A battery safety management system and method

Abstract

The present invention discloses a battery safety management system and method, which relates to the field of battery management technology. After multiple cycles of iteration based on the optimization algorithm, the optimal charging strategy is output for the battery. During the charging process, the battery is divided into several monitoring areas. After obtaining multiple data of the monitoring area in real time, the multiple data are brought into the TOPSIS algorithm to calculate the relative closeness index of each monitoring area. After the relative closeness index of each monitoring area is comprehensively weighted and calculated according to the weight coefficient, it is predicted whether the battery will be abnormal during the charging process, and a corresponding management strategy is generated based on the prediction result. During the battery charging process, the management system can dynamically adjust the charging strategy in combination with the battery's historical charging data, ensure the battery charging efficiency while ensuring the battery charging safety, and predict the battery charging abnormality, so that the battery can be processed in time before the abnormality occurs, further ensuring the battery safety.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and in particular to a battery safety management system and method. Background Art

[0002] With the rapid development of advanced battery technologies such as lithium-ion batteries, the energy density, charging and discharging speed, and service life of batteries have been significantly improved. However, these advantages also bring safety risks, such as thermal runaway, short circuit, overcharging, over-discharging, etc., which may cause battery fire, explosion or performance degradation. The management system (Battery-Safety-Management-System, BSMS) is a comprehensive management solution designed for safety risks that may occur during battery use;

[0003] The prior art has the following defects:

[0004] 1. In order to extend the service life and safety of the battery, the battery has corresponding charging strategies for different power ranges (including trickle charging, fast charging and supplementary charging) during the charging process. The existing management system usually selects the corresponding charging speed to charge the battery in different power ranges based on the preset charging strategy (usually preset by the battery manufacturer). However, this charging method cannot guarantee that the battery is charged at the optimal charging speed in each stage, and may reduce the overall charging efficiency of the battery and reduce the user experience;

[0005] 2. The existing management system usually only monitors the overall battery voltage, temperature and other parameters during the charging process. However, this monitoring will result in insufficient early warning of the battery. When the battery is abnormal, it may be accompanied by safety issues such as battery fire and explosion, and the safe charging of the battery cannot be guaranteed.

[0006] Based on this, the present invention proposes a battery safety management system and method, which can dynamically adjust the charging strategy in combination with the battery's historical charging data during the battery charging process, thereby ensuring the battery charging safety while ensuring the battery charging efficiency, and predicting battery charging anomalies, so that the battery can be processed in time before an abnormality occurs, further ensuring battery safety. Summary of the invention

[0007] The purpose of the present invention is to provide a battery safety management system and method to solve the deficiencies in the background technology.

[0008] In order to achieve the above object, the present invention provides the following technical solution: a battery safety management system and method, the management method comprising the following steps:

[0009] The management system obtains the charging restrictions of the battery at different stages through the battery database, generates several charging strategies based on the charging restrictions using a random generation tool, calculates the loss value of each charging strategy using a loss function, and outputs the optimal charging strategy for the battery after multiple iterations based on the optimization algorithm.

[0010] During the battery charging process, the management system controls the charging speed of the battery at different stages through the optimal charging strategy, and divides the battery into several monitoring areas. After obtaining multiple data of the monitoring areas in real time, the multiple data are brought into the TOPSIS algorithm to calculate the relative closeness index of each monitoring area. After analyzing the importance of each monitoring area relative to the battery, a corresponding weight coefficient is generated for each monitoring area.

[0011] After comprehensively weighting and calculating the relative proximity index of each monitoring area according to the weight coefficient, it is predicted whether the battery will have abnormalities during the charging process. If the battery is predicted to have abnormalities, a corresponding management strategy is generated based on the prediction results.

[0012] In a preferred embodiment, the loss value of each charging strategy is calculated using a loss function, including the following steps:

[0013] Obtain the charging time, charging equipment loss index, and temperature warning frequency of each charging strategy history;

[0014] The charging time, charging equipment loss index, and temperature warning frequency are input into the loss function, and the loss function calculates and outputs the loss value. The expression is: , where is the loss value, For charging time, is the charging equipment loss index, is the temperature warning frequency, , , are the adjustment coefficients for charging time, charging equipment loss index, and temperature warning frequency, respectively, and , , Both are greater than 0.

[0015] In a preferred embodiment, after multiple iterations of the optimization algorithm, an optimal charging strategy is output for the battery, including the following steps:

[0016] The loss values ​​of all charging strategies are summed up to obtain the total loss value. The proportion value of each charging strategy is obtained by dividing the loss value by the total loss value. The proportion value is mapped to the sector area of ​​the virtual roulette. The larger the proportion value of the charging strategy, the larger the sector area occupied on the virtual roulette. The virtual roulette is started to rotate. When the virtual roulette stops rotating, the charging strategy corresponding to the sector area pointed by the virtual pointer is deleted from the virtual roulette. The virtual roulette is started repeatedly. When the number of charging strategies retained on the virtual roulette is equal to the preset number threshold, the charging strategy deletion step is stopped, and the charging strategies retained on the virtual roulette are First, perform pairwise exchange operations, and then mutate the charging strategies that have completed the exchange operation. Finally, establish a set of all charging strategies that have completed the mutation operation, and recalculate the loss values ​​of all charging strategies in the set through the loss function. Repeat the above steps for cyclic iteration. When there is a charging strategy with a loss value less than or equal to the loss threshold in any output set, or the number of iterations is equal to the number threshold, it is judged that the convergence condition is met, and the management system outputs multiple sets, and integrates the multiple sets into a charging strategy set, and selects the charging strategy with the smallest loss value in the charging strategy set as the optimal charging strategy.

[0017] In a preferred embodiment, after obtaining multiple data of the monitoring area in real time, the multiple data are brought into the TOPSIS algorithm to calculate the relative closeness index of each monitoring area, including the following steps:

[0018] Acquire multiple data of the monitoring area in real time, including the temperature change rate and the internal resistance change rate, that is, the indicators of the monitoring area include the temperature change rate and the internal resistance change rate;

[0019] Construct a correlation matrix X with the monitoring area and multiple data, standardize the correlation matrix to obtain a standardized matrix Z, assign a weight to each indicator in the standardized matrix Z, and obtain a weighted matrix G;

[0020] Based on the weighted matrix G, the positive solution and negative solution are obtained, and the distance from the monitoring area to the positive solution and the distance from the negative solution are calculated. The relative closeness index calculation expression of the monitoring area is: , where is a relatively close index. is the distance to the forward solution, is the distance to the negative solution.

[0021] In a preferred embodiment, the correlation matrix is ​​standardized to obtain a standardized matrix Z, which is expressed as: , where represents the standardized value of the jth indicator in the ith monitoring area, represents the jth index value of the i-th monitoring area, is the number of monitoring areas;

[0022] Assign weights to each indicator in the standardized matrix Z so that the sum of all weights is 1. The expression is: , where is the number of indicators, is the weight of the jth indicator, then the expression of the weighted matrix G is: , where represents the weighted value of the jth standardized index in the ith monitoring area, is the weight of the jth indicator;

[0023] Based on the weighted matrix G, the positive solution and negative solution are obtained, and the expression is: , where is the forward solution, is a negative solution, is the set of temperature change rates, is a set of internal resistance change rate indicators, Indicates taking the minimum value, Indicates taking the maximum value;

[0024] Calculate the distance from the monitoring area to the positive solution and the negative solution. The expression is: , where is the distance to the forward solution, is the distance to the negative solution, represents the weighted value of the jth standardized index, represents the minimum value of index j in all monitoring areas, Indicates the maximum value of index j in all monitoring areas.

[0025] In a preferred embodiment, after analyzing the importance of each monitoring area relative to the battery, a corresponding weight coefficient is generated for each monitoring area, including the following steps:

[0026] Obtain the number of times the battery cannot be used due to historical anomalies in the monitoring area, and the number of times the battery causes safety problems due to historical anomalies;

[0027] The number of times the battery cannot be used due to historical anomalies and the number of times the battery causes safety problems due to historical anomalies are normalized, so that the value range of the number of times the battery cannot be used due to historical anomalies and the number of times the battery causes safety problems due to historical anomalies are mapped to [0,1], and the normalized value of the number of times the battery cannot be used and the normalized value of the number of times the safety problems are caused are obtained. The normalized value of the number of times the battery cannot be used and the normalized value of the number of times the safety problems are caused are summed to obtain the importance index of the monitoring area;

[0028] The importance indexes of all monitoring areas are summed up to obtain the total index, and the importance index is divided by the total index to obtain the weight coefficient of the monitoring area.

[0029] In a preferred embodiment, after the relative proximity index of each monitoring area is comprehensively weighted and calculated according to the weight coefficient, whether the battery will be abnormal during the charging process is predicted, including the following steps:

[0030] After obtaining the weight coefficient and relative proximity index of each monitoring area, a comprehensive weighted calculation is performed to obtain the overall abnormality factor of the battery, which is expressed as: , where For the The weight coefficient of each monitoring area, For the The relative closeness index of the monitoring area, is the number of monitoring areas;

[0031] The obtained overall abnormality factor is compared with the preset abnormality threshold. If the overall abnormality factor is less than or equal to the abnormality threshold, it is predicted that the battery will not have abnormalities during the charging process. If the overall abnormality factor is greater than the abnormality threshold, it is predicted that the battery will have abnormalities during the charging process.

[0032] In a preferred embodiment, the charging time is the time it takes for the battery to charge from 0% to 100%;

[0033] The calculation logic of the temperature warning frequency is: obtain the charging time of the battery and the number of temperature warnings during the charging process, and divide the number of temperature warnings by the charging time to obtain the temperature warning frequency;

[0034] The calculation expression of the charging equipment loss index is: , where is the charging current, is the charging voltage, is the equivalent resistance of the device, It is a coefficient related to the insulation and voltage withstand capacity of the equipment.

[0035] In a preferred embodiment, the management system obtains charging restriction conditions of the battery at different stages through the battery database, and generates several charging strategies based on the charging restriction conditions using a random generation tool, including the following steps:

[0036] The management system obtains the maximum charging speed and the minimum charging speed of the battery when the battery is in the trickle charging stage; obtains the maximum charging speed and the minimum charging speed of the battery when the battery is in the fast charging stage; obtains the maximum charging speed and the minimum charging speed of the battery when the battery is in the supplementary charging stage;

[0037] The maximum charging speed and the minimum charging speed of each charging stage are input as constraints into the random generation tool. Based on the constraints, the random generation tool outputs several charging strategies. Each charging strategy includes the charging speed of the trickle charging stage, the charging speed of the fast charging stage and the charging speed of the supplementary charging stage.

[0038] A battery safety management system, comprising a charging strategy generation module, a charging strategy optimization output module, a calculation module, and a prediction module;

[0039] Charging strategy generation module: obtains charging constraints of batteries at different stages through the battery database, and generates several charging strategies based on the charging constraints using a random generation tool;

[0040] Charging strategy optimization output module: After using the loss function to calculate the loss value of each charging strategy, the optimal charging strategy is output for the battery after multiple iterations based on the optimization algorithm;

[0041] Calculation module: During the charging process, the optimal charging strategy is used to control the charging speed of the battery at different stages, and the battery is divided into several monitoring areas. After obtaining multiple data of the monitoring areas in real time, the multiple data are brought into the TOPSIS algorithm to calculate the relative closeness index of each monitoring area. After analyzing the importance of each monitoring area relative to the battery, a corresponding weight coefficient is generated for each monitoring area.

[0042] Prediction module: After comprehensively weighting and calculating the relative proximity index of each monitoring area according to the weight coefficient, it predicts whether the battery will have abnormalities during the charging process, and generates corresponding management strategies based on the prediction results.

[0043] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0044] 1. The present invention uses a random generation tool to generate several charging strategies based on charging restriction conditions. After using the loss function to calculate the loss value of each charging strategy, the optimal charging strategy is output for the battery after multiple cycles of iteration based on the optimization algorithm. The battery is divided into several monitoring areas during the charging process. After obtaining multiple data of the monitoring area in real time, the multiple data are brought into the TOPSIS algorithm to calculate the relative closeness index of each monitoring area. After the relative closeness index of each monitoring area is comprehensively weighted and calculated according to the weight coefficient, it is predicted whether the battery will be abnormal during the charging process, and a corresponding management strategy is generated based on the prediction result. During the battery charging process, the management system can dynamically adjust the charging strategy in combination with the battery's historical charging data, ensure the battery charging efficiency while ensuring the battery charging safety, and predict the battery charging anomaly, so that the battery can be processed in time before the abnormality occurs, further ensuring the battery safety;

[0045] 2. The present invention sums up the loss values ​​of all charging strategies to obtain the total loss value, obtains the proportion value of each charging strategy by dividing the loss value by the total loss value, and maps the proportion value to the sector area of ​​the virtual roulette. The larger the proportion value of the charging strategy, the larger the sector area occupied on the virtual roulette. The virtual roulette is started to rotate. When the rotation of the virtual roulette stops, the charging strategy corresponding to the sector area pointed by the virtual pointer is deleted from the virtual roulette, and the virtual roulette is started repeatedly. When the number of charging strategies retained on the virtual roulette is equal to a preset number threshold, the charging strategy deletion step is stopped. Through this step, the charging strategies with small loss values ​​can be retained first, and a certain degree of randomness is added during the retention process to avoid the management system from falling into a local optimal solution, thereby increasing the robustness of the management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0047] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] Example 1: Please refer to Figure 1 As shown, a battery safety management method described in this embodiment includes the following steps:

[0050] The management system obtains the charging restrictions of the battery at different stages through the battery database, generates several charging strategies based on the charging restrictions using a random generation tool, calculates the loss value of each charging strategy using a loss function, and outputs the optimal charging strategy for the battery after multiple cycles of iteration based on the optimization algorithm. During the charging process of the battery, the management system controls the charging speed of the battery at different stages through the optimal charging strategy, and divides the battery into several monitoring areas. After obtaining multiple data of the monitoring area in real time, the multiple data are brought into the TOPSIS algorithm to calculate the relative proximity index of each monitoring area. After analyzing the importance of each monitoring area relative to the battery, a corresponding weight coefficient is generated for each monitoring area. After the relative proximity index of each monitoring area is comprehensively weighted calculated according to the weight coefficient, it is predicted whether the battery will have abnormalities during the charging process, and a corresponding management strategy is generated based on the prediction results.

[0051] The present invention generates several charging strategies based on charging restriction conditions using a random generation tool, calculates the loss value of each charging strategy using a loss function, and outputs the optimal charging strategy for the battery after multiple cycles of iteration based on the optimization algorithm. The battery is divided into several monitoring areas during the charging process, and multiple data of the monitoring area are obtained in real time. The multiple data are brought into the TOPSIS algorithm to calculate the relative closeness index of each monitoring area. After the relative closeness index of each monitoring area is comprehensively weighted and calculated according to the weight coefficient, it is predicted whether the battery will be abnormal during the charging process, and a corresponding management strategy is generated based on the prediction result. The management system can dynamically adjust the charging strategy in combination with the historical charging data of the battery during the battery charging process, ensure the battery charging efficiency while ensuring the battery charging safety, and predict the battery charging abnormality, so that the battery can be processed in time before the abnormality occurs, further ensuring the battery safety.

[0052] Embodiment 2: The management system obtains charging restriction conditions of the battery at different stages through the battery database, and generates several charging strategies based on the charging restriction conditions using a random generation tool, including the following steps:

[0053] The management system obtains the maximum charging speed and the minimum charging speed of the battery when the battery is in the trickle charging stage; obtains the maximum charging speed and the minimum charging speed of the battery when the battery is in the fast charging stage; obtains the maximum charging speed and the minimum charging speed of the battery when the battery is in the supplementary charging stage;

[0054] The maximum charging speed and the minimum charging speed of each charging stage are input as constraints into the random generation tool. The random generation tool outputs several charging strategies based on the constraints. Each charging strategy includes the charging speed of the trickle charging stage, the charging speed of the fast charging stage, and the charging speed of the supplementary charging stage.

[0055] Use the random library in Python to generate random numbers that meet the conditions. Each strategy includes three stages of charging speed: trickle charging, fast charging, and supplementary charging. The following is a sample code:

[0056] Import-random

[0057] Def-generate_charging_strategy(tricle_range,fast_range,topping_range,num_strategies):

[0058] """

[0059] Randomly generate charging strategies.

[0060] parameter:

[0061] -trickle_range: A tuple of minimum and maximum charging speeds (min_speed, max_speed) during the trickle charging phase.

[0062] -fast_range: A tuple of minimum and maximum charging speeds (min_speed, max_speed) during the fast charging phase.

[0063] - topping_range: A tuple of minimum and maximum charging speeds (min_speed, max_speed) during the topping-up phase.

[0064] -num_strategies: Number of charging strategies to generate.

[0065] return:

[0066] -A list of several charging strategies, each strategy contains three stages of charging speed.

[0067] """

[0068] strategies=[]

[0069] for_in-range(num_strategies):

[0070] trickle_speed=random.uniform(*tricle_range)

[0071] fast_speed=random.uniform(*fast_range)

[0072] topping_speed=random.uniform(*topping_range)

[0073] strategies.append({

[0074] 'Trickle-Charging-Speed': trickle_speed,

[0075] 'Fast-Charging-Speed':fast_speed,

[0076] 'Topping-Charging-Speed':topping_speed

[0077] })

[0078] Return-strategies

[0079] #Set the charging speed range for each stage (minimum speed, maximum speed)

[0080] trickle_range=(0.1,0.5)#Speed ​​range of trickle charging stage

[0081] fast_range=(1.0,3.0)#Fast charging stage speed range

[0082] topping_range=(0.2,1.0)#Supplement the speed range of the charging stage

[0083] #Generate the number of charging strategies

[0084] num_strategies=5

[0085] #Generate charging strategy

[0086] charging_strategies=generate_charging_strategy(tricle_range,fast_range,topping_range,num_strategies)

[0087] #Output the generated charging strategy

[0088] For-i,strategy-in-enumerate(charging_strategies,1):

[0089] print(f"Charging-Strategy{i}:")

[0090] For-stage,speed-in-strategy.items():

[0091] print(f"{stage}:{speed:.2f}")

[0092] print().

[0093] Function definition: generate_charging_strategy() accepts the charging speed range for each charging stage and the number of generated strategies.

[0094] Generation strategy: Use random.uniform() to generate the charging speed within the specified range for each stage and store it in a dictionary.

[0095] Output results: The generated strategies are saved in a list, and finally the charging speed of each strategy is output one by one.

[0096] Executing this code will output a specified number of charging strategies, each of which contains random speeds for trickle charging, fast charging, and supplementary charging. The speed range and number of strategies for each charging stage can be adjusted as needed.

[0097] The loss function is used to calculate the loss value of each charging strategy, including the following steps:

[0098] Obtain the charging time, charging equipment loss index, and temperature warning frequency of each charging strategy history;

[0099] The charging time, charging equipment loss index, and temperature warning frequency are input into the loss function, and the loss function calculates and outputs the loss value. The expression is: , where is the loss value, For charging time, is the charging equipment loss index, is the temperature warning frequency, , , are the adjustment coefficients for charging time, charging equipment loss index, and temperature warning frequency, respectively, and , , Both are greater than 0. The larger the loss value, the worse the performance of the charging strategy on the battery.

[0100] In this application, the charging time is the time it takes for the battery to charge from 0% to 100%. For battery charging, the longer the charging time, the lower the charging efficiency of the battery when the charging strategy is adopted;

[0101] The calculation logic of the temperature warning frequency is as follows: obtain the battery charging time and the number of temperature warnings during the charging process (when the real-time battery temperature exceeds the temperature threshold, the charging device will send a warning signal prompt), and divide the number of temperature warnings by the charging time to obtain the temperature warning frequency. The larger the temperature warning frequency, the faster the temperature rises when the battery adopts this charging strategy, which is likely to cause thermal runaway or fire of the battery.

[0102] The calculation expression of charging equipment loss index is: , where is the charging current, is the charging voltage, is the equivalent resistance of the device (can be obtained from the device data sheet or measurement), It is a coefficient related to the insulation and voltage tolerance of the equipment (which can be obtained from the equipment data sheet or measurement). The larger the charging equipment loss index is, the greater the burden on the charging equipment when the battery is charged using this charging strategy, which can easily lead to charging equipment failure or shorten its service life.

[0103] After multiple iterations of the optimization algorithm, the optimal charging strategy is output for the battery. During the charging process, the management system controls the charging speed of the battery at different stages through the optimal charging strategy, including the following steps:

[0104] After obtaining the loss value of each charging strategy, the smaller the loss value, the better the overall effect of the charging vehicle strategy application and battery charging;

[0105] The loss values ​​of all charging strategies are summed up to obtain the total loss value, and the proportion value of each charging strategy is obtained by dividing the loss value by the total loss value. The proportion value is mapped to the sector area of ​​the virtual roulette. The larger the proportion value of the charging strategy, the larger the sector area occupied on the virtual roulette. The virtual roulette is started to rotate. When the rotation of the virtual roulette stops, the charging strategy corresponding to the sector area pointed by the virtual pointer is deleted from the virtual roulette. The virtual roulette is started repeatedly. When the number of charging strategies retained on the virtual roulette is equal to the preset number threshold, the charging strategy deletion step is stopped. Through this step, the charging strategies with small loss values ​​can be retained first, and a certain degree of randomness is added in the retention process to avoid the management system from falling into a local optimal solution, thereby increasing the robustness of the management system;

[0106] The charging strategies retained on the virtual roulette wheel are first exchanged in pairs, and then the charging strategies that have completed the exchange operation are mutated. Finally, a set is established for all the charging strategies that have completed the mutation operation, and the loss values ​​of all the charging strategies in the set are recalculated through the loss function. The above steps are repeated for cyclic iteration. When there is a charging strategy with a loss value less than or equal to the loss threshold in any output set, or the number of iterations is equal to the number threshold, it is judged that the convergence condition is met, and the management system outputs multiple sets, and the multiple sets are integrated into a charging strategy set, and the charging strategy with the smallest loss value in the charging strategy set is selected as the optimal charging strategy.

[0107] The charging strategies retained on the virtual roulette are first exchanged in pairs, and then the charging strategies that have been exchanged are mutated. The example is as follows:

[0108] In this example, we will show how to perform a pairwise exchange operation on the charging strategies retained on the virtual roulette wheel and then perform a mutation operation. Suppose we have three initial charging strategies

[0109] A, B, C, each strategy consists of three charging speeds: trickle charging speed, fast charging speed and supplementary charging speed.

[0110] Example of initial charging strategy:

[0111] Strategy A: Trickle charge speed = 0.1A, fast charge speed = 1.0A, supplementary charge speed = 0.5A;

[0112] Strategy B: Trickle charge speed = 0.15A, fast charge speed = 0.9A, supplemental charge speed = 0.45A;

[0113] Strategy C: Trickle charge rate = 0.12A, fast charge rate = 0.95A, supplemental charge rate = 0.55A.

[0114] Step 1: Pairwise exchange operation

[0115] After the pairwise swapping operation, the speeds of the various charging stages of each strategy may be interchanged:

[0116] Strategy A is swapped with Strategy B:

[0117] The exchange results are: Strategy A1 and Strategy B1;

[0118] Strategy A1: Trickle charge rate = 0.15A (from B), fast charge rate = 1.0A (from A), supplemental charge rate = 0.5A (from A);

[0119] Strategy B1: Trickle charge rate = 0.1A (from A), fast charge rate = 0.9A (from B), supplemental charge rate = 0.45A (from B);

[0120] Strategy B1 is exchanged with Strategy C:

[0121] The exchange results are: strategy B2 and strategy C1;

[0122] Strategy B2: Trickle charge rate = 0.12A (from C), fast charge rate = 0.9A (from B1), supplemental charge rate = 0.45A (from B1);

[0123] Strategy C1: Trickle charge rate = 0.1A (from B1), fast charge rate = 0.95A (from C), supplemental charge rate = 0.55A (from C);

[0124] Step 2: Mutation Operation

[0125] In the mutation operation, the charging phase speed of each strategy may be randomly increased or decreased by a certain percentage. Suppose we slightly mutate the trickle charging speed of each strategy:

[0126] Strategy A1 variation: trickle charge speed changed to 0.17A (increase of 0.02A);

[0127] Strategy B2 variation: trickle charge speed changed to 0.1A (reduced by 0.02A);

[0128] Strategy C1 variation: trickle charge speed changed to 0.08A (reduced by 0.02A);

[0129] Final Result

[0130] After the exchange and mutation operations, we get the new charging strategy:

[0131] Strategy A1: Trickle charge speed = 0.17A, fast charge speed = 1.0A, supplemental charge speed = 0.5A;

[0132] Strategy B2: Trickle charge speed = 0.1A, fast charge speed = 0.9A, supplemental charge speed = 0.45A;

[0133] Strategy C1: Trickle charge rate = 0.08A, fast charge rate = 0.95A, supplemental charge rate = 0.55A.

[0134] After this process, each strategy is recombined and mutated, providing a variety of alternatives for subsequent fitness evaluation and selection. Such operations help introduce new changes, making the charging strategy more diverse, thereby increasing the probability of obtaining the optimal strategy.

[0135] Divide the battery into several monitoring areas, including the following steps:

[0136] For different batteries, the division results of the monitoring areas are different. In this application, the battery is divided into at least two monitoring areas;

[0137] Specifically, examples of different battery monitoring area divisions are as follows:

[0138] 1) Lithium-ion battery module

[0139] Single cell monitoring area: Each cell is used as an independent monitoring area, mainly monitoring temperature, voltage and current. This method is used for large lithium-ion battery modules, such as electric vehicle battery packs, and can accurately detect single cell abnormalities.

[0140] Module monitoring area: Multiple cells form a module, and the system uses the module as a monitoring unit. Suitable for small and medium-sized devices, such as laptops and power tools.

[0141] Hot zone monitoring area: Divide the high-temperature area inside the battery (such as the central area and the edge area) and monitor the temperature distribution at different locations to prevent local overheating. Electric vehicle battery packs usually use this method and combine it with the cooling system to achieve temperature control.

[0142] 2) Lithium iron phosphate battery pack

[0143] Block monitoring area: Multiple cells are combined into one area and monitored in a module form. Commonly used in energy storage systems and heavy vehicles to monitor the voltage and temperature of each module during charging.

[0144] Circulation cooling monitoring area: Since lithium iron phosphate batteries are not sensitive to temperature but have poor heat dissipation, a monitoring area will be set around the cooling pipe to check the cooling effect and temperature consistency.

[0145] 3) NiMH battery pack

[0146] Series group monitoring area: Multiple series-connected batteries are used as a monitoring area, which is suitable for hybrid vehicle applications. The voltage, temperature and internal resistance changes of each series group are monitored to ensure consistency.

[0147] Charging load zone monitoring area: divides the battery into different load zones, focusing on the heat dissipation effect during charging. Suitable for power tools and portable devices.

[0148] 4) Lead-acid battery pack

[0149] Single cell monitoring area: Each single lead-acid battery is used as an independent monitoring area, usually used in uninterruptible power supply (UPS) systems to monitor voltage, electrolyte temperature and acid concentration.

[0150] Inter-group balancing monitoring area: Lead-acid batteries are usually used in groups, and the voltage and current between multiple batteries are balanced and monitored to prevent individual batteries from excessive consumption or aging failure during the charging and discharging process.

[0151] Heat dissipation area monitoring area: In heavy-duty applications (such as forklifts), it is more difficult to dissipate heat from lead-acid batteries. The system will set up multiple temperature monitoring areas and combine heat dissipation measures to prevent overheating.

[0152] 5) Solid-state batteries

[0153] Electrode area monitoring area: According to the characteristics of solid-state batteries, the temperature and voltage changes of the anode, cathode and electrolyte separation areas are monitored to avoid interface failure.

[0154] Structural stress monitoring area: Solid-state batteries have high internal pressure, so they can also be zoned according to the battery structure to monitor areas where expansion or contraction may occur. This is suitable for electronic devices and future electric vehicle batteries.

[0155] After obtaining multiple data of the monitoring area in real time, the multiple data are brought into the TOPSIS algorithm to calculate the relative closeness index of each monitoring area, including the following steps:

[0156] Acquire multiple data of the monitoring area in real time, including the temperature change rate and the internal resistance change rate, that is, the indicators of the monitoring area include the temperature change rate and the internal resistance change rate;

[0157] The calculation logic of the temperature change rate is as follows: during the monitoring period, obtain the current temperature and the previous temperature of the battery, obtain the temperature difference by subtracting the previous temperature from the current temperature, and obtain the temperature change rate by dividing the absolute value of the temperature difference by the monitoring time. The greater the temperature change rate, the faster the battery temperature rises or drops during the monitoring time, which may cause the following battery abnormalities:

[0158] 1) The faster the temperature rises, the more abnormalities may result:

[0159] Thermal runaway: If the temperature rises rapidly, it may be due to a rapid chemical reaction inside the battery, generating a large amount of heat. Further temperature rise may cause thermal runaway, resulting in battery fire or explosion. Overcharging: If the charging current is too large during fast charging, the temperature may rise rapidly. Overcharging accelerates battery aging, affects battery life, and may cause battery swelling or leakage. Internal short circuit: An internal short circuit causes the battery to heat up and the temperature to rise rapidly. If the temperature continues to rise, it may damage the internal structure of the battery and increase the risk of accidents. Poor contact or high current: Poor contact between the battery and the charger or excessive current can also cause the battery to heat up. Long-term heating may cause damage to the internal components of the battery and even cause the risk of explosion or combustion. Excessive external ambient temperature: If the ambient temperature around the battery is too high, it may accelerate the temperature rise of the battery, which will have an adverse effect on the internal materials and electrolytes of the battery, causing the battery to age faster or leak.

[0160] 2) The faster the temperature drops, the more abnormalities may result:

[0161] Battery polarization: Rapid temperature drop can cause polarization on the electrode surface inside the battery, affecting the battery's discharge efficiency, which may cause the battery to have difficulty discharging at low temperatures or insufficient output current. Lithium ion deposition and dendrite formation: When the temperature drops rapidly, the battery may be in a low temperature environment, causing lithium ions to deposit on the negative electrode surface and form lithium dendrites. This can damage the internal structure of the battery, reduce capacity, and in severe cases may cause an internal short circuit. Electrolyte solidification or increased viscosity: Rapid cooling may cause the battery electrolyte to solidify or increase viscosity, thereby reducing the mobility of ions inside the battery and increasing internal resistance. High internal resistance can affect battery performance and reduce battery efficiency. Accelerated aging: Rapid temperature drop may also cause uneven shrinkage of battery materials, generate internal stress, and lead to material degradation and accelerated aging. Triggering of safety protection mechanisms: Some batteries may trigger protection mechanisms when the temperature drops sharply, such as reducing the discharge rate or completely stopping discharge, to prevent further temperature drops from damaging the battery and affecting normal battery use.

[0162] In summary: Rapid changes in battery temperature, especially rapid temperature rise or drop, may be a potential risk warning signal for the battery management system. By continuously monitoring the temperature change rate, abnormal battery temperature conditions can be identified, thereby discovering and preventing potential safety hazards at an early stage.

[0163] The calculation logic of the internal resistance change rate is as follows: during the monitoring period, obtain the battery's current internal resistance and the internal resistance of the previous moment, obtain the internal resistance difference by subtracting the internal resistance of the previous moment from the current internal resistance, and obtain the internal resistance change rate by dividing the absolute value of the internal resistance difference by the monitoring duration. The greater the internal resistance change rate, the faster the battery's internal resistance increases or decreases during the monitoring duration, which may cause the following battery abnormalities:

[0164] 1) The faster the internal resistance increases, the more abnormalities may result:

[0165] Battery aging or degradation: During the use of the battery, the loss of active materials will cause the internal resistance to increase. The rapid increase in internal resistance may be due to the battery entering a rapid aging stage, which will reduce the battery's capacity and performance. Degradation or reduction of electrolyte: A rapid increase in internal resistance may mean that there is a problem with the electrolyte, such as evaporation or chemical degradation. Insufficient electrolyte will increase the difficulty of ion migration, resulting in an increase in internal resistance and affecting the charge and discharge efficiency. Degradation or peeling of electrode materials: If the electrode material is damaged by use or overload, the contact area is reduced or the conductivity is reduced, which will also cause the internal resistance to rise rapidly and affect the battery life. Increased internal polarization of the battery: The rapid increase in internal resistance may indicate that the battery polarization phenomenon has intensified, resulting in an increase in the resistance to ion flow. Polarization will cause the battery's output voltage to decrease, affecting the discharge performance. Temperature effect: A drop in temperature will increase the internal resistance of the battery, so the internal resistance of the battery may increase faster in low temperature environments. This situation may affect the performance of the battery at low temperatures and even cause rapid capacity decay.

[0166] 2) The faster the internal resistance decreases, the more abnormalities may result:

[0167] Internal short circuit in the battery: If the internal resistance drops suddenly and significantly, it may be due to an internal short circuit in the battery. A short circuit can cause the internal temperature of the battery to rise sharply, further damaging the battery structure and increasing safety risks. Uneven charging or discharging: A drop in internal resistance may indicate that there is a short conductive path in some cells inside the battery, which may be caused by uneven charging and discharging. Such conditions may cause battery overheating or voltage instability. Broken or damaged diaphragm: If the internal resistance drops rapidly, it may be that the diaphragm is damaged, causing the electrodes to directly contact and form a partial short circuit. This situation is very dangerous and may cause thermal runaway of the battery. Rising ambient temperature: Rising temperature will reduce the internal resistance of the battery. If the internal resistance drops too quickly, it may indicate that the ambient temperature rises sharply in a short period of time, causing the battery operating temperature to exceed the safe range and affecting the normal operation of the battery. Abnormal electrolyte distribution: If the electrolyte flows unevenly or redistributes inside the battery, it may cause a short-term drop in internal resistance, but this situation may be temporary and unstable, which may easily cause battery failure.

[0168] In summary: By monitoring the rate of change of internal resistance, it is possible to identify potential abnormalities such as battery aging, internal short circuit, electrolyte problems, etc. When the internal resistance rises or falls too quickly, it may indicate that the electrochemical state of the battery has changed significantly, which requires timely analysis and processing to avoid battery performance degradation or increased safety risks.

[0169] The monitoring area and multiple data are combined to construct a correlation matrix X, where each row represents a monitoring area and each column represents an indicator; ,in, is the number of monitoring areas, is the number of indicators, Represents the jth indicator value of the ith monitoring area.

[0170] The correlation matrix is ​​standardized to obtain the standardized matrix Z, which is expressed as: , where represents the standardized value of the jth indicator in the ith monitoring area, represents the jth index value of the i-th monitoring area, is the number of monitoring areas;

[0171] Assign weights to each indicator in the standardized matrix Z so that the sum of all weights is 1. The expression is: , where is the number of indicators, is the weight of the jth indicator, then the expression of the weighted matrix G is: , where represents the weighted value of the jth standardized index in the ith monitoring area, represents the standardized value of the jth indicator in the ith monitoring area, is the weight of the jth indicator;

[0172] In this application, for abnormal management of the battery, it is necessary to minimize the temperature change rate and the internal resistance change rate, and obtain the positive solution and the negative solution based on the weighted matrix G, and the expression is: , where is the forward solution, is a negative solution, represents the weighted value of the jth standardized index in the ith monitoring area, is a set of temperature change rate indicators, is a set of internal resistance change rate indicators, Indicates taking the minimum value, Indicates taking the maximum value.

[0173] Calculate the distance from the monitoring area to the positive solution and the negative solution. The expression is: , where is the distance to the forward solution, is the distance to the negative solution, represents the weighted value of the jth standardized index, represents the minimum value of index j in all monitoring areas, represents the maximum value of index j in all monitoring areas;

[0174] The calculation expression of the relative closeness index of the monitoring area is: , where is a relatively close index. is the distance to the forward solution, The larger the relative closeness index is, the more likely it is that anomalies will occur in the monitoring area.

[0175] After analyzing the importance of each monitoring area relative to the battery, a corresponding weight coefficient is generated for each monitoring area, including the following steps:

[0176] Obtain the number of times the battery cannot be used due to historical anomalies in the monitoring area, and the number of times the battery causes safety problems due to historical anomalies (safety problems include thermal runaway, explosion, etc.);

[0177] If a certain monitoring area causes the battery to be unusable multiple times, it means that the abnormality in this area directly affects the overall functional stability of the battery. Such an area may involve key parameters of battery operation, such as temperature, voltage, internal resistance, etc., indicating that the abnormality in the area is often a factor that directly damages the overall performance or function of the battery.

[0178] Safety issues such as thermal runaway and explosion are often accompanied by high energy release and rapid reaction, which are extreme cases of battery abnormalities. If the historical abnormalities of a monitoring area have repeatedly caused these safety issues, it means that once the abnormality of this area occurs, it will directly threaten the safety of the battery system. Therefore, ensuring the stable operation of this area is crucial to preventing serious safety risks.

[0179] Monitoring areas that frequently experience serious safety issues usually have a higher risk level. Therefore, in order to ensure the safety of the battery system, these areas must be monitored more intensively and accurately. Historical data shows that if an area has caused safety issues multiple times after a failure, it is necessary to prioritize resources for risk control and prevention.

[0180] The operational stability of the battery system depends on the coordinated operation of each monitoring area. If the abnormality of a certain area leads to a large number of unavailability and safety events, it means that the health status of this area has a greater impact on the overall system. This area may play a core role in the system architecture, such as thermal management or current control of the battery, so effective monitoring and management of this area is crucial.

[0181] The number of times the battery cannot be used due to historical anomalies and the number of times the battery causes safety problems due to historical anomalies are normalized, so that the value range of the number of times the battery cannot be used due to historical anomalies and the number of times the battery causes safety problems due to historical anomalies are mapped to [0,1], and the normalized value of the number of times the battery cannot be used and the normalized value of the number of times the safety problems are caused are obtained. The normalized value of the number of times the battery cannot be used and the normalized value of the number of times the safety problems are caused are summed to obtain the importance index of the monitoring area;

[0182] The importance indexes of all monitoring areas are summed up to obtain the total index, and the importance index is divided by the total index to obtain the weight coefficient of the monitoring area.

[0183] After the relative proximity index of each monitoring area is comprehensively weighted and calculated according to the weight coefficient, it is predicted whether the battery will be abnormal during the charging process, and a corresponding management strategy is generated based on the prediction result, including the following steps:

[0184] After obtaining the weight coefficient and relative proximity index of each monitoring area, a comprehensive weighted calculation is performed to obtain the overall abnormality factor of the battery, which is expressed as: , where For the The weight coefficient of each monitoring area, For the The relative closeness index of the monitoring area, is the number of monitoring areas;

[0185] The larger the overall abnormality factor is, the more likely the battery is to be abnormal. The obtained overall abnormality factor is compared with the preset abnormality threshold. If the overall abnormality factor is less than or equal to the abnormality threshold, it is predicted that the battery will not be abnormal during the charging process. If the overall abnormality factor is greater than the abnormality threshold, it is predicted that the battery will be abnormal during the charging process.

[0186] When it is predicted that the battery will be abnormal during the charging process, the corresponding management strategy is generated based on the prediction results:

[0187] According to the time window and development trend of abnormal prediction, formulate a phased charging adjustment strategy (such as gradually reducing the current or switching to trickle charging mode). Implement different degrees of adjustment measures in the early, middle and late stages of charging to disperse abnormal pressure and protect the battery. Avoid sudden changes caused by a single adjustment, make the adjustment process smooth, and maintain the relative stability of the charging process.

[0188] In the process of implementing the management strategy, the parameters of each monitoring area are monitored in real time, abnormal developments and changes are recorded, and the strategy is adjusted dynamically. Based on real-time data feedback, charging parameters and control measures are flexibly adjusted to prevent abnormal escalation or rapid strategy adjustment. Ensure that the management strategy is consistent with the actual situation and improve the effectiveness and flexibility of abnormal management.

[0189] An early warning mechanism is set up based on the prediction results and charging status. Once the abnormal indicators are close to the warning threshold, the emergency response plan is triggered (such as reducing the charging current, switching to safe mode, etc.). Before the abnormality may turn into a fault, the early warning system is activated, the relevant management system or personnel are notified in advance, and emergency processing is automatically performed. Potential risks are avoided in advance to ensure the safety of the battery and charging system.

[0190] Save the data during the abnormal prediction and management process for subsequent analysis and optimization of the prediction model. By recording abnormalities and managing data, continuously optimize the prediction model to enhance the accuracy of abnormal prediction and the effectiveness of management strategies. Continuously improve management strategies to enhance the safety and reliability of the battery charging process.

[0191] Through the above-mentioned advance management strategy, possible abnormalities can be effectively intervened during the battery charging process, reducing the negative impact of abnormalities on the battery system and accumulating experience and data for subsequent charging processes.

[0192] Embodiment 3: A battery safety management system described in this embodiment includes a charging strategy generation module, a charging strategy optimization output module, a calculation module, and a prediction module;

[0193] Charging strategy generation module: obtains charging restriction conditions of the battery at different stages through the battery database, generates several charging strategies based on the charging restriction conditions using a random generation tool, and sends the several charging strategies to the charging strategy optimization output module;

[0194] Charging strategy optimization output module: After using the loss function to calculate the loss value of each charging strategy, the optimal charging strategy is output for the battery after multiple cycles of iteration based on the optimization algorithm, and the optimal charging strategy is sent to the calculation module;

[0195] Calculation module: During the charging process, the optimal charging strategy is used to control the charging speed of the battery at different stages, and the battery is divided into several monitoring areas. After obtaining multiple data of the monitoring areas in real time, the multiple data are brought into the TOPSIS algorithm to calculate the relative closeness index of each monitoring area. After analyzing the importance of each monitoring area relative to the battery, a corresponding weight coefficient is generated for each monitoring area, and the relative closeness index and weight coefficient are sent to the prediction module;

[0196] Prediction module: After comprehensively weighting and calculating the relative proximity index of each monitoring area according to the weight coefficient, it predicts whether the battery will have abnormalities during the charging process, and generates corresponding management strategies based on the prediction results.

[0197] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0198] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0199] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A battery safety management method, characterized in that: The management method comprises the following steps: The management system obtains the charging restrictions of the battery at different stages through the battery database, generates several charging strategies based on the charging restrictions using a random generation tool, calculates the loss value of each charging strategy using a loss function, and outputs the optimal charging strategy for the battery after multiple iterations based on the optimization algorithm. During the battery charging process, the management system controls the charging speed of the battery at different stages through the optimal charging strategy, and divides the battery into several monitoring areas. After obtaining multiple data of the monitoring areas in real time, the multiple data are brought into the TOPSIS algorithm to calculate the relative closeness index of each monitoring area. After analyzing the importance of each monitoring area relative to the battery, a corresponding weight coefficient is generated for each monitoring area. After comprehensively weighting and calculating the relative proximity index of each monitoring area according to the weight coefficient, it is predicted whether the battery will be abnormal during the charging process. If the battery is predicted to be abnormal, a corresponding management strategy is generated based on the prediction result; The loss function is used to calculate the loss value of each charging strategy, including the following steps: Obtain the charging time, charging equipment loss index, and temperature warning frequency of each charging strategy history; The charging time, charging equipment loss index, and temperature warning frequency are input into the loss function, and the loss function calculates and outputs the loss value. The expression is: , where is the loss value, For charging time, is the charging equipment loss index, is the temperature warning frequency, , , are the adjustment coefficients for charging time, charging equipment loss index, and temperature warning frequency, respectively, and , , All are greater than 0; The charging time is the time it takes for the battery to charge from 0% to 100%; The calculation logic of the temperature warning frequency is: obtain the charging time of the battery and the number of temperature warnings during the charging process, and divide the number of temperature warnings by the charging time to obtain the temperature warning frequency; The calculation expression of the charging equipment loss index is: , where is the charging current, is the charging voltage, is the equivalent resistance of the device, It is a coefficient related to the insulation and voltage withstand capacity of the equipment.

2. A battery safety management method according to claim 1, characterized in that: After multiple iterations of the optimization algorithm, the optimal charging strategy is output for the battery, including the following steps: The loss values ​​of all charging strategies are summed up to obtain the total loss value. The proportion value of each charging strategy is obtained by dividing the loss value by the total loss value. The proportion value is mapped to the sector area of ​​the virtual roulette. The larger the proportion value of the charging strategy, the larger the sector area occupied on the virtual roulette. The virtual roulette is started to rotate. When the virtual roulette stops rotating, the charging strategy corresponding to the sector area pointed by the virtual pointer is deleted from the virtual roulette. The virtual roulette is started repeatedly. When the number of charging strategies retained on the virtual roulette is equal to the preset number threshold, the charging strategy deletion step is stopped, and the charging strategies retained on the virtual roulette are First, perform pairwise exchange operations, and then mutate the charging strategies that have completed the exchange operation. Finally, establish a set of all charging strategies that have completed the mutation operation, and recalculate the loss values ​​of all charging strategies in the set through the loss function. Repeat the above steps for cyclic iteration. When there is a charging strategy with a loss value less than or equal to the loss threshold in any output set, or the number of iterations is equal to the number threshold, it is judged that the convergence condition is met, and the management system outputs multiple sets, and integrates the multiple sets into a charging strategy set, and selects the charging strategy with the smallest loss value in the charging strategy set as the optimal charging strategy.

3. A battery safety management method according to claim 2, characterized in that: After obtaining multiple data of the monitoring area in real time, the multiple data are brought into the TOPSIS algorithm to calculate the relative closeness index of each monitoring area, including the following steps: Acquire multiple data of the monitoring area in real time, including the temperature change rate and the internal resistance change rate, that is, the indicators of the monitoring area include the temperature change rate and the internal resistance change rate; Construct a correlation matrix X with the monitoring area and multiple data, standardize the correlation matrix to obtain a standardized matrix Z, assign a weight to each indicator in the standardized matrix Z, and obtain a weighted matrix G; Based on the weighted matrix G, the positive solution and negative solution are obtained, and the distance from the monitoring area to the positive solution and the distance from the negative solution are calculated. The relative closeness index calculation expression of the monitoring area is: , where is a relatively close index. is the distance to the forward solution, is the distance to the negative solution.

4. A battery safety management method according to claim 3, characterized in that: The correlation matrix is ​​standardized to obtain the standardized matrix Z, which is expressed as: , where represents the standardized value of the jth indicator in the ith monitoring area, represents the jth index value of the i-th monitoring area, is the number of monitoring areas; Assign weights to each indicator in the standardized matrix Z so that the sum of all weights is 1. The expression is: , where is the number of indicators, is the weight of the jth indicator, then the expression of the weighted matrix G is: , where represents the weighted value of the jth standardized index in the ith monitoring area, is the weight of the jth indicator; Based on the weighted matrix G, the positive solution and negative solution are obtained, and the expression is: , where is the forward solution, is a negative solution, is the set of temperature change rates, is a set of internal resistance change rate indicators, Indicates taking the minimum value, Indicates taking the maximum value; Calculate the distance from the monitoring area to the positive solution and the negative solution. The expression is: , where is the distance to the forward solution, is the distance to the negative solution, represents the weighted value of the jth standardized index, represents the minimum value of index j in all monitoring areas, Indicates the maximum value of index j in all monitoring areas.

5. A battery safety management method according to claim 4, characterized in that: After analyzing the importance of each monitoring area relative to the battery, a corresponding weight coefficient is generated for each monitoring area, including the following steps: Obtain the number of times the battery cannot be used due to historical anomalies in the monitoring area, and the number of times the battery causes safety problems due to historical anomalies; The number of times the battery cannot be used due to historical anomalies and the number of times the battery causes safety problems due to historical anomalies are normalized, so that the value range of the number of times the battery cannot be used due to historical anomalies and the number of times the battery causes safety problems due to historical anomalies are mapped to [0,1], and the normalized value of the number of times the battery cannot be used and the normalized value of the number of times the safety problems are caused are obtained. The normalized value of the number of times the battery cannot be used and the normalized value of the number of times the safety problems are caused are summed to obtain the importance index of the monitoring area; The importance indexes of all monitoring areas are summed up to obtain the total index, and the importance index is divided by the total index to obtain the weight coefficient of the monitoring area.

6. A battery safety management method according to claim 5, characterized in that: After the relative proximity index of each monitoring area is comprehensively weighted and calculated according to the weight coefficient, it is predicted whether the battery will be abnormal during the charging process, including the following steps: After obtaining the weight coefficient and relative proximity index of each monitoring area, a comprehensive weighted calculation is performed to obtain the overall abnormality factor of the battery, which is expressed as: , where For the The weight coefficient of each monitoring area is For the The relative closeness index of the monitoring area, is the number of monitoring areas; The obtained overall abnormality factor is compared with the preset abnormality threshold. If the overall abnormality factor is less than or equal to the abnormality threshold, it is predicted that the battery will not have abnormalities during the charging process. If the overall abnormality factor is greater than the abnormality threshold, it is predicted that the battery will have abnormalities during the charging process.

7. A battery safety management method according to claim 6, characterized in that: The management system obtains the charging restrictions of the battery at different stages through the battery database, and uses a random generation tool to generate several charging strategies based on the charging restrictions, including the following steps: The management system obtains the maximum charging speed and the minimum charging speed of the battery when the battery is in the trickle charging stage; obtains the maximum charging speed and the minimum charging speed of the battery when the battery is in the fast charging stage; obtains the maximum charging speed and the minimum charging speed of the battery when the battery is in the supplementary charging stage; The maximum charging speed and the minimum charging speed of each charging stage are input as constraints into the random generation tool. Based on the constraints, the random generation tool outputs several charging strategies. Each charging strategy includes the charging speed of the trickle charging stage, the charging speed of the fast charging stage and the charging speed of the supplementary charging stage.

8. A battery safety management system, used to implement the management method according to any one of claims 1 to 7, characterized in that: It includes charging strategy generation module, charging strategy optimization output module, calculation module and prediction module; Charging strategy generation module: obtains charging constraints of batteries at different stages through the battery database, and generates several charging strategies based on the charging constraints using a random generation tool; Charging strategy optimization output module: After using the loss function to calculate the loss value of each charging strategy, the optimal charging strategy is output for the battery after multiple iterations based on the optimization algorithm; Calculation module: During the charging process, the optimal charging strategy is used to control the charging speed of the battery at different stages, and the battery is divided into several monitoring areas. After obtaining multiple data of the monitoring areas in real time, the multiple data are brought into the TOPSIS algorithm to calculate the relative closeness index of each monitoring area. After analyzing the importance of each monitoring area relative to the battery, a corresponding weight coefficient is generated for each monitoring area. Prediction module: After comprehensively weighting and calculating the relative proximity index of each monitoring area according to the weight coefficient, it predicts whether the battery will have abnormalities during the charging process, and generates corresponding management strategies based on the prediction results.

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