted by the present invention is:

[0005] The first aspect of the present invention provides a method for monitoring and managing the working status of a lithium battery based on a BMS, comprising the following steps:

[0006] Obtain several groups of lithium battery working state parameters of the lithium battery in each charge and discharge plan, build a radar chart distribution framework according to the preset lithium battery health status assessment model in the BMS, introduce the Hermite curve method to align and analyze the several groups of lithium battery working state parameters in the radar chart distribution framework, and obtain the lithium ion free dynamic matrix chain of the lithium battery in all charge and discharge plans;

[0007] Constructing a hidden Markov chain model of a lithium ion free dynamic matrix chain, analyzing the charging and discharging priority scheduling order of each battery pack based on the hidden Markov chain, and analyzing and optimizing the internal resistance change of the low balanced charge and discharge cycle battery pack in the pattern distribution diagram of all battery packs in the lithium battery according to the charging and discharging priority scheduling order, and obtaining a first lithium battery monitoring and management plan;

[0008] Acquire several lithium ion free dynamic matrix chains under the actual charge and discharge plan after executing the first lithium battery monitoring and management scheme to construct a standard periodic capacity loss characteristic-periodic capacity loss constant mapping table, calculate the capacity loss drift rate between the standard periodic capacity loss characteristic and the actual periodic capacity loss characteristic according to the mapping table, and optimize the periodic capacity calibration strategy based on the capacity loss drift rate to obtain the second lithium battery monitoring and management scheme;

[0009] Obtain the optimal charging mode control strategy of the charger for the lithium battery, run the optimal charging mode control strategy to obtain the charging rate variation amplitude of the lithium battery, and manage the thermal runaway emergency plan of the lithium battery and the charger and recommend the best charging wire product by analyzing the charging rate variation amplitude.

[0010] Further, in a preferred embodiment of the present invention, the method of obtaining several groups of lithium battery working state parameters of the lithium battery in each charge and discharge plan, constructing a radar chart distribution framework according to a preset lithium battery health status assessment model in the BMS, introducing the Hermite curve method to align and analyze the several groups of lithium battery working state parameters in the radar chart distribution framework, and obtaining the lithium ion free dynamic matrix chain of the lithium battery in all charge and discharge plans, specifically includes the following steps:

[0011] Preset the load object and several monitoring timestamps of the lithium battery, obtain the BMS management strategy of the lithium battery for the load object and different optimal charge and discharge plans formulated by the BMS management strategy, execute the BMS management strategy based on different optimal charge and discharge plans to perform charge and discharge monitoring on the lithium battery at several monitoring timestamps, and obtain several groups of lithium battery working status parameters for each charge and discharge plan;

[0012] Obtain a preset lithium battery health status assessment model in the BMS, extract assessment criteria through the preset lithium battery health status assessment model in the BMS, and construct a radar chart distribution architecture according to the assessment criteria;

[0013] Obtain N lithium battery health status assessment indicators through the assessment criteria, divide the radar chart distribution architecture into a plurality of sub-assessment indicator radar chart fields based on the N lithium battery health status assessment indicators, and obtain the charge and discharge safety benchmark threshold of each lithium battery health status assessment indicator;

[0014] The Hermite curve method is introduced to draw the curve area of ​​the charge and discharge safety benchmark threshold of each lithium battery health status assessment indicator, and the Hermite surface area of ​​each sub-assessment indicator radar chart is obtained. At the same time, several groups of lithium battery working state parameters corresponding to each charge and discharge plan are fitted in the Hermite curve method to obtain several Hermite curves for each charge and discharge plan;

[0015] Align several Hermite curves of each charging and discharging plan one by one to the Hermite surface area of ​​each sub-evaluation indicator radar chart, obtain several alignment rates of each charging and discharging plan, calculate the average value according to the several alignment rates of each charging and discharging plan, and obtain the average alignment rate under each charging and discharging plan;

[0016] The health status of the lithium battery is analyzed by the average registration rate under each charge and discharge plan to determine the lithium ion migration characteristics of each lithium battery working state parameter, and the lithium ion free dynamic matrix chain of the lithium battery in all charge and discharge plans is generated according to the lithium ion migration characteristics.

[0017] Further, in a preferred embodiment of the present invention, the health status of the lithium battery is analyzed by the average registration rate under each charge and discharge plan to determine the lithium ion migration characteristics of each lithium battery working state parameter, and the lithium ion free dynamic matrix chain of the lithium battery in all charge and discharge plans is generated according to the lithium ion migration characteristics, which specifically includes the following steps:

[0018] Connecting a plurality of monitoring time stamp nodes in time sequence to form a preset monitoring time period, and obtaining a determination frequency of a situation in which the average registration rate under each charging and discharging plan is greater than a preset average registration rate when the radar chart distribution architecture is in the preset monitoring time period;

[0019] If the determination frequency is greater than the preset determination frequency, it means that the health of the lithium battery is significantly reduced compared with the normal health. At this time, the electrode material information, electrolyte information, diaphragm pore size distribution and production process information of the lithium battery are obtained, and the circuit input parameters corresponding to each group of lithium battery working state parameters in each charge and discharge plan are obtained;

[0020] Constructing a three-dimensional model of a lithium battery according to the electrode material information, electrolyte information, diaphragm pore size distribution and production process information, and writing circuit input parameters corresponding to each group of lithium battery working state parameters into the three-dimensional model of the lithium battery to perform charge and discharge simulation;

[0021] During the simulation, the lithium ions in the three-dimensional model of the lithium battery are measured by a transmission electron microscope to obtain the lithium ion particle size and specific surface area generated under each group of lithium battery working state parameters in each charge and discharge plan, and the diffusion coefficient of the lithium ions in the electrode material corresponding to each group of lithium battery working state parameters in each charge and discharge plan is obtained in the big data network based on the lithium ion particle size, specific surface area and electrolyte information;

[0022] The diffusion distribution law of lithium ions in different electrode materials is obtained through the big data network, and the CANOPY clustering algorithm is introduced. M initial CANOPY clusters are created according to the diffusion distribution law of lithium ions in different electrode materials. The diffusion coefficients of lithium ions in electrode materials corresponding to the working state parameters of each lithium battery in each charge and discharge plan are allocated to the M initial CANOPY clusters, and the diffusion distribution law after clustering is output;

[0023] A lithium ion charge migration model and a lithium ion discharge migration model are constructed and outputted according to the diffusion distribution law after clustering, and the volume ratio of the lithium ion charge migration model and the lithium ion discharge migration model is calculated. The lithium ion migration characteristics of the working state parameters of each lithium battery are determined according to the volume ratio.

[0024] The lithium ion migration characteristics of each lithium battery working state parameter are defined as a matrix link unit. All matrix link units in each charge and discharge plan are dynamically planned through matrix chain multiplication to obtain the lithium ion free dynamic matrix chain of the lithium battery in all charge and discharge plans.

[0025] Further, in a preferred embodiment of the present invention, the hidden Markov chain model of the lithium ion free dynamic matrix chain is constructed, and the charging and discharging priority scheduling order of each battery pack is analyzed based on the hidden Markov chain, and the internal resistance change of the low balanced charge and discharge cycle battery pack is analyzed and optimized in the pattern distribution diagram of all battery packs in the lithium battery according to the charging and discharging priority scheduling order, so as to obtain the first lithium battery monitoring and management plan, which specifically includes the following steps:

[0026] Acquire a state set and an observation set of a lithium ion free dynamic matrix chain, determine the probability distribution and transition probability of each state in the state set according to the observation set, and construct a hidden Markov chain model of the lithium ion free dynamic matrix chain based on the probability distribution and transition probability of each state in the state set;

[0027] The maximum likelihood estimation algorithm is introduced to extract the state sequence of the hidden Markov chain model in the current lithium battery internal resistance to construct a state transition probability matrix, and the likelihood function of each state sequence in the state transition probability matrix is ​​calculated and maximized to obtain the state transition probability of the lithium ion free dynamic matrix chain in the current lithium battery internal resistance;

[0028] Determine whether the state transition probability is greater than the preset state transition probability. If so, it indicates that the internal resistance of the lithium battery may increase in the lithium ion free dynamic matrix chain, and obtain the pattern distribution diagram of all battery packs in the lithium battery;

[0029] Obtain the optimal charge and discharge cycle management strategy of the BMS for the lithium battery, and run the optimal charge and discharge cycle management strategy on the lithium battery. At this time, measure the temperature control value of each battery pack, and mark the battery pack with a temperature control value less than the preset temperature control value in the pattern distribution map, and define it as a low balanced charge and discharge cycle battery pack;

[0030] Preset a charge and discharge priority scheduling order threshold, obtain the charge and discharge priority scheduling order of each battery group in the lithium battery by the BMS, mark the battery group corresponding to the charge and discharge priority scheduling order greater than the charge and discharge priority scheduling order threshold in the pattern distribution map, and obtain the battery group pattern distribution area with a high priority scheduling order;

[0031] Obtaining the real-time scheduling frequency of each battery group when the lithium battery executes the optimal charge and discharge cycle management strategy, calculating the charge and discharge cycle scheduling weight of each battery group based on the real-time scheduling frequency, and extracting the charge and discharge cycle scheduling weight corresponding to the low balanced charge and discharge cycle battery group;

[0032] If the charge and discharge cycle scheduling weight corresponding to the low balanced charge and discharge cycle battery group is in the battery group pattern distribution area with a high priority scheduling order, the internal resistance change rate of this battery group at the preset time node is measured, and the optimal charge and discharge cycle management strategy of the BMS is optimized and managed in combination with the high priority scheduling order of the battery group pattern distribution area and the internal resistance change rate to obtain the first lithium battery monitoring and management plan.

[0033] Further, in a preferred embodiment of the present invention, the acquisition of several lithium ion free dynamic matrix chains under the actual charge and discharge plan after executing the first lithium battery monitoring and management scheme is to construct a standard periodic capacity loss characteristic-periodic capacity loss constant mapping table, and the capacity loss drift rate between the standard periodic capacity loss characteristic and the actual periodic capacity loss characteristic is calculated according to the mapping table, and the periodic capacity calibration strategy is optimized based on the capacity loss drift rate to obtain the second lithium battery monitoring and management scheme, which specifically includes the following steps:

[0034] Obtaining the actual charge and discharge plan, and restoring the internal resistance coefficient of the low-balanced charge and discharge cycle battery pack of the lithium battery to the ideal internal resistance coefficient by running the first lithium battery monitoring and management solution, and then continuing to monitor several lithium ion free dynamic matrix chains of the lithium battery under the actual charge and discharge plan based on the BMS;

[0035] Based on the big data network, the knowledge graph of lithium battery system is obtained, and several lithium ion free dynamic matrix chains are imported into the knowledge graph of lithium battery system for identification, and several standard periodic capacity loss characteristics and corresponding periodic capacity loss constants are obtained, and a standard periodic capacity loss characteristic-periodic capacity loss constant mapping table is constructed;

[0036] The actual capacity calibration behavior of each lithium-ion free dynamic matrix chain is evaluated by BSM to extract the actual periodic capacity loss characteristics of each lithium-ion free dynamic matrix chain. The Mahalanobis distance between the actual periodic capacity loss characteristics of each lithium-ion free dynamic matrix chain and each standard periodic capacity loss characteristic is calculated one by one in the standard periodic capacity loss characteristic-periodic capacity loss constant mapping table to obtain the Mahalanobis distance calculation result.

[0037] A Mahalanobis distance threshold interval is preset, and if at least one Mahalanobis distance in the Mahalanobis distance calculation result is within the Mahalanobis distance threshold interval, a periodic capacity loss constant corresponding to the standard periodic capacity loss feature within the Mahalanobis distance threshold interval is extracted;

[0038] Introducing a subwindow between the standard periodic capacity loss feature and the actual periodic capacity loss feature of the drift detection algorithm, generating an adaptive drift direction according to the periodic capacity loss constant corresponding to the standard periodic capacity loss feature, describing the size of the sliding subwindow according to the adaptive drift direction, and obtaining the capacity loss drift rate;

[0039] Based on the capacity loss drift rate, the capacity of the lithium battery and the periodic capacity calibration strategy of the BMS are optimized and managed and uploaded to the maintenance log to obtain a second lithium battery monitoring and management solution.

[0040] Furthermore, in a preferred embodiment of the present invention, the optimal charging mode control strategy of the charger for the lithium battery is obtained, the optimal charging mode control strategy is executed to obtain the charging rate variation amplitude of the lithium battery, and the thermal runaway emergency plan of the lithium battery and the charger is managed by analyzing the charging rate variation amplitude, as well as the recommendation of the optimal charging wire product, which specifically includes the following steps:

[0041] Obtaining a charging mode control strategy of the charger under different preset charging protocol constraints; wherein the charging protocol includes a fast charging protocol, a trickle charging protocol, and a pulse charging protocol;

[0042] Obtain the model and specification of the lithium battery, and obtain the charging regulation benchmark in the big data network based on the model and specification, and extract the actual charging protocol of the lithium battery according to the model and specification and the charging regulation benchmark;

[0043] The correlation between the actual charging protocol and different preset charging protocols is calculated through the chi-square test to obtain multiple correlations, and the charging mode control strategy corresponding to the preset charging protocol with the maximum correlation is extracted, which is defined as the optimal charging mode control strategy of the charger for the lithium battery;

[0044] Preset multiple target time nodes, obtain the specifications of the charging wires currently connecting the charger and the lithium battery, execute the optimal charging mode control strategy under the connection state of the charger and the lithium battery according to the specifications of the charging wires, and obtain the charging rate at each preset target time node;

[0045] The charging rate change amplitude of the charger for the lithium battery is calculated in combination with the charging rate at each preset target time node. If the charging rate change amplitude is less than the preset charging rate change amplitude, the charging rate change amplitude is evaluated based on the BMS in the charger to obtain an evaluation result, and the corresponding thermal runaway emergency plan of the lithium battery and the charger is retrieved according to the evaluation result;

[0046] If the charging rate variation amplitude is greater than the preset charging rate variation amplitude, obtaining the interface specifications of the lithium battery and the interface specifications of the charger, and constructing a standard charging wire model according to the interface specifications of the lithium battery and the interface specifications of the charger;

[0047] Preset a matching rate threshold, obtain related charging wire products in the big data network based on the standard charging wire model, calculate the matching rate between the actual charging protocol and each related charging wire product, extract the charging wire products corresponding to the matching rate above the matching rate threshold, and obtain multiple optimal charging wire products;

[0048] Construct a descending sorting table, sort multiple optimal charging wire products in descending order according to the adaptation rate in the descending sorting table, obtain a descending sorting table of optimal charging wire products and upload it to the lithium battery management platform.

[0049] A second aspect of the present invention provides a lithium battery working state monitoring and management system based on a BMS, wherein the lithium battery working state monitoring and management system based on a BMS comprises a memory and a processor, wherein a lithium battery working state monitoring and management method program based on a BMS is stored in the memory, and when the lithium battery working state monitoring and management method program based on a BMS is executed by the processor, the following steps are implemented:

[0050] Obtain several groups of lithium battery working state parameters of the lithium battery in each charge and discharge plan, build a radar chart distribution framework according to the preset lithium battery health status assessment model in the BMS, introduce the Hermite curve method to align and analyze the several groups of lithium battery working state parameters in the radar chart distribution framework, and obtain the lithium ion free dynamic matrix chain of the lithium battery in all charge and discharge plans;

[0051] Constructing a hidden Markov chain model of a lithium ion free dynamic matrix chain, analyzing the charging and discharging priority scheduling order of each battery pack based on the hidden Markov chain, and analyzing and optimizing the internal resistance change of the low balanced charge and discharge cycle battery pack in the pattern distribution diagram of all battery packs in the lithium battery according to the charging and discharging priority scheduling order, and obtaining a first lithium battery monitoring and management plan;

[0052] Acquire several lithium ion free dynamic matrix chains under the actual charge and discharge plan after executing the first lithium battery monitoring and management scheme to construct a standard periodic capacity loss characteristic-periodic capacity loss constant mapping table, calculate the capacity loss drift rate between the standard periodic capacity loss characteristic and the actual periodic capacity loss characteristic according to the mapping table, and optimize the periodic capacity calibration strategy based on the capacity loss drift rate to obtain the second lithium battery monitoring and management scheme;

[0053] Obtain the optimal charging mode control strategy of the charger for the lithium battery, run the optimal charging mode control strategy to obtain the charging rate variation amplitude of the lithium battery, and manage the thermal runaway emergency plan of the lithium battery and the charger and recommend the best charging wire product by analyzing the charging rate variation amplitude.

[0054] The present invention solves the technical defects existing in the background technology, and the beneficial technical effects of the present invention are:

[0055] Acquire several groups of lithium battery working state parameters in each charging and discharging plan, align and analyze the several groups of lithium battery working state parameters in the radar chart distribution framework, and obtain the lithium ion free dynamic matrix chain of the lithium battery in all charging and discharging plans; construct a hidden Markov chain model of the lithium ion free dynamic matrix chain, analyze the charging and discharging priority scheduling order of each battery group based on the hidden Markov chain, so as to adjust and obtain the first lithium battery monitoring and management plan; calculate the capacity loss drift rate between the standard periodic capacity loss characteristic and the actual periodic capacity loss characteristic by constructing a standard periodic capacity loss characteristic-periodic capacity loss constant mapping table, and optimize and manage the periodic capacity calibration strategy based on the capacity loss drift rate to obtain the second lithium battery monitoring and management plan; obtain the optimal charging mode control strategy of the charger for the lithium battery, run the optimal charging mode control strategy to obtain the charging rate change amplitude of the lithium battery, and manage the thermal runaway emergency plan of the lithium battery and the charger by analyzing the charging rate change amplitude and recommending the best charging wire product. The present invention can further efficiently and reasonably monitor and manage the working status of the lithium battery based on the BMS, so as to improve the charging and discharging performance and safety of the lithium battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0057] Figure 1 A first method flow chart of a method for monitoring and managing the working status of a lithium battery based on a BMS is shown;

[0058] Figure 2 A second method flow chart of a method for monitoring and managing the working status of a lithium battery based on a BMS is shown;

[0059] Figure 3 A third method flow chart of a method for monitoring and managing the working status of a lithium battery based on a BMS is shown;

[0060] Figure 4 A system framework diagram of a lithium battery working status monitoring and management system based on BMS is shown. DETAILED DESCRIPTION

[0061] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0062] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0063] The first aspect of the present invention provides a method for monitoring and managing the working status of a lithium battery based on a BMS, such as Figure 1 As shown, the following steps are included:

[0064] S102: obtaining several groups of lithium battery working state parameters of the lithium battery in each charge and discharge plan, constructing a radar chart distribution framework according to a preset lithium battery health status assessment model in the BMS, introducing a Hermite curve method to align and analyze the several groups of lithium battery working state parameters in the radar chart distribution framework, and obtaining a lithium ion free dynamic matrix chain of the lithium battery in all charge and discharge plans;

[0065] S104: constructing a hidden Markov chain model of a lithium ion free dynamic matrix chain, analyzing the charging and discharging priority scheduling order of each battery pack based on the hidden Markov chain, and analyzing and optimizing the internal resistance change of the low balanced charge and discharge cycle battery pack in the pattern distribution diagram of all battery packs in the lithium battery according to the charging and discharging priority scheduling order, and obtaining a first lithium battery monitoring and management plan;

[0066] S106: obtaining a plurality of lithium ion free dynamic matrix chains under the actual charge and discharge plan after executing the first lithium battery monitoring and management scheme, so as to construct a standard periodic capacity loss characteristic-periodic capacity loss constant mapping table, calculating the capacity loss drift rate between the standard periodic capacity loss characteristic and the actual periodic capacity loss characteristic according to the mapping table, and optimizing and managing the periodic capacity calibration strategy based on the capacity loss drift rate, so as to obtain a second lithium battery monitoring and management scheme;

[0067] S108: Obtaining an optimal charging mode control strategy for the charger for the lithium battery, running the optimal charging mode control strategy to obtain the charging rate variation amplitude of the lithium battery, and managing the thermal runaway emergency plan of the lithium battery and the charger and recommending the best charging wire product by analyzing the charging rate variation amplitude.

[0068] Furthermore, in a preferred embodiment of the present invention, the several groups of lithium battery working state parameters of the lithium battery in each charge and discharge plan are obtained, a radar chart distribution framework is constructed according to a preset lithium battery health status assessment model in the BMS, and the Hermite curve method is introduced to align and analyze the several groups of lithium battery working state parameters in the radar chart distribution framework to obtain a lithium ion free dynamic matrix chain of the lithium battery in all charge and discharge plans, such as Figure 2 As shown, the specific steps include:

[0069] S202: Preset a load object and several monitoring timestamps of the lithium battery, obtain a BMS management strategy for the lithium battery for the load object and different optimal charge and discharge plans formulated by the BMS management strategy, and perform charge and discharge monitoring on the lithium battery at several monitoring timestamps based on the execution of the BMS management strategy under different optimal charge and discharge plans to obtain several groups of lithium battery working status parameters for each charge and discharge plan;

[0070] S204: obtaining a preset lithium battery health status assessment model in the BMS, extracting assessment criteria through the preset lithium battery health status assessment model in the BMS, and constructing a radar chart distribution architecture according to the assessment criteria;

[0071] S206: Obtain N lithium battery health status assessment indicators through the assessment criteria, divide the radar chart distribution framework into a plurality of sub-assessment indicator radar chart areas based on the N lithium battery health status assessment indicators, and obtain the charge and discharge safety benchmark threshold of each lithium battery health status assessment indicator;

[0072] S208: Introducing the Hermite curve method to draw the curve area of ​​the charge and discharge safety benchmark threshold of each lithium battery health status assessment indicator, obtaining the Hermite surface area of ​​each sub-assessment indicator radar chart area, and at the same time fitting the several groups of lithium battery working state parameters corresponding to each charge and discharge plan in the Hermite curve method to obtain several Hermite curves for each charge and discharge plan;

[0073] S210: aligning several Hermite curves of each charging and discharging plan one by one to the Hermite surface area of ​​each sub-evaluation indicator radar chart, obtaining several alignment rates of each charging and discharging plan, and calculating the average according to the several alignment rates of each charging and discharging plan to obtain the average alignment rate under each charging and discharging plan;

[0074] S212: Analyze the health status of the lithium battery through the average registration rate under each charge and discharge plan to determine the lithium ion migration characteristics of each lithium battery working state parameter, and generate a lithium ion free dynamic matrix chain of the lithium battery in all charge and discharge plans according to the lithium ion migration characteristics.

[0075] It should be noted that BMS is a battery management system. Lithium batteries are usually equipped with a BMS battery management system to monitor and manage the voltage, current, temperature and other parameters of their charging and discharging, in order to protect the battery from dangerous situations such as overcharging, over-discharging, and short circuit, so as to optimize the battery charging and discharging performance. However, as the use time of lithium batteries increases, the lithium batteries themselves will undergo a variety of health changes, such as increased internal resistance, capacity decay, circuit aging, and thermal runaway, which will cause the charging and discharging performance of lithium batteries to drop significantly, and easily lead to charging and discharging safety accidents. Therefore, it is necessary to regularly evaluate the health of lithium batteries, but the traditional evaluation method of BMS for lithium batteries is usually to perform the evaluation based on a preset health status evaluation model. The traditional health status evaluation model can only evaluate the use of lithium batteries one by one according to each preset indicator. The evaluation rate is slow, and the evaluation process and results are prone to errors, which is not conducive to the monitoring and management of lithium batteries by BMS. In addition, the traditional health status evaluation model cannot further analyze the migration characteristics of lithium ions, and the evaluation function is single; this method can evaluate the health status of lithium batteries based on BMS. The previously preset lithium battery health status assessment model is converted into a radar chart distribution architecture. By using the Hermite curve method to perform Hermite curve fitting and registration on several groups of lithium battery working status parameters of each charging and discharging plan within the radar chart distribution architecture at the same time, the health status of the lithium battery can be determined more quickly and efficiently. Compared with the traditional health status assessment model, the assessment rate is faster. Among them, the Chinese name of the Hermite curve method is the Hermite curve method. The Hermite curve fitting and registration method can avoid curve fitting deviation and improve the accuracy of health status assessment. The average registration rate calculated according to the Hermite curve can further analyze the migration characteristics of lithium ions and realize the diversification of assessment functions.

[0076] Further, in a preferred embodiment of the present invention, the health status of the lithium battery is analyzed by the average registration rate under each charge and discharge plan to determine the lithium ion migration characteristics of each lithium battery working state parameter, and the lithium ion free dynamic matrix chain of the lithium battery in all charge and discharge plans is generated according to the lithium ion migration characteristics, which specifically includes the following steps:

[0077] Connecting a plurality of monitoring time stamp nodes in time sequence to form a preset monitoring time period, and obtaining a determination frequency of a situation in which the average registration rate under each charging and discharging plan is greater than a preset average registration rate when the radar chart distribution architecture is in the preset monitoring time period;

[0078] If the determination frequency is greater than the preset determination frequency, it means that the health of the lithium battery is significantly reduced compared with the normal health. At this time, the electrode material information, electrolyte information, diaphragm pore size distribution and production process information of the lithium battery are obtained, and the circuit input parameters corresponding to each group of lithium battery working state parameters in each charge and discharge plan are obtained;

[0079] Constructing a three-dimensional model of a lithium battery according to the electrode material information, electrolyte information, diaphragm pore size distribution and production process information, and writing circuit input parameters corresponding to each group of lithium battery working state parameters into the three-dimensional model of the lithium battery to perform charge and discharge simulation;

[0080] During the simulation, the lithium ions in the three-dimensional model of the lithium battery are measured by a transmission electron microscope to obtain the lithium ion particle size and specific surface area generated under each group of lithium battery working state parameters in each charge and discharge plan, and the diffusion coefficient of the lithium ions in the electrode material corresponding to each group of lithium battery working state parameters in each charge and discharge plan is obtained in the big data network based on the lithium ion particle size, specific surface area and electrolyte information;

[0081] The diffusion distribution law of lithium ions in different electrode materials is obtained through the big data network, and the CANOPY clustering algorithm is introduced. M initial CANOPY clusters are created according to the diffusion distribution law of lithium ions in different electrode materials. The diffusion coefficients of lithium ions in electrode materials corresponding to the working state parameters of each lithium battery in each charge and discharge plan are allocated to the M initial CANOPY clusters, and the diffusion distribution law after clustering is output;

[0082] A lithium ion charge migration model and a lithium ion discharge migration model are constructed and outputted according to the diffusion distribution law after clustering, and the volume ratio of the lithium ion charge migration model and the lithium ion discharge migration model is calculated. The lithium ion migration characteristics of the working state parameters of each lithium battery are determined according to the volume ratio.

[0083] The lithium ion migration characteristics of each lithium battery working state parameter are defined as a matrix link unit. All matrix link units in each charge and discharge plan are dynamically planned through matrix chain multiplication to obtain the lithium ion free dynamic matrix chain of the lithium battery in all charge and discharge plans.

[0084] It should be noted that changes in the health status of lithium batteries are often closely related to changes in the migration of lithium ions inside the lithium batteries. Therefore, the changes in the lithium ion mobility of the lithium battery in each charge and discharge plan can be further obtained by analyzing the migration characteristics of lithium ions, so as to provide analytical data basis for the efficient management of lithium batteries by the subsequent BMS; among them, the average registration rate reflects that the health status of the lithium battery has reached or exceeded the preset charge and discharge safety benchmark threshold, and the judgment frequency that the average registration rate is greater than the preset average registration rate reflects the frequency of the lithium battery reaching or exceeding the preset charge and discharge safety benchmark threshold, indicating that the degree of lithium ion changes inside the lithium battery is large; by constructing a three-dimensional model of the lithium battery and simulating and measuring the lithium ions under each charge and discharge plan, the diffusion coefficient of the lithium ions in the electrode material corresponding to each group of lithium battery working state parameters in each charge and discharge plan can be obtained. The diffusion coefficient reflects the migration ability or movement speed of lithium ions in the battery material. Battery internal resistance adjustment and capacity calibration have certain data analysis value; the diffusion distribution law corresponding to the lithium ion diffusion coefficient is clustered by the CANOPY clustering algorithm. The diffusion distribution law represents the diffusion path, discrete degree and diffusion law of lithium ions. According to the diffusion distribution law, the lithium ion migration behavior during the charging and discharging of the lithium battery can be further analyzed to determine the migration characteristics of the lithium ions, wherein the migration characteristics include but are not limited to the migration number, mobility and conductivity. Finally, according to the migration of lithium ions, the lithium ion free dynamic matrix chain of the lithium battery in all charging and discharging plans can be constructed in the matrix chain multiplication. The lithium ion free dynamic matrix chain expresses the free dynamic behavior and changes of all lithium ions in the lithium battery under different charging and discharging plans. This lithium ion free dynamic matrix chain can provide a basis for the internal resistance and capacity error analysis of the lithium battery itself, greatly improving the accuracy of the monitoring and management of the working status of the lithium battery, and has high reliability. Among them, the Chinese name of the CANOPY clustering algorithm is the Peng clustering algorithm, which can improve the data clustering rate and accuracy, reduce the computational complexity, and has strong robustness to noise data and outliers.

[0085] Further, in a preferred embodiment of the present invention, the hidden Markov chain model of the lithium ion free dynamic matrix chain is constructed, and the charging and discharging priority scheduling order of each battery pack is analyzed based on the hidden Markov chain, and the internal resistance change of the low balanced charge and discharge cycle battery pack is analyzed and optimized in the pattern distribution diagram of all battery packs in the lithium battery according to the charging and discharging priority scheduling order, so as to obtain the first lithium battery monitoring and management plan, which specifically includes the following steps:

[0086] Acquire a state set and an observation set of a lithium ion free dynamic matrix chain, determine the probability distribution and transition probability of each state in the state set according to the observation set, and construct a hidden Markov chain model of the lithium ion free dynamic matrix chain based on the probability distribution and transition probability of each state in the state set;

[0087] The maximum likelihood estimation algorithm is introduced to extract the state sequence of the hidden Markov chain model in the current lithium battery internal resistance to construct a state transition probability matrix, and the likelihood function of each state sequence in the state transition probability matrix is ​​calculated and maximized to obtain the state transition probability of the lithium ion free dynamic matrix chain in the current lithium battery internal resistance;

[0088] Determine whether the state transition probability is greater than the preset state transition probability. If so, it indicates that the internal resistance of the lithium battery may increase in the lithium ion free dynamic matrix chain, and obtain the pattern distribution diagram of all battery packs in the lithium battery;

[0089] Obtain the optimal charge and discharge cycle management strategy of the BMS for the lithium battery, and run the optimal charge and discharge cycle management strategy on the lithium battery. At this time, measure the temperature control value of each battery pack, and mark the battery pack with a temperature control value less than the preset temperature control value in the pattern distribution map, and define it as a low balanced charge and discharge cycle battery pack;

[0090] Preset a charge and discharge priority scheduling order threshold, obtain the charge and discharge priority scheduling order of each battery group in the lithium battery by the BMS, mark the battery group corresponding to the charge and discharge priority scheduling order greater than the charge and discharge priority scheduling order threshold in the pattern distribution map, and obtain the battery group pattern distribution area with a high priority scheduling order;

[0091] Obtaining the real-time scheduling frequency of each battery group when the lithium battery executes the optimal charge and discharge cycle management strategy, calculating the charge and discharge cycle scheduling weight of each battery group based on the real-time scheduling frequency, and extracting the charge and discharge cycle scheduling weight corresponding to the low balanced charge and discharge cycle battery group;

[0092] If the charge and discharge cycle scheduling weight corresponding to the low balanced charge and discharge cycle battery group is in the battery group pattern distribution area with a high priority scheduling order, the internal resistance change rate of this battery group at the preset time node is measured, and the optimal charge and discharge cycle management strategy of the BMS is optimized and managed in combination with the high priority scheduling order of the battery group pattern distribution area and the internal resistance change rate to obtain the first lithium battery monitoring and management plan.

[0093] It should be noted that as the battery is used, the electrolyte, electrodes and other components inside the battery may change, resulting in an increase in the internal resistance of the battery. The increase in internal resistance will lead to an increase in power output, unstable voltage and significant heat generation, affecting the performance and efficiency of the battery. Therefore, the BMS needs to monitor the changes in the internal resistance of the battery to perform charge and discharge cycles to correct the internal resistance of the lithium battery. However, the current BMS internal resistance adjustment method for lithium batteries is to uniformly execute the same preset charge and discharge cycle strategy for all battery packs in the lithium battery. This will cause the internal resistance of each battery pack of the lithium battery to be adjusted unevenly, which may cause the frequently called battery packs to fail to achieve the ideal working state, and there will be problems such as output power disorder and unconcentrated heat dissipation, reducing the management accuracy of the BMS for lithium batteries. The method can first analyze the internal resistance of the lithium battery through the state transition probability of the lithium ion free dynamic matrix chain in the current lithium battery internal resistance. If the internal resistance increases, the low balanced charge and discharge cycle battery group is screened out by running the optimal charge and discharge cycle management strategy on the lithium battery. The low average The balanced charge and discharge cycle battery group is a battery group that cannot achieve balanced charge and discharge cycle when the BMS adjusts the internal resistance of the lithium battery; then the battery group pattern distribution area with a high priority scheduling order is marked according to the charge and discharge priority scheduling order. If the charge and discharge cycle scheduling weight corresponding to the low balanced charge and discharge cycle battery group is in the battery group pattern distribution area with a high priority scheduling order, it means that this low balanced charge and discharge cycle battery group needs to be frequently called when the lithium battery is working. Therefore, it is necessary to adjust the optimal charge and discharge cycle management strategy of the BMS to achieve priority adjustment of the internal resistance of the low balanced charge and discharge cycle battery group, so that the internal resistance of the lithium battery can be reasonably and efficiently adjusted according to the working state of the lithium battery, thereby avoiding the power increase, heat and voltage instability caused by the increase of the internal resistance of the lithium battery, and further improving the charge and discharge performance and safety of the lithium battery.

[0094] Furthermore, in a preferred embodiment of the present invention, the acquisition of several lithium ion free dynamic matrix chains under the actual charge and discharge plan after executing the first lithium battery monitoring and management scheme is performed to construct a standard periodic capacity loss characteristic-periodic capacity loss constant mapping table, and the capacity loss drift rate between the standard periodic capacity loss characteristic and the actual periodic capacity loss characteristic is calculated according to the mapping table. Based on the capacity loss drift rate, the periodic capacity calibration strategy is optimized and managed to obtain the second lithium battery monitoring and management scheme, such as Figure 3 As shown, the specific steps include:

[0095] S302: Obtaining an actual charge and discharge plan, and restoring the internal resistance coefficient of the lithium battery low-balanced charge and discharge cycle battery pack to an ideal internal resistance coefficient by running a first lithium battery monitoring and management solution. At this time, the BMS continues to monitor a plurality of lithium ion free dynamic matrix chains of the lithium battery under the actual charge and discharge plan;

[0096] S304: obtaining a lithium battery system knowledge graph based on a big data network, importing a number of lithium ion free dynamic matrix chains into the lithium battery system knowledge graph for identification, obtaining a number of standard periodic capacity loss characteristics and corresponding periodic capacity loss constants, and constructing a standard periodic capacity loss characteristic-periodic capacity loss constant mapping table;

[0097] S306: Evaluate the actual capacity calibration behavior of each lithium-ion free dynamic matrix chain through BSM to extract the actual periodic capacity loss characteristics of each lithium-ion free dynamic matrix chain, and calculate the Mahalanobis distance between the actual periodic capacity loss characteristics of each lithium-ion free dynamic matrix chain and each standard periodic capacity loss characteristic in the standard periodic capacity loss characteristic-periodic capacity loss constant mapping table one by one to obtain the Mahalanobis distance calculation result;

[0098] S308: Preset a Mahalanobis distance threshold interval, and if at least one Mahalanobis distance in the Mahalanobis distance calculation result is within the Mahalanobis distance threshold interval, extract a periodic capacity loss constant corresponding to a standard periodic capacity loss feature within the Mahalanobis distance threshold interval;

[0099] S310: introducing a sub-window between the standard periodic capacity loss feature and the actual periodic capacity loss feature of the drift detection algorithm, generating an adaptive drift direction according to the periodic capacity loss constant corresponding to the standard periodic capacity loss feature, describing the size of the sliding sub-window according to the adaptive drift direction, and obtaining a capacity loss drift rate;

[0100] S312: Based on the capacity loss drift rate, the capacity of the lithium battery and the periodic capacity calibration strategy of the BMS are optimized and managed and uploaded to the maintenance log to obtain a second lithium battery monitoring and management solution.

[0101] It should be noted that, similarly, as the frequency of battery charge and discharge cycles increases, the available capacity of the battery will gradually decrease, resulting in capacity decay, which will lead to a rapid decrease in the use time and service life of the lithium battery, and it is easy to cause the lithium battery working power to decrease, the equipment performance to be limited, and the safety of use to be reduced during use. Therefore, this method can identify several standard periodic capacity loss characteristics and corresponding periodic capacity loss constants in the lithium battery system knowledge graph according to several lithium ion free dynamic matrix chains under the actual charge and discharge plan, so as to construct a standard periodic capacity loss characteristic-periodic capacity loss constant mapping table, and according to the mapping The shooting table calculates the capacity loss drift rate between the standard periodic capacity loss characteristics and the actual periodic capacity loss characteristics; among them, when at least one Mahalanobis distance in the Mahalanobis distance calculation result is within the Mahalanobis distance threshold range, it means that the lithium battery has capacity loss or attenuation. The capacity of the lithium battery and the periodic capacity calibration strategy of the BMS can be optimized and managed according to the capacity loss drift rate, so as to eliminate the capacity drift that occurs during the use of the lithium battery, so as to improve the health, working performance and safety factor of the lithium battery, increase the use time and service life of the lithium battery, and at the same time ensure that the BMS monitors and manages the battery status accurately, with high economic benefits and reliability.

[0102] Furthermore, in a preferred embodiment of the present invention, the optimal charging mode control strategy of the charger for the lithium battery is obtained, the optimal charging mode control strategy is executed to obtain the charging rate variation amplitude of the lithium battery, and the thermal runaway emergency plan of the lithium battery and the charger is managed by analyzing the charging rate variation amplitude, as well as the recommendation of the optimal charging wire product, which specifically includes the following steps:

[0103] Obtaining a charging mode control strategy of the charger under different preset charging protocol constraints; wherein the charging protocol includes a fast charging protocol, a trickle charging protocol, and a pulse charging protocol;

[0104] Obtain the model and specification of the lithium battery, and obtain the charging regulation benchmark in the big data network based on the model and specification, and extract the actual charging protocol of the lithium battery according to the model and specification and the charging regulation benchmark;

[0105] The correlation between the actual charging protocol and different preset charging protocols is calculated through the chi-square test to obtain multiple correlations, and the charging mode control strategy corresponding to the preset charging protocol with the maximum correlation is extracted, which is defined as the optimal charging mode control strategy of the charger for the lithium battery;

[0106] Preset multiple target time nodes, obtain the specifications of the charging wires currently connecting the charger and the lithium battery, execute the optimal charging mode control strategy under the connection state of the charger and the lithium battery according to the specifications of the charging wires, and obtain the charging rate at each preset target time node;

[0107] The charging rate change amplitude of the charger for the lithium battery is calculated in combination with the charging rate at each preset target time node. If the charging rate change amplitude is less than the preset charging rate change amplitude, the charging rate change amplitude is evaluated based on the BMS in the charger to obtain an evaluation result, and the corresponding thermal runaway emergency plan of the lithium battery and the charger is retrieved according to the evaluation result;

[0108] If the charging rate variation amplitude is greater than the preset charging rate variation amplitude, obtaining the interface specifications of the lithium battery and the interface specifications of the charger, and constructing a standard charging wire model according to the interface specifications of the lithium battery and the interface specifications of the charger;

[0109] Preset a matching rate threshold, obtain related charging wire products in the big data network based on the standard charging wire model, calculate the matching rate between the actual charging protocol and each related charging wire product, extract the charging wire products corresponding to the matching rate above the matching rate threshold, and obtain multiple optimal charging wire products;

[0110] Construct a descending sorting table, sort multiple optimal charging wire products in descending order according to the adaptation rate in the descending sorting table, obtain a descending sorting table of optimal charging wire products and upload it to the lithium battery management platform.

[0111] It should be noted that lithium batteries are usually preset with standard charging protocols when leaving the factory to achieve accurate communication with different chargers, thereby achieving efficient charging. However, when a charging wire that is incompatible with the charging protocol is connected between the lithium battery and the charger, the lithium battery charging rate may slow down, the charging heat may rise rapidly, or even the battery may fail to charge. These phenomena may cause dangerous accidents such as continuous high temperature and explosion of the lithium battery, greatly increasing the risk factor of lithium battery use. The present method can evaluate the charging rate change of the charging wire connected to the lithium battery and the charger under the execution of the optimal charging mode control strategy, thereby further analyzing the compatibility of the charging wire between the lithium battery and the charger. If the charging rate change amplitude is less than the preset charging rate change amplitude, it means that the charging performance of the lithium battery and the charger is stable under the current charging wire connection, and no excessive current and voltage changes are generated. Although the charging rate change amplitude is less than the preset charging rate change amplitude, a change in charging rate still occurs, which may be caused by the BMS's failure to execute the thermal runaway strategy for the lithium battery and the charger in a timely manner. Therefore, it is necessary to efficiently manage and retrieve the thermal runaway emergency plan corresponding to the lithium battery and the charger; and if the charging rate change amplitude is greater than the preset charging rate change amplitude, it means that at this time, under the current charging wire connection, the current and voltage of the lithium battery and the charger have undergone a large change, making the charging performance trend gradually unstable. This may be caused by the charging wire being incompatible with the charging protocol of the lithium battery or the charger. Therefore, this method can realize the screening and recommendation of the best charging wire products suitable for lithium batteries and chargers, so that users can better choose and purchase charging wire products suitable for lithium batteries or chargers to ensure the charging rate and charging safety of the lithium battery, and greatly improve the user's charging experience.

[0112] In addition, the BMS-based lithium battery working status monitoring and management method further includes the following steps:

[0113] Obtaining the regular characteristics of the user's charging behavior, and presetting a safe charging time period based on an analysis of the regular characteristics of the user's charging behavior;

[0114] If there is charging behavior within the safe charging time period, the remaining power information of the lithium battery is obtained, and the protocol switching decision of the BMS for the remaining power information is obtained, and whether the lithium battery executes the fast charging protocol within the safe charging time period is determined according to the protocol switching decision;

[0115] If the lithium battery has executed the fast charging protocol, the fast charging monitoring management strategy for the remaining power information of the BMS in the state of executing the fast charging protocol is obtained, and the current characteristic state parameters within the preset time period are obtained. The fast charging monitoring management strategy is evaluated based on the current characteristic state parameters within the preset time period to obtain the fast charging safety assessment score;

[0116] If the fast charging safety assessment score is lower than the preset fast charging safety assessment score, the BMS is controlled to stop executing the fast charging protocol and switch to the conventional charging protocol. The charging rate of the conventional charging protocol under different preset environmental factor combinations is obtained based on the big data network. The conventional charging protocol is trained under different preset environmental factors by the support vector machine.

[0117] Charging rate under factor combination conditions to build a charging rate prediction model;

[0118] Acquire real-time environmental factors in a safe charging time period, import the real-time environmental factors into a charging rate prediction model, and obtain a charging rate prediction value for executing a conventional charging protocol in the safe charging time period;

[0119] The time node when the lithium battery charging is completed is calculated in combination with the remaining power information and the charging rate prediction value. If the time node when the lithium battery charging is completed exceeds the user's lithium battery usage time node, the charging parameters of the conventional charging protocol are adjusted.

[0120] It should be noted that different users have different charging behaviors for lithium batteries. For example, some users prefer to fast charge lithium batteries while sleeping at night. However, this kind of fast charging behavior often has great safety hazards, which can easily lead to lithium battery fast charging safety accidents when charging is unsupervised, endangering the user's life safety. This method can quickly evaluate the safety of lithium batteries executing fast charging protocols within a preset safe charging time period according to the user's charging behavior rules, switch the charging protocol according to the fast charging safety assessment score obtained by the evaluation, and if it switches to the conventional charging protocol, it will predict the charging rate of the conventional charging protocol according to the time node of the user's required use of the lithium battery, thereby intelligently regulating the charging parameters of the conventional charging protocol to ensure that the user's lithium battery charging needs can be met under conventional charging, improve the safety factor of users' irregular execution of lithium battery fast charging, avoid the occurrence rate of charging safety hazard accidents, and improve the charging performance of lithium batteries.

[0121] In addition, the BMS-based lithium battery working status monitoring and management method further includes the following steps:

[0122] Obtaining the user's movement trajectory record within a preset time period, and presetting the charging and borrowing device positioning range according to the movement trajectory record;

[0123] Obtaining the layout locations of all charging borrowing devices within the charging device positioning range, drawing an initial planning map based on the charging device positioning range, and marking the layout location of each charging borrowing device in the initial planning map to obtain a charging borrowing device planning map;

[0124] Obtaining coordinate information of the current user position and coordinate information of the center point of the charging borrowing equipment planning map, calculating a coordinate offset ratio between the coordinate information of the current user position and the coordinate information of the center point of the charging borrowing equipment planning map, and presetting an optimal borrowing planning value based on the coordinate offset ratio;

[0125] Taking the current user location as the center point, plan the optimal borrowing range in the charging borrowing equipment planning map until the optimal borrowing planning threshold is reached to obtain the optimal borrowing range;

[0126] Construct a sorting table, calculate the distance values ​​between all charging borrowing devices in the optimal borrowing range and the current user's location, import the distance values ​​into the sorting table for sorting from small to large, and mark the charging borrowing device corresponding to the shortest distance value, and upload the charging borrowing device corresponding to the shortest distance value to the lithium battery BMS emergency management plan.

[0127] It should be noted that, since lithium batteries can be installed on certain mobile devices, users can carry them with them, but lithium batteries will run out of power after a certain period of use. At this time, it is necessary to use charging equipment to charge the lithium batteries. With the advancement of technology, charging borrowing equipment is currently installed in various places, which can facilitate users to borrow charging equipment at will. This method can filter out the charging borrowing equipment with the shortest distance from the user according to the user's current location and recommend it to the user, so that the user can quickly borrow the charging equipment to charge the lithium battery in time, avoid the lithium battery being completely exhausted and causing damage to the lithium battery, and increase the service life of the lithium battery, with high economic benefits.

[0128] In a second aspect, the present invention provides a lithium battery working state monitoring and management system based on a BMS, wherein the lithium battery working state monitoring and management system based on a BMS comprises a memory 41 and a processor 42, wherein the memory 41 stores a lithium battery working state monitoring and management method program based on a BMS, and when the lithium battery working state monitoring and management method program based on a BMS is executed by the processor 42, Figure 4 As shown, implement the following steps:

[0129] Obtain several groups of lithium battery working state parameters of the lithium battery in each charge and discharge plan, build a radar chart distribution framework according to the preset lithium battery health status assessment model in the BMS, introduce the Hermite curve method to align and analyze the several groups of lithium battery working state parameters in the radar chart distribution framework, and obtain the lithium ion free dynamic matrix chain of the lithium battery in all charge and discharge plans;

[0130] Constructing a hidden Markov chain model of a lithium ion free dynamic matrix chain, analyzing the charging and discharging priority scheduling order of each battery pack based on the hidden Markov chain, and analyzing and optimizing the internal resistance change of the low balanced charge and discharge cycle battery pack in the pattern distribution diagram of all battery packs in the lithium battery according to the charging and discharging priority scheduling order, and obtaining a first lithium battery monitoring and management plan;

[0131] Acquire several lithium ion free dynamic matrix chains under the actual charge and discharge plan after executing the first lithium battery monitoring and management scheme to construct a standard periodic capacity loss characteristic-periodic capacity loss constant mapping table, calculate the capacity loss drift rate between the standard periodic capacity loss characteristic and the actual periodic capacity loss characteristic according to the mapping table, and optimize the periodic capacity calibration strategy based on the capacity loss drift rate to obtain the second lithium battery monitoring and management scheme;

[0132] Obtain the optimal charging mode control strategy of the charger for the lithium battery, run the optimal charging mode control strategy to obtain the charging rate variation amplitude of the lithium battery, and manage the thermal runaway emergency plan of the lithium battery and the charger and recommend the best charging wire product by analyzing the charging rate variation amplitude.

[0133] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for monitoring and managing the working status of a lithium battery based on BMS, characterized in that: The following steps are involved: Obtain several groups of lithium battery working state parameters of the lithium battery in each charge and discharge plan, build a radar chart distribution framework according to the preset lithium battery health status assessment model in the BMS, introduce the Hermite curve method to align and analyze the several groups of lithium battery working state parameters in the radar chart distribution framework, and obtain the lithium ion free dynamic matrix chain of the lithium battery in all charge and discharge plans; Constructing a hidden Markov chain model of a lithium ion free dynamic matrix chain, analyzing the charging and discharging priority scheduling order of each battery pack based on the hidden Markov chain, and analyzing and optimizing the internal resistance change of the low balanced charge and discharge cycle battery pack in the pattern distribution diagram of all battery packs in the lithium battery according to the charging and discharging priority scheduling order, and obtaining a first lithium battery monitoring and management plan; Acquire several lithium ion free dynamic matrix chains under the actual charge and discharge plan after executing the first lithium battery monitoring and management scheme to construct a standard periodic capacity loss characteristic-periodic capacity loss constant mapping table, calculate the capacity loss drift rate between the standard periodic capacity loss characteristic and the actual periodic capacity loss characteristic according to the mapping table, and optimize the periodic capacity calibration strategy based on the capacity loss drift rate to obtain the second lithium battery monitoring and management scheme; Obtaining the optimal charging mode control strategy of the charger for the lithium battery, running the optimal charging mode control strategy to obtain the charging rate variation amplitude of the lithium battery, and managing the thermal runaway emergency plan of the lithium battery and the charger and recommending the best charging wire product by analyzing the charging rate variation amplitude; Among them, obtaining several groups of lithium battery working state parameters of the lithium battery in each charging and discharging plan, building a radar chart distribution framework according to the preset lithium battery health status assessment model in the BMS, introducing the Hermite curve method to align and analyze the several groups of lithium battery working state parameters in the radar chart distribution framework, and obtaining the lithium ion free dynamic matrix chain of the lithium battery in all charging and discharging plans, specifically includes the following steps: Preset the load object and several monitoring timestamps of the lithium battery, obtain the BMS management strategy of the lithium battery for the load object and different optimal charge and discharge plans formulated by the BMS management strategy, execute the BMS management strategy based on different optimal charge and discharge plans to perform charge and discharge monitoring on the lithium battery at several monitoring timestamps, and obtain several groups of lithium battery working status parameters for each charge and discharge plan; Obtain a preset lithium battery health status assessment model in the BMS, extract assessment criteria through the preset lithium battery health status assessment model in the BMS, and construct a radar chart distribution architecture according to the assessment criteria; Obtain N lithium battery health status assessment indicators through the assessment criteria, divide the radar chart distribution architecture into a plurality of sub-assessment indicator radar chart fields based on the N lithium battery health status assessment indicators, and obtain the charge and discharge safety benchmark threshold of each lithium battery health status assessment indicator; The Hermite curve method is introduced to draw the curve area of ​​the charge and discharge safety benchmark threshold of each lithium battery health status assessment indicator, and the Hermite surface area of ​​each sub-assessment indicator radar chart is obtained. At the same time, several groups of lithium battery working state parameters corresponding to each charge and discharge plan are fitted in the Hermite curve method to obtain several Hermite curves for each charge and discharge plan; Align several Hermite curves of each charging and discharging plan one by one to the Hermite surface area of ​​each sub-evaluation indicator radar chart, obtain several alignment rates of each charging and discharging plan, calculate the average value according to the several alignment rates of each charging and discharging plan, and obtain the average alignment rate under each charging and discharging plan; The health status of the lithium battery is analyzed by the average registration rate under each charge and discharge plan to determine the lithium ion migration characteristics of each lithium battery working state parameter, and the lithium ion free dynamic matrix chain of the lithium battery in all charge and discharge plans is generated according to the lithium ion migration characteristics.

2. A method for monitoring and managing the working status of a lithium battery based on BMS according to claim 1, characterized in that: The health status of the lithium battery is analyzed by the average registration rate under each charge and discharge plan to determine the lithium ion migration characteristics of each lithium battery working state parameter, and the lithium ion free dynamic matrix chain of the lithium battery in all charge and discharge plans is generated according to the lithium ion migration characteristics, which specifically includes the following steps: Connecting a plurality of monitoring time stamp nodes in time sequence to form a preset monitoring time period, and obtaining a determination frequency of a situation in which the average registration rate under each charging and discharging plan is greater than a preset average registration rate when the radar chart distribution architecture is in the preset monitoring time period; If the determination frequency is greater than the preset determination frequency, it means that the health of the lithium battery is significantly reduced compared with the normal health. At this time, the electrode material information, electrolyte information, diaphragm pore size distribution and production process information of the lithium battery are obtained, and the circuit input parameters corresponding to each group of lithium battery working state parameters in each charge and discharge plan are obtained; Constructing a three-dimensional model of a lithium battery according to the electrode material information, electrolyte information, diaphragm pore size distribution and production process information, and writing circuit input parameters corresponding to each group of lithium battery working state parameters into the three-dimensional model of the lithium battery to perform charge and discharge simulation; During the simulation, the lithium ions in the three-dimensional model of the lithium battery are measured by a transmission electron microscope to obtain the lithium ion particle size and specific surface area generated under each group of lithium battery working state parameters in each charge and discharge plan, and the diffusion coefficient of the lithium ions in the electrode material corresponding to each group of lithium battery working state parameters in each charge and discharge plan is obtained in the big data network based on the lithium ion particle size, specific surface area and electrolyte information; The diffusion distribution law of lithium ions in different electrode materials is obtained through the big data network, and the CANOPY clustering algorithm is introduced. M initial CANOPY clusters are created according to the diffusion distribution law of lithium ions in different electrode materials. The diffusion coefficients of lithium ions in electrode materials corresponding to the working state parameters of each lithium battery in each charge and discharge plan are allocated to the M initial CANOPY clusters, and the diffusion distribution law after clustering is output; A lithium ion charge migration model and a lithium ion discharge migration model are constructed and outputted according to the diffusion distribution law after clustering, and the volume ratio of the lithium ion charge migration model and the lithium ion discharge migration model is calculated. The lithium ion migration characteristics of the working state parameters of each lithium battery are determined according to the volume ratio. The lithium ion migration characteristics of each lithium battery working state parameter are defined as a matrix link unit. All matrix link units in each charge and discharge plan are dynamically planned through matrix chain multiplication to obtain the lithium ion free dynamic matrix chain of the lithium battery in all charge and discharge plans.

3. A method for monitoring and managing the working status of a lithium battery based on BMS according to claim 1, characterized in that: The hidden Markov chain model of the lithium ion free dynamic matrix chain is constructed, and the charging and discharging priority scheduling order of each battery group is analyzed based on the hidden Markov chain, and the internal resistance change of the low balanced charging and discharging cycle battery group is analyzed and optimized in the pattern distribution diagram of all battery groups in the lithium battery according to the charging and discharging priority scheduling order, so as to obtain the first lithium battery monitoring and management plan, which specifically includes the following steps: Acquire a state set and an observation set of a lithium ion free dynamic matrix chain, determine the probability distribution and transition probability of each state in the state set according to the observation set, and construct a hidden Markov chain model of the lithium ion free dynamic matrix chain based on the probability distribution and transition probability of each state in the state set; The maximum likelihood estimation algorithm is introduced to extract the state sequence of the hidden Markov chain model in the current lithium battery internal resistance to construct a state transition probability matrix, and the likelihood function of each state sequence in the state transition probability matrix is ​​calculated and maximized to obtain the state transition probability of the lithium ion free dynamic matrix chain in the current lithium battery internal resistance; Determine whether the state transition probability is greater than the preset state transition probability. If so, it indicates that the internal resistance of the lithium battery may increase in the lithium ion free dynamic matrix chain, and obtain the pattern distribution diagram of all battery packs in the lithium battery; Obtain the optimal charge and discharge cycle management strategy of the BMS for the lithium battery, and run the optimal charge and discharge cycle management strategy on the lithium battery. At this time, measure the temperature control value of each battery pack, and mark the battery pack with a temperature control value less than the preset temperature control value in the pattern distribution map, and define it as a low balanced charge and discharge cycle battery pack; Preset a charge and discharge priority scheduling order threshold, obtain the charge and discharge priority scheduling order of each battery group in the lithium battery by the BMS, mark the battery group corresponding to the charge and discharge priority scheduling order greater than the charge and discharge priority scheduling order threshold in the pattern distribution map, and obtain the battery group pattern distribution area with a high priority scheduling order; Obtaining the real-time scheduling frequency of each battery group when the lithium battery executes the optimal charge and discharge cycle management strategy, calculating the charge and discharge cycle scheduling weight of each battery group based on the real-time scheduling frequency, and extracting the charge and discharge cycle scheduling weight corresponding to the low balanced charge and discharge cycle battery group; If the charge and discharge cycle scheduling weight corresponding to the low balanced charge and discharge cycle battery group is in the battery group pattern distribution area with a high priority scheduling order, the internal resistance change rate of this battery group at the preset time node is measured, and the optimal charge and discharge cycle management strategy of the BMS is optimized and managed in combination with the high priority scheduling order of the battery group pattern distribution area and the internal resistance change rate to obtain the first lithium battery monitoring and management plan.

4. A method for monitoring and managing the working status of a lithium battery based on BMS according to claim 1, characterized in that: The method of obtaining a plurality of lithium ion free dynamic matrix chains under the actual charge and discharge plan after executing the first lithium battery monitoring and management scheme is to construct a standard periodic capacity loss characteristic-periodic capacity loss constant mapping table, and calculating the capacity loss drift rate between the standard periodic capacity loss characteristic and the actual periodic capacity loss characteristic according to the mapping table, and optimizing and managing the periodic capacity calibration strategy based on the capacity loss drift rate to obtain the second lithium battery monitoring and management scheme, specifically includes the following steps: Obtaining the actual charge and discharge plan, and restoring the internal resistance coefficient of the low-balanced charge and discharge cycle battery pack of the lithium battery to the ideal internal resistance coefficient by running the first lithium battery monitoring and management solution, and then continuing to monitor several lithium ion free dynamic matrix chains of the lithium battery under the actual charge and discharge plan based on the BMS; Based on the big data network, the knowledge graph of lithium battery system is obtained, and several lithium ion free dynamic matrix chains are imported into the knowledge graph of lithium battery system for identification, and several standard periodic capacity loss characteristics and corresponding periodic capacity loss constants are obtained, and a standard periodic capacity loss characteristic-periodic capacity loss constant mapping table is constructed; The actual capacity calibration behavior of each lithium-ion free dynamic matrix chain is evaluated by BSM to extract the actual periodic capacity loss characteristics of each lithium-ion free dynamic matrix chain. The Mahalanobis distance between the actual periodic capacity loss characteristics of each lithium-ion free dynamic matrix chain and each standard periodic capacity loss characteristic is calculated one by one in the standard periodic capacity loss characteristic-periodic capacity loss constant mapping table to obtain the Mahalanobis distance calculation result. A Mahalanobis distance threshold interval is preset, and if at least one Mahalanobis distance in the Mahalanobis distance calculation result is within the Mahalanobis distance threshold interval, a periodic capacity loss constant corresponding to the standard periodic capacity loss feature within the Mahalanobis distance threshold interval is extracted; Introducing a subwindow between the standard periodic capacity loss feature and the actual periodic capacity loss feature of the drift detection algorithm, generating an adaptive drift direction according to the periodic capacity loss constant corresponding to the standard periodic capacity loss feature, describing the size of the sliding subwindow according to the adaptive drift direction, and obtaining the capacity loss drift rate; Based on the capacity loss drift rate, the capacity of the lithium battery and the periodic capacity calibration strategy of the BMS are optimized and managed and uploaded to the maintenance log to obtain a second lithium battery monitoring and management solution.

5. The method for monitoring and managing the working status of a lithium battery based on BMS according to claim 1, characterized in that: The method of obtaining the optimal charging mode control strategy of the charger for the lithium battery, executing the optimal charging mode control strategy to obtain the charging rate variation amplitude of the lithium battery, and managing the thermal runaway emergency plan of the lithium battery and the charger and recommending the optimal charging wire product by analyzing the charging rate variation amplitude specifically includes the following steps: Obtaining a charging mode control strategy of the charger under different preset charging protocol constraints; wherein the charging protocol includes a fast charging protocol, a trickle charging protocol, and a pulse charging protocol; Obtain the model and specification of the lithium battery, and obtain the charging regulation benchmark in the big data network based on the model and specification, and extract the actual charging protocol of the lithium battery according to the model and specification and the charging regulation benchmark; The correlation between the actual charging protocol and different preset charging protocols is calculated through the chi-square test to obtain multiple correlations, and the charging mode control strategy corresponding to the preset charging protocol with the maximum correlation is extracted, which is defined as the optimal charging mode control strategy of the charger for the lithium battery; Preset multiple target time nodes, obtain the specifications of the charging wires currently connecting the charger and the lithium battery, execute the optimal charging mode control strategy under the connection state of the charger and the lithium battery according to the specifications of the charging wires, and obtain the charging rate at each preset target time node; The charging rate change amplitude of the charger for the lithium battery is calculated in combination with the charging rate at each preset target time node. If the charging rate change amplitude is less than the preset charging rate change amplitude, the charging rate change amplitude is evaluated based on the BMS in the charger to obtain an evaluation result, and the corresponding thermal runaway emergency plan of the lithium battery and the charger is retrieved according to the evaluation result; If the charging rate variation amplitude is greater than the preset charging rate variation amplitude, obtaining the interface specifications of the lithium battery and the interface specifications of the charger, and constructing a standard charging wire model according to the interface specifications of the lithium battery and the interface specifications of the charger; Preset a matching rate threshold, obtain related charging wire products in the big data network based on the standard charging wire model, calculate the matching rate between the actual charging protocol and each related charging wire product, extract the charging wire products corresponding to the matching rate above the matching rate threshold, and obtain multiple optimal charging wire products; Construct a descending sorting table, sort multiple optimal charging wire products in descending order according to the adaptation rate in the descending sorting table, obtain a descending sorting table of optimal charging wire products and upload it to the lithium battery management platform.

6. A lithium battery working status monitoring and management system based on BMS, characterized in that: The BMS-based lithium battery working status monitoring and management system includes a memory and a processor, wherein the memory stores a BMS-based lithium battery working status monitoring and management method program, and when the BMS-based lithium battery working status monitoring and management method program is executed by the processor, the BMS-based lithium battery working status monitoring and management method steps as described in any one of claims 1 to 5 are implemented.

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