An intelligent monitoring system for storage batteries

Abstract

The present invention discloses an intelligent monitoring system for storage batteries, which relates to the technical field of storage battery monitoring. The present invention collects historical data of storage batteries, constructs a sample set based on the collected historical data of storage batteries, and at the same time processes the historical data of storage batteries in the sample set through a data processing method. After the processing is completed, the processed historical data of storage batteries is analyzed through a data analysis method to obtain the analyzed historical data of storage batteries. At the same time, the analyzed historical data of storage batteries is trained through a genetic algorithm to determine the normal working range of storage batteries; finally, the battery data monitored in real time is intelligently judged based on the determined normal working range of storage batteries, and the storage batteries are controlled based on the judgment result, improving the accuracy of intelligent monitoring of storage batteries.

Description

Technical Field

[0001] The present invention relates to the technical field of battery monitoring, and particularly to an intelligent battery monitoring system. Background Art

[0002] The existing on-line battery monitoring technology mainly monitors the discharge voltage and internal resistance of the battery. Although it has a certain monitoring and alarming function, it has certain limitations; the data monitoring is not comprehensive, resulting in deviations in the judgment of the battery performance.

[0003] The prior art, such as a vehicle battery monitoring system disclosed in the invention patent application with the publication number of CN116381497A, the method thereof includes: a first detection module, which includes a positive probe assembly and a negative probe assembly, for detecting the voltage and current of the battery; a second detection module, which includes at least one temperature sensor, for detecting the temperature of the battery; a processing module, which is used for determining the state information of the battery according to the voltage, the current and the temperature, and determining the recommended information for the battery based on the state information. The state information includes the actual operating state and the life state of the battery, and the recommended information is used to guide the user to use the battery; a display module, which is used for displaying the state information and the recommended information.

[0004] For the above-mentioned solution, it can be seen that the current battery monitoring system mainly monitors the discharge voltage, current and temperature of the battery. However, since the battery state is calculated according to the current data, environmental factors are ignored, and single data is prone to misjudgment, which has great limitations. The present invention integrates multiple groups of recent data of the battery to estimate the battery state, and judges the battery state data collected in real time, improving the accuracy of battery state monitoring. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent battery monitoring system, which solves the problem in the background art that the data monitoring is not comprehensive, resulting in deviations in the judgment of the battery performance.

[0006] To solve the above technical problem that the data monitoring is not comprehensive, resulting in deviations in the judgment of the battery performance, the present invention adopts the following technical solution: The present invention provides an intelligent battery monitoring method, which specifically includes the following steps:

[0007] S1. Collect the historical data of the battery, and construct a sample set based on the collected historical data of the battery;

[0008] S2. Process the historical data of the battery in the sample set through a data processing method to obtain the processed historical data of the battery;

[0009] S3. Analyze the processed historical battery data through data analysis methods to obtain the analyzed historical battery data;

[0010] S31. Calculate the battery capacity of the battery based on the processed historical battery data;

[0011] S32. Analyze the charging process and discharging process of the battery based on the processed historical battery data, and output the charging parameters and discharging parameters of the analyzed battery;

[0012] S321. Analyze the charging process of the battery;

[0013] Construct a battery charging current curve, a battery charging voltage curve, and a battery charging temperature curve based on the processed historical battery data;

[0014] The formula for constructing the battery charging current curve is as follows;

[0015] Summarize the charging current data in the most recent 10 groups of battery charging stage data, and perform fitting analysis on the selected data through Gaussian fitting;

[0016] The Gaussian fitting expression is as follows:

[0017] ;

[0018] Among them, n represents the order of Gaussian fitting, represents the height of curve i, represents the x-axis center position coordinate of curve i, represents the width of curve i, represents the fitted battery charging current curve;

[0019] The formula for constructing the battery charging voltage curve is as follows:

[0020] Set the charging process resistance R to be constant;

[0021] ;

[0022] Among them, represents the constructed battery charging voltage curve;

[0023] The formula for constructing the battery charging temperature curve is as follows:

[0024] ;

[0025] Among them, represents the constructed battery charging temperature curve, is the relationship weight between the temperature and current and voltage during the battery charging process, represents the initial temperature;

[0026] S322. Analyze the discharging process of the storage battery;

[0027] S33. Aggregate the calculated battery capacity, charging parameters, and discharging parameters to obtain the analyzed historical data of the storage battery;

[0028] S4. Train the analyzed historical data of the storage battery to determine the normal working range of the storage battery;

[0029] S5. Collect the data of the storage battery in real time, make an intelligent judgment on the real-time monitored battery data based on the determined normal working range of the storage battery, and control the storage battery based on the judgment result.

[0030] In the present invention, by collecting the historical data of the storage battery, constructing a sample set based on the collected historical data of the storage battery, and at the same time processing the historical data of the storage battery in the sample set through a data processing method, after the processing is completed, analyzing the processed historical data of the storage battery through a data analysis method to obtain the analyzed historical data of the storage battery, and at the same time training the analyzed historical data of the storage battery through a genetic algorithm to determine the normal working range of the storage battery; finally, making an intelligent judgment on the real-time monitored battery data based on the determined normal working range of the storage battery, and controlling the storage battery based on the judgment result, the accuracy of the intelligent monitoring of the storage battery is improved.

[0031] Preferably, the collecting the historical data of the storage battery and constructing a sample set based on the collected historical data of the storage battery includes the following steps:

[0032] The historical data of the storage battery includes the storage battery number, the storage battery charging stage data, and the storage battery discharging stage data;

[0033] The storage battery charging stage data includes: the storage battery charging voltage, the storage battery charging current, and the storage battery charging stage temperature;

[0034] The storage battery discharging stage data includes: the storage battery discharging voltage, the storage battery discharging current, and the storage battery discharging stage temperature.

[0035] Preferably, the processing the historical data of the storage battery in the sample set through a data processing method to obtain the processed historical data of the storage battery includes the following steps:

[0036] Process the historical data of the storage battery in the sample set through a data normalization method as follows:

[0037] ;

[0038] Wherein, represents the minimum value of the historical data of the storage battery in the sample set, represents the maximum value of the historical data of the storage battery in the sample set, represents the -th historical data of the storage battery in the sample set, represents the standardized historical data of the storage battery.

[0039] The present invention processes the historical data of the storage battery in the sample set through a data standardization method, and by means of the data standardization method, improves the reliability of the historical data of the storage battery in the sample set and ensures the accuracy of the intelligent monitoring of the storage battery.

[0040] Preferably, calculating the battery capacity of the storage battery based on the processed historical data of the storage battery includes the following steps:

[0041] Summarize the data of the charging stage of the storage battery in the most recent 10 groups of processed historical data of the storage battery;

[0042] Set that the process of the storage battery from being plugged into the charger to being fully charged and then unplugged from the charger is regarded as one charging process, and the charging process data generated during this period is regarded as the same group of charging data;

[0043] Set a charging time threshold. When the time interval between two groups of charging data is monitored to be less than the charging time threshold, the two groups of charging data are summarized into one group of charging data;

[0044] Calculate the total battery capacity based on the collected charging data;

[0045] The formula for calculating the total battery capacity is as follows:

[0046] ;

[0047] Wherein, represents the current value collected per minute, and T represents the total charging time.

[0048] Preferably, analyzing the discharging process of the storage battery includes the following steps:

[0049] Based on the processed historical data of the storage battery, construct a discharging current curve, a discharging voltage curve, and a discharging temperature curve of the storage battery;

[0050] The formula for constructing the discharging current curve of the storage battery is as follows;

[0051] Summarize the discharging current data in the discharging stage data of the most recent 10 groups of the storage battery, and perform fitting analysis on the selected data by means of Gaussian fitting;

[0052] The Gaussian fitting expression is as follows:

[0053] ;

[0054] Among them, n represents the order of Gaussian fitting, represents the height of curve i, represents the x-axis center position coordinate of curve i, represents the width of curve i, represents the battery discharge current curve after fitting;

[0055] The construction formula of the battery voltage curve is as follows:

[0056] Set the internal resistance of the battery during discharge to be constant;

[0057] ;

[0058] Among them, represents the constructed battery discharge voltage curve;

[0059] The construction formula of the battery discharge temperature curve is as follows:

[0060] ;

[0061] Among them, represents the constructed battery discharge temperature curve, is the relationship weight between temperature and current and voltage during battery discharge, represents the initial temperature.

[0062] The present invention summarizes the battery charging stage data and battery discharge stage data in the processed battery historical data of the latest 10 groups, and constructs voltage, current, and temperature curves through Gaussian fitting and the relationships among voltage, current, and temperature; at the same time, calculates the total battery capacity based on the collected charging data, provides a quantitative result for battery monitoring, and improves the effectiveness of intelligent battery monitoring.

[0063] Preferably, training the analyzed battery historical data to determine the normal working range of the battery includes the following steps:

[0064] S41. Initialize the analyzed battery historical data, perform chromosome coding on the analyzed battery historical data based on the hybrid genetic algorithm, and construct a population set;

[0065] Set each chromosome coding to represent a set of battery historical data, set the population set size, and the maximum number of iterations .

[0066] S42. Set the fitness function of the battery historical data based on the constructed population set size;

[0067] Set the fitness function:

[0068] ;

[0069] Among them, represents the fitness function of the historical data of the storage battery;

[0070] S43. Genetic operator selection operation;

[0071] Set the probability that the chromosome coding is selected as ;

[0072] ;

[0073] Among them, is the scale of the population set, is the fitness of the k-th chromosome coding, represents the k-th chromosome coding in the population set.

[0074] S44. Chromosome crossover operation;

[0075] Randomly select a crossover point among the chromosome codings in the population set, and at the same time exchange the chromosome coding segments between the crossover points to form new chromosome codings, thus forming a new population;

[0076] Eliminate the repeated chromosome codings in order and ensure that the lengths of the chromosome codings are the same to form the offspring chromosomes;

[0077] S45. Chromosome mutation operation;

[0078] Set that the mutation process is to perform substitution within the gene segments of the parental chromosomes to generate chromosome codings different from the parental ones; based on the set mutation probability mutate the parental chromosome codings to generate a new generation of offspring chromosomes;

[0079] Calculate the fitness of all offspring chromosomes. If the offspring are better than the parents, replace the parental chromosome codings; otherwise, if the parents are more excellent, it means the mutation fails, and select the parents to continue the iterative operation.

[0080] S46. Output the optimal solution to the problem;

[0081] Set the iteration threshold Y, and set that when the chromosome codings output after Y iterations remain unchanged or when the hybrid genetic algorithm reaches the set maximum number of iterations, terminate the hybrid genetic algorithm, and use the multi-group chromosome codings output at the last time as the normal working range of the storage battery.

[0082] The present invention determines the normal operating ranges of the charging and discharging stages of the storage battery by using a genetic algorithm, setting the genetic chromosome coding, and continuously iterating, thereby improving the intelligence of the storage battery monitoring.

[0083] Preferably, the real-time collection of the storage battery data, the intelligent judgment of the real-time monitored battery data based on the determined normal operating range of the storage battery, and the control of the storage battery based on the judgment result include the following steps:

[0084] Set the thresholds of each parameter of the storage battery based on the determined normal operating range of the storage battery. When the real-time collected battery data exceeds the set threshold, immediately stop the operation of the storage battery.

[0085] The present invention sets the thresholds of each parameter of the storage battery based on the determined normal operating range of the storage battery, and real-time monitors the state parameters of the storage battery, and compares the real-time monitored state parameters of the storage battery with the set thresholds, thereby improving the accuracy of the judgment of the storage battery state monitoring.

[0086] To solve the above technical problems, the present invention also adopts the following technical solution. The storage battery intelligent monitoring system is used to implement a storage battery intelligent monitoring method. The system includes: a data collection module, a data processing module, a data analysis module, a data training module, and a real-time monitoring and control module;

[0087] The data collection module is used to collect the historical data and real-time monitoring data of the storage battery;

[0088] The data processing module is used to process the collected historical data and real-time monitoring data of the storage battery;

[0089] The data analysis module is used to analyze the processed historical data of the storage battery to obtain the analyzed historical data of the storage battery;

[0090] The data training module is used to train the analyzed historical data of the storage battery to determine the normal operating range of the storage battery;

[0091] The real-time monitoring and control module is used to monitor and control the storage battery according to the determined normal operating range of the storage battery. Description of the Drawings

[0092] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0093] Figure 1The figure is a flow chart of the intelligent battery monitoring method of the present invention. DETAILED DESCRIPTION

[0094] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0095] In a specific embodiment of the present invention,

[0096] Reference Figure 1 As shown, the present invention provides a battery intelligent monitoring method, comprising the following steps:

[0097] S1. Collecting battery historical data, and building a sample set based on the collected battery historical data;

[0098] S2. Processing the battery historical data in the sample set by a data processing method to obtain processed battery historical data;

[0099] S3, analyzing the processed battery historical data by a data analysis method to obtain analyzed battery historical data;

[0100] S31, calculating the battery capacity of the battery based on the processed battery historical data;

[0101] S32, analyzing the charging process and discharging process of the battery based on the processed battery historical data, and outputting the charging parameters and discharging parameters of the battery after analysis;

[0102] S33, summarizing the calculated battery capacity, charging parameters, and discharging parameters to obtain analyzed battery history data;

[0103] S4, training the analyzed battery historical data to determine the normal working range of the battery;

[0104] S5. Collect battery data in real time, make intelligent judgments on the real-time monitored battery data based on the determined normal working range of the battery, and control the battery based on the judgment results.

[0105] Further, refer to Figure 1 As shown, collecting battery historical data and constructing a sample set based on the collected battery historical data includes the following steps:

[0106] The historical data of the battery includes the battery number, battery charging stage data and battery discharging stage data;

[0107] The data during the battery charging stage includes: the battery charging voltage, the battery charging current, and the temperature during the battery charging stage;

[0108] The data during the battery discharging stage includes: the battery discharging voltage, the battery discharging current, and the temperature during the battery discharging stage;

[0109] Further, referring to Figure 1 as shown, processing the historical battery data in the sample set through a data processing method, the processed historical battery data obtained includes the following steps:

[0110] Processing the historical battery data in the sample set through a data normalization method is as follows:

[0111] ;

[0112] wherein, represents the minimum value of the historical battery data in the sample set, represents the maximum value of the historical battery data in the sample set, represents the th historical battery data in the sample set, represents the normalized historical battery data;

[0113] Further, referring to Figure 1 as shown, calculating the battery capacity of the battery based on the processed historical battery data includes the following steps:

[0114] Summarize the data during the battery charging stage in the last 10 groups of processed historical battery data;

[0115] Set that when the battery is plugged into the charger and starts charging until it is unplugged after charging is completed as one charging process, and the charging process data generated during this period is regarded as the same group of charging data;

[0116] Further, set a charging time threshold. When the time interval between two groups of charging data is monitored to be less than the charging time threshold, the two groups of charging data are summarized into one group of charging data;

[0117] Further, calculate the total battery capacity based on the collected charging data;

[0118] The formula for calculating the total battery capacity is as follows:

[0119] ;

[0120] wherein, represents the current value collected per minute, and T represents the total charging time;

[0121] Further, referring to Figure 1 as shown, based on the processed historical data of the battery, analyze the charging process and discharging process of the battery, and output the charging parameters and discharging parameters of the battery after analysis, including the following steps:

[0122] S321. Analyze the charging process of the battery;

[0123] Based on the processed historical data of the battery, construct a battery charging current curve, a battery charging voltage curve, and a battery charging temperature curve;

[0124] The formula for constructing the battery charging current curve is as follows;

[0125] Summarize the charging current data in the most recent 10 groups of battery charging stage data, and perform fitting analysis on the selected data through Gaussian fitting;

[0126] The Gaussian fitting expression is as follows:

[0127] ;

[0128] where n represents the order of Gaussian fitting, represents the height of curve i, represents the x-axis center position coordinate of curve i, represents the width of curve i, represents the battery charging current curve after fitting;

[0129] The formula for constructing the battery charging voltage curve is as follows:

[0130] Set the charging process resistance R to be constant;

[0131] ;

[0132] where, represents the constructed battery charging voltage curve;

[0133] The formula for constructing the battery charging temperature curve is as follows:

[0134] ;

[0135] where, represents the constructed battery charging temperature curve, is the relationship weight between the temperature and the current and voltage during the battery charging process, represents the initial temperature;

[0136] S322. Analyze the discharging process of the battery;

[0137] Construct the battery discharge current curve, battery discharge voltage curve, and battery discharge temperature curve based on the processed historical battery data;

[0138] The formula for constructing the battery discharge current curve is as follows;

[0139] Summarize the discharge current data in the most recent 10 groups of battery discharge stage data, and perform fitting analysis on the selected data through Gaussian fitting;

[0140] The Gaussian fitting expression is as follows:

[0141] ;

[0142] Among them, n represents the order of Gaussian fitting, represents the height of curve i, represents the x-axis center position coordinate of curve i, represents the width of curve i, represents the battery discharge current curve after fitting;

[0143] The formula for constructing the battery voltage curve is as follows:

[0144] Set the internal resistance of the battery during discharge to be constant;

[0145] ;

[0146] Among them, represents the constructed battery discharge voltage curve;

[0147] The formula for constructing the battery discharge temperature curve is as follows:

[0148] ;

[0149] Among them, represents the constructed battery discharge temperature curve, is the relationship weight between temperature and current and voltage during battery discharge, represents the initial temperature;

[0150] Furthermore, referring to Figure 1 as shown, training the analyzed historical battery data to determine the normal working range of the battery includes the following steps:

[0151] S41. Initialize the analyzed historical battery data, perform chromosome encoding on the analyzed historical battery data based on the hybrid genetic algorithm, and construct a population set;

[0152] Set each group of chromosome codes to represent a set of historical data of storage batteries, set the scale of the population set, and the maximum number of iterations .

[0153] S42. Based on the constructed scale of the population set, set the fitness function of the historical data of storage batteries;

[0154] Set the fitness function:

[0155] ;

[0156] Among them, represents the fitness function of the historical data of storage batteries;

[0157] S43. Genetic operator selection operation;

[0158] Set the probability that the chromosome code is selected as ;

[0159] ;

[0160] Among them, is the scale of the population set, is the fitness of the k-th chromosome code, represents the k-th chromosome code in the population set.

[0161] S44. Chromosome crossover operation;

[0162] Randomly select a crossover point among the chromosome codes in the population set, and at the same time exchange the chromosome code segments between the crossover points to form a new chromosome code, thereby forming a new population;

[0163] Eliminate the repeated chromosome codes in order and ensure that the lengths of the chromosome codes are the same to form the offspring chromosomes;

[0164] S45. Chromosome mutation operation;

[0165] Set that the mutation process is to perform replacement within the gene segments of the parental chromosomes to generate chromosome codes different from the parental ones; based on the set mutation probability perform mutation on the parental chromosome codes, thereby generating a new generation of offspring chromosomes;

[0166] Calculate the fitness of all offspring chromosomes. If the offspring are better than the parents, replace the parental chromosome codes; otherwise, if the parents are more excellent, it means the mutation fails, and select the parents to continue the iterative operation.

[0167] S46. Output the optimal solution to the problem;

[0168] Set the iteration threshold Y. It is set that when the chromosome coding output after Y iterations remains unchanged or when the hybrid genetic algorithm reaches the set maximum number of iterations, the hybrid genetic algorithm is terminated, and the multiple groups of chromosome coding output in the last time are used as the normal working range of the storage battery.

[0169] Furthermore, referring to Figure 1 As shown, collect the storage battery data in real time, make an intelligent judgment on the battery data monitored in real time based on the determined normal working range of the storage battery, and control the storage battery based on the judgment result, including the following steps:

[0170] Set the threshold values of each parameter of the storage battery based on the determined normal working range of the storage battery. When the battery data collected in real time exceeds the set threshold value, immediately stop the operation of the storage battery.

[0171] In a specific embodiment, the storage battery intelligent monitoring system is used to implement a storage battery intelligent monitoring method. The system includes: a data collection module, a data processing module, a data analysis module, a data training module, and a real-time monitoring and control module.

[0172] The data collection module is used to collect the historical data of the storage battery and the real-time monitoring data of the storage battery.

[0173] The data processing module is used to process the historical data of the storage battery and the real-time monitoring data of the storage battery collected.

[0174] The data analysis module is used to analyze the historical data of the storage battery after processing to obtain the analyzed historical data of the storage battery.

[0175] The data training module is used to train the historical data of the storage battery after analysis to determine the normal working range of the storage battery.

[0176] The real-time monitoring and control module is used to monitor and control the storage battery according to the determined normal working range of the storage battery.

[0177] It should be noted that

[0178] The above content is only an example and explanation of the concept of the present invention. Those skilled in the art of this technology make various modifications or supplements to the described specific embodiments or use similar methods to replace them. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should belong to the protection scope of the present invention.

Claims

1. An intelligent monitoring system for a storage battery, characterized in that, It includes the following steps: S1. Collect the historical data of the battery, and construct a sample set based on the collected historical data of the battery; S2. Process the historical data of the battery in the sample set through a data processing method to obtain the processed historical data of the battery; S3. Analyze the processed historical data of the battery through a data analysis method to obtain the analyzed historical data of the battery; S31. Calculate the battery capacity of the battery based on the processed historical data of the battery; S32. Analyze the charging process and discharging process of the battery based on the processed historical data of the battery, and output the charging parameters and discharging parameters of the analyzed battery; S321. Analyze the charging process of the battery; Construct a battery charging current curve, a battery charging voltage curve, and a battery charging temperature curve based on the processed historical data of the battery; The construction formula of the battery charging current curve is as follows; Summarize the charging current data in the recent 10 groups of battery charging stage data, and perform fitting analysis on the selected data through Gaussian fitting; The Gaussian fitting expression is as follows: ； where n represents the order of Gaussian fitting, represents the height of curve i, represents the x-axis center position coordinate of curve i, represents the width of curve i, represents the battery charging current curve after fitting; The construction formula of the battery charging voltage curve is as follows: Set the charging process resistance R to be constant; ； Among them, represents the constructed battery charging voltage curve; The construction formula of the battery charging temperature curve is as follows: ； Among them, represents the constructed battery charging temperature curve, is the relationship weight between temperature, current and voltage during the battery charging process, represents the initial temperature; S322. Analyze the discharging process of the battery; S33. Summarize the calculated battery capacity, charging parameters, and discharging parameters to obtain the analyzed historical data of the battery; S4. Train the analyzed historical data of the battery to determine the normal working range of the battery; S5. Collect the battery data in real time, perform intelligent judgment on the real-time monitored battery data based on the determined normal working range of the battery, and control the battery based on the judgment result.

2. The intelligent battery monitoring system according to claim 1, wherein, The step of collecting the historical data of the battery and constructing a sample set based on the collected historical data of the battery includes the following steps: The historical data of the battery includes the battery number, the battery charging stage data, and the battery discharging stage data; The battery charging stage data includes: the battery charging voltage, the battery charging current, and the battery charging stage temperature; The battery discharging stage data includes: the battery discharging voltage, the battery discharging current, and the battery discharging stage temperature.

3. The intelligent battery monitoring system according to claim 1, characterized in that, The step of processing the historical data of the battery in the sample set through a data processing method to obtain the processed historical data of the battery includes the following steps: Process the historical data of the battery in the sample set through a data standardization method as follows: ； Among them, represents the minimum value of the historical data of the storage batteries in the sample set, represents the maximum value of the historical data of the storage batteries in the sample set, represents the th historical data of the storage batteries in the sample set, represents the historical data of the storage batteries after standardization.

4. An intelligent battery monitoring system according to claim 1, wherein, The step of calculating the battery capacity of the battery based on the processed historical data of the battery includes the following steps: Summarize the battery charging stage data in the recent 10 groups of processed historical data of the battery; Set that the process from when the battery is plugged into the charger to when the battery is fully charged and then unplugged from the charger is regarded as one charging process, and the charging process data generated during this period is regarded as the same group of charging data; Set a charging time threshold. When the time interval between two groups of charging data is monitored to be less than the charging time threshold, the two groups of charging data are summarized into one group of charging data; Calculate the total battery capacity based on the collected charging data; The calculation formula of the total battery capacity is as follows: ； Among them, represents the current value collected per minute, and T represents the total charging time.

5. An intelligent battery monitoring system according to claim 1, characterized in that The step of analyzing the discharging process of the battery includes the following steps: Construct the battery discharge current curve, battery discharge voltage curve, and battery discharge temperature curve based on the processed historical battery data; The formula for constructing the battery discharge current curve is as follows; Summarize the discharge current data in the most recent 10 groups of battery discharge stage data, and perform fitting analysis on the selected data through Gaussian fitting; The Gaussian fitting expression is as follows: ； where n represents the order of Gaussian fitting, represents the height of curve i, represents the x-axis center position coordinate of curve i, represents the width of curve i, represents the battery discharge current curve after fitting; The formula for constructing the battery voltage curve is as follows: Set the internal resistance of the battery during the discharge process Constant; ； Among them, represents the constructed discharge voltage curve of the storage battery; The formula for constructing the battery discharge temperature curve is as follows: ； Among them, represents the constructed battery discharge temperature curve, is the relationship weight between temperature, current and voltage during the battery discharge process, represents the initial temperature.

6. The intelligent battery monitoring system according to claim 5, characterized in that, Training the analyzed historical battery data to determine the normal operating range of the battery includes the following steps: S41. Initialize the analyzed historical battery data, perform chromosome coding on the analyzed historical battery data based on the hybrid genetic algorithm, and construct a population set; Set each group of chromosome coding to represent a set of historical data of storage batteries, and set the scale of the population set and the maximum number of iterations ; S42. Set the fitness function of the historical battery data based on the scale of the constructed population set; Set the fitness function: ； Among them, The fitness function representing the historical data of the storage battery; S43. Genetic operator selection operation; Set the probability that the chromosome encoding is selected to be ; ； Among them, is the scale of the population set, is the fitness of the k-th chromosome encoding, represents the k-th chromosome encoding in the population set; S44. Chromosome crossover operation; Randomly select a crossover point in the chromosome coding of the population set, and simultaneously exchange the chromosome coding segments between the crossover points to form new chromosome coding, thereby forming a new population; Sequentially eliminate the duplicate chromosome coding and ensure that the chromosome coding lengths are the same to form offspring chromosomes; S45. Chromosome mutation operation; The set mutation process is to perform replacement within the gene segments of the parental chromosome to generate a chromosome encoding different from that of the parental generation; based on the set mutation probability Mutate the chromosome encoding of the parental generation to generate a new generation of offspring chromosomes; Calculate the fitness of all offspring chromosomes. If the offspring are superior to the parent, replace the parent chromosome coding. Otherwise, if the parent is more excellent, it means the mutation fails, and select the parent to continue the iterative operation; S46. Output the optimal solution to the problem; Set the iteration threshold Y. Set that when the chromosome coding remains unchanged after Y iterations or when the hybrid genetic algorithm reaches the set maximum number of iterations, terminate the hybrid genetic algorithm, and use the multiple groups of chromosome coding output last time as the normal operating range of the battery.

7. An intelligent battery monitoring system according to claim 1, wherein The real-time collection of battery data, the intelligent judgment of the real-time monitored battery data based on the determined normal operating range of the battery, and the control of the battery based on the judgment result include the following steps: Set the thresholds for each parameter of the battery based on the determined normal operating range of the battery. When the real-time collected battery data exceeds the set threshold, immediately stop the operation of the battery.

8. An intelligent battery monitoring system implementing the one described in any one of claims 1-7, characterized in that, Include: Data collection module, data processing module, data analysis module, data training module, and real-time monitoring and control module; The data collection module is used to collect the historical battery data and the real-time monitored battery data; The data processing module is used to process the collected historical battery data and the real-time monitored battery data; The data analysis module is used to analyze the processed historical battery data to obtain the analyzed historical battery data; The data training module is used to train the analyzed historical battery data to determine the normal operating range of the battery; The real-time monitoring and control module is used to monitor and control the battery according to the determined normal operating range of the battery.

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