A BMS intelligent management system based on big data

Abstract

The present application discloses a BMS intelligent management system based on big data, which relates to the technical field of energy storage intelligent management, and solves the technical problem that it is difficult for the existing BMS management system to set reasonable charging parameters according to the usage of the battery, resulting in an increased possibility of abnormalities during the charging process of the battery, and further reducing the service life of the battery. It includes: a data acquisition module: collecting real-time detection values; a central processing module: generating optimal charging data and charging warning data according to historical charging data and standard charging data; generating the current charging strategy according to the optimal charging data; a charging control module: charging the battery according to the charging strategy; a charging monitoring module: generating a warning signal according to the real-time detection value and the charging warning data; a warning module: obtaining the warning signal and giving an alarm according to the warning signal; charging the battery according to the optimal charging parameters of the current state of the battery to increase the service life of the battery.

Description

Technical Field

[0001] This application belongs to the technical field of energy storage intelligent management, involves energy storage intelligent management technology, and specifically is a BMS intelligent management system based on big data. Background Technique

[0002] The BMS (Battery Management System) battery management system is a device or system used to monitor, control, and protect batteries; with the popularization of electric vehicles and energy storage systems, the battery management system (BMS) as a core component of power supply has received extensive attention, and its performance and reliability are crucial.

[0003] The prior art (a patent for invention with the publication number of CN117317418B) discloses a battery control method and system for a BMS management system, including executing the circuit board self-check regulations to obtain the basic parameters of the battery and dynamically updating the self-check rules; during the charging process, monitoring the battery status in real time according to the dynamically updated self-check rules to generate real-time monitoring data; combining the dynamically updated self-check rules and the real-time monitoring data to generate an intelligent response signal to the battery charging strategy; sending the real-time monitoring data to the cloud, and the cloud combines the historical performance data and the real-time monitoring data of the battery and uses a prediction algorithm to predict potential faults; this battery control method through dynamic adaptive rules, intelligent response strategies, and cloud analysis and fault prediction can thus make corresponding strategies in advance, reduce the costs and downtime caused by unexpected faults, enhance the response speed, reliability, and adaptive ability of the battery management system, improve the battery usage efficiency and lifespan, and achieve the optimization of battery performance and preventive maintenance.

[0004] The above BMS management system sets corresponding monitoring standards according to the current state of the battery, obtains monitoring data in real time, and adjusts the charging parameters of the battery according to the difference between the monitoring data and the monitoring standards; dynamically updating the monitoring standards only aims to improve the accuracy of monitoring; when the system detects abnormal data, it often means that the battery has suffered a certain degree of damage; high-precision abnormal monitoring only reduces the impact on the battery caused by each abnormal occurrence; during the usage cycle of the battery, due to unreasonable charging parameters, such abnormalities may be repeatedly triggered, resulting in the cumulative damage of the battery during charging becoming increasingly serious, and ultimately reducing the lifespan of the battery; therefore, a BMS intelligent management system based on big data is needed. Summary of the Invention

[0005] This application aims to solve at least one of the technical problems existing in the prior art; for this purpose, this application proposes a big data-based BMS intelligent management system to solve the technical problem that the existing BMS management system is difficult to set reasonable charging parameters according to the usage of the battery, resulting in an increased possibility of abnormality during the charging process of the battery, and further reducing the service life of the battery.

[0006] To achieve the above object, the first aspect of this application provides a big data-based BMS intelligent management system, including: a data acquisition module, a central processing module, a charging control module, a charging monitoring module, an early warning module, and a database;

[0007] The data acquisition module: obtains the real-time detection values of each charging parameter and each monitoring parameter in real time through the data acquisition device connected thereto;

[0008] The central processing module: obtains a number of historical charging data and standard charging data of the current battery through the connected database; generates optimal charging data and charging early warning data according to the historical charging data and the standard charging data; generates the current charging strategy according to the optimal charging data;

[0009] The charging control module: obtains the charging strategy and charges the battery according to the charging strategy;

[0010] The charging monitoring module: obtains the real-time detection values of each charging parameter and the charging early warning data, and generates an early warning signal according to the real-time detection values and the charging early warning data;

[0011] The early warning module: obtains the early warning signal and gives an alarm according to the early warning signal.

[0012] This application obtains the historical usage data of the battery to be controlled and the standard charging data of the same-signal battery, generates optimal charging data and charging early warning data according to the historical charging data and the standard charging data; generates the current charging strategy according to the optimal charging data; charges the battery according to the charging strategy; obtains the real-time detection values of each monitoring parameter in real time; generates an early warning signal according to the real-time detection values and the charging early warning data during the charging process; generates optimal charging data and charging early warning data corresponding to the current state of the battery according to the historical charging data and the standard charging data, charges the battery using the optimal charging data, and monitors each monitoring parameter during the charging process using the charging early warning data, so that the battery can be charged according to the optimal charging parameters of the current state of the battery to reduce the possibility of the battery being abnormal, and at the same time use targeted charging early warning data to monitor the charging process, improve the accuracy and timeliness of the monitoring, and further increase the service life of the battery.

[0013] Preferably, generating the optimal charging data and charging warning data according to the historical charging data and the standard charging data includes:

[0014] Extracting the optimal standard charging values corresponding to each charging parameter in the standard charging data, and the monitoring standard data at each charging duration; extracting the charging duration and the historical charging values corresponding to several charging parameters in each historical charging data;

[0015] Generating a correction coefficient according to the charging duration, several historical charging values in each historical charging data, and several optimal standard charging data in the standard charging data; generating the optimal charging data for this charging according to the correction coefficient; generating the charging warning data corresponding to this charging according to the correction coefficient.

[0016] Preferably, generating the correction coefficient according to the charging duration, several historical charging values in each historical charging data, and several optimal standard charging data in the standard charging data includes:

[0017] Numbering each charging parameter, and marking the historical charging value corresponding to each charging parameter as LCni; i is the number of the charging parameter; n is the number of the charging times, that is, the number of the historical charging data;

[0018] Through the formula Calculate the correction coefficient JXn for the nth charging; where, α1 and α2 are adjustment coefficients; the specific values are set according to expert experience; LTn is the charging duration of the nth charging; DT is the unit charging duration; ZBCni is the optimal standard charging value corresponding to the charging parameter numbered i in the nth charging, and is calculated through the formula ZBC ni =β 1i ×JX (n-1) ×ZBC (n-1)i Calculate; where, n is a positive integer greater than or equal to 2; β1i is the proportionality coefficient corresponding to the test charging parameter numbered i, and 0 < β1i < 1, and the specific coefficient is set according to expert experience; JX(n - 1) is the correction coefficient corresponding to the (n - 1)th charging; ZBC(n - 1)i is the optimal charging value corresponding to the charging parameter numbered i in the (n - 1)th charging; ZBC1i is the optimal standard charging value corresponding to each charging parameter in the standard charging data.

[0019] Preferably, generating the optimal charging data for this charging according to the correction coefficient includes:

[0020] Taking the optimal charging value corresponding to each charging parameter in the standard charging data as the optimal standard charging value for the first time, and marking it as ZBC1i;

[0021] Through the formula Calculate the optimal charging value ZCi corresponding to the charging parameter numbered i in the nth charge; where, β3i is the proportionality coefficient corresponding to the test charging parameter numbered i, and 0 < β1i < 1, and the specific coefficient is set according to expert experience; N is the total number of historical charging data;

[0022] Successively obtain the optimal charging values corresponding to each charging parameter in this charge, and integrate each charging parameter and its corresponding optimal charging value into the optimal charging data.

[0023] Preferably, the generating of the charging warning data corresponding to this charge according to the correction coefficient includes:

[0024] Obtain the total charging duration of the charging duration in each historical charging data, and obtain the monitoring standard data in the standard charging data where the charging duration matches the total charging duration; extract the maximum standard safety value corresponding to each monitoring parameter in the monitoring safety data, and mark it as ZBAj; j is the number of the monitoring parameter;

[0025] Through the formula Calculate the maximum safety value ZAj corresponding to the monitoring parameter numbered j in this charge; where, β2j is the proportionality coefficient corresponding to the monitoring parameter numbered j, and 0 < β2j < 1, and the specific coefficient is set according to expert experience; N is the total number of historical charging data;

[0026] Successively obtain the maximum safety values corresponding to each monitoring parameter in this charge, and integrate each monitoring parameter and its corresponding maximum safety value in this charge into the charging warning data.

[0027] Preferably, the generating of the current charging strategy according to the optimal charging data includes:

[0028] Obtain the current rated capacity of the battery, divide the rated capacity to obtain several power segments; obtain the upper limit power of each power segment and mark it as SD;

[0029] Extract the optimal charging value ZCi corresponding to each charging parameter in this optimal charging data;

[0030] Through the formula Calculate the capacity-optimal charging value RZCi corresponding to the test charging parameter numbered i in each power segment; where, γ is the adjustment coefficient corresponding to the power segment; ED is the current rated capacity;

[0031] Successively obtain the capacity-optimal charging values of each test charging parameter in each power segment; generate a charging strategy according to the capacity-optimal charging values of each charging parameter in each power segment.

[0032] Preferably, dividing the rated capacity to obtain several power segments includes:

[0033] Obtain the rated capacity and the set segment length; divide the rated capacity by the segment length to obtain several power segments, and use the upper limit value of each power segment as the upper limit power of the power segment; when the rated capacity is less than the upper limit power of the last power segment, use the rated capacity as the upper limit power of the last power segment.

[0034] Preferably, charging the battery according to the charging strategy includes the following steps:

[0035] Step 1: Obtain the rated capacity and the battery power of the current battery, and determine whether the battery power is equal to the rated capacity; if so, go to Step 2; if not, go to Step 5;

[0036] Step 2: Find the corresponding power segment according to the battery power, and obtain the optimal charging values of each charging parameter corresponding to the power segment;

[0037] Step 3: Adjust the current charging parameters using the optimal charging values; at the same time, generate a parameter adjustment interval time according to the battery power and the power upper limit of the corresponding battery group;

[0038] Step 4: Time according to the parameter adjustment interval time, and when the parameter interval adjustment time arrives, jump to Step 1;

[0039] Step 5: Stop charging.

[0040] Preferably, generating the parameter adjustment interval time according to the battery power and the power upper limit of the corresponding battery group includes:

[0041] Obtain the difference between the power upper limit of the corresponding battery group and the current battery power and mark it as the required charging amount; obtain the unit charging amount of each charging parameter of the battery group under the optimal charging parameter; generate the required charging time according to the required charging amount and the unit charging amount; mark the sum of the required charging time and the set delay time as the parameter adjustment interval time.

[0042] Preferably, generating a warning signal according to the real-time detection value and the charging warning data includes:

[0043] Obtain the real-time detection values of each monitoring parameter; extract the maximum safety values of each monitoring parameter corresponding in the charging warning data;

[0044] When there is a monitoring parameter among the real-time detection values of each monitoring parameter that is not within its corresponding maximum safety value; then generate a warning signal for the corresponding monitoring parameter.

[0045] Compared with the prior art, the beneficial effects of this application are:

[0046] 1. This application generates optimal charging data and charging warning data based on the historical usage data of the battery to be controlled and the standard charging data of batteries with the same signal. It generates the current charging strategy according to the optimal charging data, charges the battery according to the charging strategy, obtains the real-time detection values of each monitoring parameter in real time, generates a warning signal during the charging process based on the real-time detection values and the charging warning data, generates optimal charging data and charging warning data corresponding to the current state of the battery according to the historical charging data and the standard charging data, charges the battery using the optimal charging data, and monitors each monitoring parameter during the charging process using the charging warning data, enabling the battery to be charged according to the optimal charging parameters of the current state of the battery to reduce the possibility of battery anomalies. At the same time, the charging process is monitored using targeted charging warning data to improve the accuracy and timeliness of monitoring, thereby increasing the service life of the battery. Description of the Drawings

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0048] Figure 1 It is a schematic diagram of the principle of the BMS intelligent management system in this application;

[0049] Figure 2 It is a schematic diagram of the steps of the BMS intelligent management method in this application. Detailed Embodiments

[0050] The following will clearly and completely describe the technical solutions of the present application in combination with the embodiments. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application.

[0051] Please refer to Figure 1 - Figure 2 , the first aspect embodiment of this application provides a big data-based BMS intelligent management system, including: a data acquisition module, a central processing module, a charging control module, a charging monitoring module, a warning module, and a database;

[0052] Data acquisition module: Real-time acquisition of real-time detection values of various charging parameters and various monitoring parameters through data acquisition devices connected thereto; charging parameters are parameters that can be controlled during the charging of the energy storage battery, such as current, voltage, and power during charging; monitoring parameters are monitoring items during the charging process of the energy storage battery, such as charging current, voltage, power, and temperature of the battery; data acquisition devices include current and voltage acquisition devices, power acquisition devices, and temperature acquisition devices, etc.; real-time detection values are values obtained by real-time acquisition of charging parameters and monitoring parameters.

[0053] Central processing module: Obtain a number of historical charging data and standard charging data of the current battery through a database connected thereto; historical charging data is data recorded during the previous charging of the energy storage battery; standard charging data is the charging data that is most suitable for the current energy storage battery model obtained from testing batteries of the same model as the current energy storage battery; generate optimal charging data and charging warning data based on historical charging data and standard charging data; optimal charging data is the value of the optimal charging parameters of the current battery obtained based on the usage of the current battery and the standard charging data of the corresponding battery model; charging warning data is the warning values of various monitoring parameters during the charging process of the current battery; generate the current charging strategy based on the optimal charging data.

[0054] Charging control module: Obtain the charging strategy and charge the battery according to the charging strategy.

[0055] Charging monitoring module: Obtain the real-time detection values of various charging parameters and charging warning data, and generate warning signals based on the real-time detection values and charging warning data; warning signals are warning signals obtained based on the real-time detection values corresponding to various monitoring parameters in each stage of the battery charging process and the safety values corresponding to the charging warning data.

[0056] The warning module: Obtain warning signals and issue alarms according to the warning signals; when the real-time monitoring value of the monitoring parameter temperature during charging is higher than the corresponding maximum safety value, generate a temperature alarm signal, and relevant personnel can perform corresponding operations according to the temperature alarm signal, such as turning on the air conditioner for heat dissipation and stopping charging, etc.

[0057] In this embodiment, historical usage data of the battery to be controlled and standard charging data of batteries with the same signal are obtained. Optimal charging data and charging warning data are generated based on the historical charging data and the standard charging data. A current charging strategy is generated according to the optimal charging data. The battery is charged according to the charging strategy. The real-time detection values of each monitoring parameter are obtained in real time. A warning signal is generated based on the real-time detection values and the charging warning data during the charging process. Optimal charging data and charging warning data corresponding to the current state of the battery are generated based on the historical charging data and the standard charging data. The battery is charged using the optimal charging data. During the charging process, the charging warning data is used to monitor each monitoring parameter during the charging process, so that the battery can be charged according to the optimal charging parameters of the current state of the battery, reducing the possibility of battery anomalies. At the same time, targeted charging warning data is used to monitor the charging process, improving the accuracy and timeliness of monitoring, and thus increasing the service life of the battery.

[0058] Generating optimal charging data and charging warning data based on historical charging data and standard charging data includes:

[0059] Extract the optimal standard charging values corresponding to each charging parameter in the standard charging data. It can be understood that the optimal standard charging values of each charging parameter recorded in the standard charging data are the fastest and safest charging values obtained during the test of the corresponding model battery; and the monitoring standard data for each charging duration; the monitoring standard data is the various monitoring parameters and their corresponding maximum standard safety values during the charging process for each charging duration when charging with the optimal charging value. Extract the charging duration and the historical charging values corresponding to several charging parameters in each historical charging data; the historical charging data is the data recorded during the charging process of the corresponding battery, and historical charging data is recorded each time it is used. The charging duration is the duration of each battery charging, and the historical charging value is the charging value of each charging parameter during the corresponding charging process.

[0060] Generate a correction coefficient based on the charging duration, several historical charging values in each historical charging data, and several optimal standard charging data in the standard charging data; the correction coefficient is generated according to the usage situation of the current battery and is used to correct the optimal standard data to the optimal charging data in the current battery usage state; it can be understood as the difference between the current battery state and the state of a battery with the same usage duration of the optimal charging parameters under test conditions. Generate the optimal charging data for this charge according to the correction coefficient. Generate the charging warning data corresponding to this charge according to the correction coefficient.

[0061] Generate a correction coefficient based on the charging duration in each historical charging data, several historical charging values, and several optimal standard charging data in the standard charging data, including: number each charging parameter, and mark the historical charging value corresponding to each charging parameter as LCni; i is the number of the charging parameter; n is the number of charging times. Since each charging will generate a historical charging data, n can also be understood as the number of the historical charging data;

[0062] Calculate the correction coefficient JXn for the nth charging through the formula ; where α1 and α2 are adjustment coefficients; the specific values are set according to expert experience; LTn is the charging duration of the nth charging; DT is the unit charging duration; ZBCni is the optimal standard charging value corresponding to the charging parameter numbered i in the nth charging; in this embodiment, the correction coefficient for each use is calculated through the above formula; when the difference between the historical charging value corresponding to each charging parameter during the corresponding charging process and the optimal standard charging value in the corresponding state is greater, the more serious the impact on the battery aging. At the same time, the longer the charging duration in this state, the more serious the impact on the battery aging, and the greater the difference between the battery state after use and the state after using the optimal standard charging value, the greater the degree of correction required; the smaller the correction coefficient is set. For example, after one use, non-standard charging use causes the battery state to decline compared to the state of the battery in the optimal use state. Since battery aging will cause the corresponding battery's ability to withstand current, voltage, power, and temperature to decline, the optimal charging parameters corresponding to the charging parameters and the maximum safety values corresponding to the monitoring parameters will also decrease accordingly; the optimal charging parameters of each charging parameter and the maximum safety values corresponding to each monitoring parameter in the current state can be obtained through the generated correction coefficient;

[0063] The optimal standard charging value corresponding to the charging parameter numbered i in the nth charging is calculated through the formula ZBC ni =β 1i ×JX (n-1) ×ZBC (n-1)iCalculated; where n is a positive integer greater than or equal to 2; β1i is the proportionality coefficient corresponding to the test charging parameter numbered i, and 0 < β1i < 1, and the specific coefficient is set according to expert experience; JX(n - 1) is the correction coefficient corresponding to the (n - 1)th charge; ZBC(n - 1)i is the optimal charging value corresponding to the charging parameter numbered i in the (n - 1)th charge; ZBC1i is the optimal standard charging value corresponding to each charging parameter in the standard charging data; if the values of the charging parameters for each charge are different from the optimal standard charging values, the optimal standard charging values corresponding to each charge will be different, and they are all generated based on the previous optimal standard charging value and the difference between the previous actual charging value and the optimal standard charging value, ensuring that the optimal standard charging parameters can change according to the actual charging situation of the battery and increasing the accuracy of the correction coefficient.

[0064] In this embodiment, by generating a corresponding correction coefficient according to the actual charging situation of the battery, the optimal charging data and charging warning data generated subsequently according to the correction coefficient conform to the current usage situation of the battery.

[0065] Generating the optimal charging data for this charge according to the correction coefficient includes: taking the optimal charging values corresponding to each charging parameter in the standard charging data as the first optimal standard charging values and marking them as ZC1i;

[0066] Through the formula Calculating the optimal charging value ZCi corresponding to the charging parameter numbered i in the nth charge; where β3i is the proportionality coefficient corresponding to the test charging parameter numbered i, and 0 < β1i < 1, and the specific coefficient is set according to expert experience; N is the total number of historical charging data;

[0067] Sequentially obtaining the optimal charging values corresponding to each charging parameter in this charge, and integrating each charging parameter and its corresponding optimal charging value into the optimal charging data; actually, the optimal charging values corresponding to each charging parameter in the optimal charging data corresponding to this charge are the same as the corresponding optimal standard charging values, and different symbols are used for marking to distinguish their corresponding functions. The optimal standard charging values are used to participate in the calculation of the correction coefficient, while the optimal charging values are used to generate the next charging strategy.

[0068] Generating the charging warning data corresponding to this charge according to the correction coefficient includes: obtaining the total charging duration of the charging duration in each historical charging data, and obtaining the monitoring standard data in the standard charging data that matches the total charging duration; extracting the maximum standard safety values corresponding to each monitoring parameter in the monitoring safety data and marking them as ZBAj; j is the number of the monitoring parameter;

[0069] Through the formula Calculate the maximum safe value ZAj corresponding to the monitoring parameter with the current charging number j; where β2j is the proportionality coefficient corresponding to the monitoring parameter with the number j, and 0 < β2j < 1, and the specific coefficient is set according to expert experience; its function is to adjust the maximum safe values corresponding to different monitoring parameters; N is the total number of historical charging data;

[0070] Successively obtain the maximum safe values corresponding to each monitoring parameter during this charging, and integrate each monitoring parameter and its corresponding maximum safe value during this charging into charging warning data.

[0071] In this embodiment, the maximum safe values of each monitoring parameter under the current battery state are calculated through the above formula; when the actual charging values of each charging parameter during battery charging deviate more from the corresponding optimal charging values, the more serious the impact on battery aging, and at the same time, the longer the charging duration in this state, the more serious the impact on battery aging, and the greater the difference between the battery state after use and the state after using the optimal standard charging value. Since battery aging will cause the corresponding current, voltage, power, and temperature that the battery can withstand to decrease, the maximum safe values corresponding to the monitoring parameters will also decrease accordingly; in this embodiment, a corresponding correction coefficient is generated according to the specific use state of the battery, and the maximum standard safe value under the corresponding use duration is adjusted according to the correction coefficient to obtain the maximum safe value corresponding to the current battery state, making the maximum safe value more compatible with the current battery state, thereby improving the accuracy and timeliness of monitoring, and further increasing the service life of the battery.

[0072] Generate the current charging strategy according to the optimal charging data, including: obtaining the current rated capacity of the battery, dividing the rated capacity to obtain several power segments; obtaining the upper power of each power segment and marking it as SD; extracting the optimal charging value ZCi corresponding to each charging parameter in the current optimal charging data;

[0073] Dividing the rated capacity to obtain several power segments includes: obtaining the rated capacity and the set segment length; dividing the rated capacity by the segment length to obtain several power segments, and taking the upper limit value of each power segment as the upper power of the power segment; when the rated capacity is less than the upper power of the last power segment, taking the rated capacity as the upper power of the last power segment.

[0074] Through the formula Calculate the capacity optimal charging value RZCi corresponding to the test charging parameter with the number i in each power segment; where γ is the adjustment coefficient corresponding to the power segment; the specific value is set according to experience; ED is the current rated capacity;

[0075] Successively obtain the optimal charging values of each test charging parameter in each power segment; generate a charging strategy based on the optimal charging values of each charging parameter in each power segment.

[0076] When the remaining power of the battery is close to full charge, in order to protect the battery and avoid overcharging, the charging speed of the battery will gradually decrease; this helps to ensure the safety of the battery and extend its service life. When the remaining power of the battery is low, when the battery power is low, the internal chemical reaction is easier to proceed, thus allowing a higher charging rate. Therefore, the charging speed can be set slightly faster at this time;

[0077] In this embodiment, the actual charging values of each charging parameter are fine-tuned according to the above formula based on the rated capacity of each battery and the actual power pairs at each charging stage of the battery. When the power is low, the corresponding optimal charging value can be set closer to the set optimal charging value. When the power is high, it is slightly farther from the corresponding optimal charging value; the set optimal charging value is fine-tuned according to the power of each stage of the battery, so that the impact on the battery at each stage during the charging process is smaller, and the service life of the battery is further increased.

[0078] It should be noted that the rated capacity in this embodiment is generated according to the aging condition of the battery. The more serious the aging, the more the corresponding rated capacity is reduced compared to the rated capacity at the time of leaving the factory. It can also be calculated by the formula where YED is the original rated capacity, and δ is the adjustment coefficient, and the specific value is set according to experience.

[0079] Charge the battery according to the charging strategy, including the following steps:

[0080] Step 1: Obtain the rated capacity and the battery power of the current battery, and determine whether the battery power is equal to the rated capacity; if yes, go to Step 2; if no, go to Step 5;

[0081] Step 2: Find the corresponding power segment according to the battery power, and obtain the optimal charging values of each charging parameter corresponding to the power segment;

[0082] Step 3: Adjust the current charging parameters using the optimal charging values; at the same time, generate a parameter adjustment interval time according to the battery power and the power upper limit of the corresponding battery group;

[0083] Step 4: Time according to the parameter adjustment interval time. When the parameter interval adjustment time arrives, jump to Step 1;

[0084] Step 5: Stop charging.

[0085] Generate a parameter adjustment interval time according to the battery power and the upper limit of the power of the corresponding battery group, including: obtaining the difference between the upper limit of the power of the corresponding battery group and the current battery power and marking it as the required charging amount; obtaining the unit charging amount corresponding to the optimal charging parameter for each charging parameter of the battery group; generating the required charging time according to the required charging amount and the unit charging amount; specifically, the charging time = required charging amount / unit charging amount; marking the sum of the required charging time and the set delay time as the parameter adjustment interval time.

[0086] Generate a warning signal according to the real-time detection value and the charging warning data, including: obtaining the real-time detection value of each monitoring parameter; extracting the maximum safety value corresponding to each monitoring parameter in the charging warning data; when there is a monitoring parameter in the real-time detection values of each monitoring parameter that is not within its corresponding maximum safety value; then generate a warning signal for the corresponding monitoring parameter.

[0087] It should be noted that in this embodiment, the real-time detection value of each charging parameter is also obtained in real time, and the optimal charging parameter corresponding to each charging parameter in the corresponding charging amount segment in the charging strategy is extracted. When there is a charging parameter in the real-time monitoring values of each charging parameter that is not within its corresponding optimal charging value, a parameter adjustment signal for the corresponding parameter is generated, and the corresponding parameter is adjusted according to the parameter adjustment signal to the corresponding optimal charging value.

[0088] Some of the data in the above formula are calculated by removing the dimension and taking its numerical value. The formula is obtained by software simulation of a large amount of collected data to get a formula that is closest to the actual situation; the preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained by simulation of a large amount of data.

[0089] The working principle of this application:

[0090] This application obtains the historical usage data of the battery to be controlled and the standard charging data of the same-signal battery, generates the optimal charging data and charging warning data according to the historical charging data and the standard charging data; generates the current charging strategy according to the optimal charging data; charges the battery according to the charging strategy; obtains the real-time detection value of each monitoring parameter in real time; generates a warning signal according to the real-time detection value and the charging warning data during the charging process; generates the optimal charging data and charging warning data corresponding to the current state of the battery according to the historical charging data and the standard charging data, charges the battery using the optimal charging data, and monitors each monitoring parameter during the charging process using the charging warning data, so that the battery can be charged according to the optimal charging parameter of the current state of the battery to reduce the possibility of the battery being abnormal, and at the same time monitor the charging process using the targeted charging warning data to improve the accuracy and timeliness of the monitoring, thereby increasing the service life of the battery.

[0091] The above embodiments are only used to illustrate the technical solutions of the present application rather than to limit them. Although the present application has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A BMS intelligent management system based on big data, comprising: A data acquisition module, a central processing module, a charging control module, a charging monitoring module, an early warning module and a database; characterized in that, The data acquisition module: obtains the real-time detection values of each charging parameter and each monitoring parameter in real time through a data acquisition device connected thereto; The central processing module: obtains a number of historical charging data and standard charging data of the current battery through a connected database; generates optimal charging data and charging early warning data according to the historical charging data and the standard charging data; extracts the optimal standard charging values corresponding to each charging parameter in the standard charging data, and the monitoring standard data at each charging duration; extracts the charging duration and the historical charging values corresponding to a number of charging parameters in each historical charging data; Generates a correction coefficient according to the charging duration, a number of historical charging values in each historical charging data and a number of optimal standard charging data in the standard charging data; Obtains each correction coefficient, multiplies each correction coefficient and then corrects the optimal standard charging value to obtain the corresponding optimal charging value, integrates each optimal charging value into optimal charging data; multiplies each correction coefficient and then corrects the maximum standard charging value to obtain the corresponding maximum safety value; integrates each maximum safety value into charging early warning data; Generates the optimal charging data for this charging according to the correction coefficient, including: Taking the optimal charging values corresponding to each charging parameter in the standard charging data as the first optimal standard charging values, and marking them as ZBC1i; Through the formula the optimal charging value ZCi corresponding to the charging parameter numbered i in the nth charge is calculated; where β3i is the proportionality coefficient corresponding to the test charging parameter numbered i; N is the total number of historical charging data; JXk is the correction coefficient for the kth charge; Sequentially obtains the optimal charging values corresponding to each charging parameter in this charging, and integrates each charging parameter and its corresponding optimal charging value into optimal charging data; Generates the charging early warning data corresponding to this charging according to the correction coefficient, including: Obtains the total charging duration of the charging duration in each historical charging data, and obtains the monitoring standard data in the standard charging data that matches the charging duration and the total charging duration; extracts the maximum standard safety values corresponding to each monitoring parameter in the monitoring safety data, and marks them as ZBAj; j is the number of the monitoring parameter; Through the formula the maximum safety value ZAj corresponding to the monitoring parameter with the current charging number j is calculated; where β2j is the proportionality coefficient corresponding to the monitoring parameter with the number j; N is the total number of historical charging data; Sequentially obtains the maximum safety values corresponding to each monitoring parameter in this charging, and integrates each monitoring parameter and its corresponding maximum safety value corresponding to this charging into charging early warning data; Generates the current charging strategy according to the optimal charging data; The charging control module: obtains the charging strategy and charges the battery according to the charging strategy; The charging monitoring module: obtains the real-time detection values of each charging parameter and the charging early warning data, and generates an early warning signal according to the real-time detection values and the charging early warning data; The early warning module: obtains the early warning signal and gives an alarm according to the early warning signal.

2. The BMS intelligent management system based on big data according to claim 1, wherein The generating a correction coefficient according to the charging duration, a number of historical charging values in each historical charging data and a number of optimal standard charging data in the standard charging data includes: Numbers each charging parameter, and marks the historical charging value corresponding to each charging parameter as LCni; i is the number of the charging parameter; n is the number of the charging times; Through the formula the correction coefficient JXn for the nth charge is calculated; where, α1 and α2 are adjustment coefficients; LTn is the charging duration of the nth charge; DT is the unit charging duration; ZBCni is the optimal standard charging value corresponding to the charging parameter numbered i in the nth charge, and is calculated through the formula where, n is a positive integer greater than or equal to 2; β1i is the proportionality coefficient corresponding to the test charging parameter numbered i; JX(n - 1) is the correction coefficient corresponding to the (n - 1)th charge; ZBC(n - 1)i is the optimal charging value corresponding to the charging parameter numbered i in the (n - 1)th charge; ZBC1i is the optimal standard charging value corresponding to each charging parameter in the standard charging data.

3. An intelligent management system for BMS based on big data according to claim 1, characterized in that, The generating the current charging strategy according to the optimal charging data includes: Obtain the current rated capacity of the battery, divide the rated capacity to obtain several power segments; obtain the upper limit power of each power segment and mark it as SD; Extract the optimal charging values ZCi corresponding to each charging parameter in the current optimal charging data; Through the formula the optimal charging value RZC_i corresponding to the test charging parameter numbered i in each power segment is calculated; where γ is the adjustment coefficient corresponding to the power segment; ED is the current rated capacity; Successively obtain the capacity optimal charging values of each test charging parameter in each power segment; generate a charging strategy according to the capacity optimal charging values of each charging parameter in each power segment.

4. A BMS intelligent management system based on big data according to claim 3, characterized in that, The dividing the rated capacity to obtain several power segments includes: Obtain the rated capacity and the set segment length; divide the rated capacity by the segment length to obtain several power segments, and use the upper limit value of each power segment as the upper limit power of the power segment; when the rated capacity is less than the upper limit power of the last power segment, use the rated capacity as the upper limit power of the last power segment.

5. The intelligent management system of BMS based on big data according to claim 1, characterized in that, The charging the battery according to the charging strategy includes the following steps: Step 1: Obtain the rated capacity and the battery power of the current battery, and determine whether the battery power is equal to the rated capacity; if so, go to Step 2; if not, go to Step 5; Step 2: Find the corresponding power segment according to the battery power, and obtain the optimal charging values of each charging parameter corresponding to the power segment; Step 3: Use the optimal charging values to adjust the current charging parameters; at the same time, generate a parameter adjustment interval time according to the battery power and the power upper limit of the corresponding battery group; Step 4: Time according to the parameter adjustment interval time, and when the parameter interval adjustment time arrives, jump to Step 1; Step 5: Stop charging.

6. The intelligent management system of BMS based on big data according to claim 5, characterized in that, The generating the parameter adjustment interval time according to the battery power and the power upper limit of the corresponding battery group includes: Obtain the difference between the power upper limit of the corresponding battery group and the current battery power and mark it as the required charging amount; obtain the unit charging amount of each charging parameter of the battery group corresponding to the optimal charging parameter; generate the required charging time according to the required charging amount and the unit charging amount; mark the sum of the required charging time and the set delay time as the parameter adjustment interval time.

7. A BMS intelligent management system based on big data according to claim 1, characterized in that, The generating an early warning signal according to the real-time detection value and the charging early warning data includes: Obtain the real-time detection values of each monitoring parameter; extract the maximum safety values corresponding to each monitoring parameter in the charging early warning data; When there is a monitoring parameter among the real-time detection values of each monitoring parameter that is not within its corresponding maximum safety value; then generate an early warning signal for the corresponding monitoring parameter.

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