BMS intelligent management system based on big data

Abstract

The invention discloses a BMS intelligent management system based on big data, relates to the technical field of energy storage intelligent management, and solves the technical problems that an existing BMS management system is difficult to set reasonable charging parameters according to the use condition of a battery, so that the possibility of abnormality of the battery in the charging process is increased, and the service life of the battery is shortened. Comprising a data acquisition module for acquiring a real-time detection value; the central processing module is used for generating optimal charging data and charging early warning data according to the historical charging data and the standard charging data; generating a current charging strategy according to the optimal charging data; the charging control module is used for charging the battery according to the charging strategy; the charging monitoring module is used for generating an early warning signal according to the real-time detection numerical value and the charging early warning data; the early warning module is used for acquiring an early warning signal and giving an alarm according to the early warning signal; the battery is charged according to the optimal charging parameter of the current state of the battery, and the service life of the battery is prolonged.

Description

Technical Field

[0001] The present application belongs to the field of energy storage intelligent management technology, and relates to energy storage intelligent management technology, specifically a BMS intelligent management system based on big data. Background Art

[0002] BMS (Battery Management System) is a device or system used to monitor, control and protect batteries. With the popularity of electric vehicles and energy storage systems, battery management systems (BMS) have received widespread attention as a core component of power supply, among which its performance and reliability are crucial.

[0003] The prior art (invention patent with announcement number CN117317418B) discloses a battery control method and system for a BMS management system, including executing circuit board self-test regulations to obtain basic battery parameters and dynamically updating self-test rules; during the charging process, monitoring the battery status in real time according to the dynamically updated self-test rules to generate real-time monitoring data; combining the dynamically updated self-test rules and real-time monitoring data to generate an intelligent response signal to adjust the battery charging strategy; sending real-time monitoring data to the cloud, which uses a prediction algorithm to predict potential faults based on the battery's historical performance data and real-time monitoring data; the battery control method uses dynamic adaptive rules, intelligent response strategies, and cloud analysis and fault prediction to prepare response strategies in advance, reduce costs and downtime caused by unexpected faults, enhance the response speed, reliability and adaptability of the battery management system, improve battery efficiency and life, and achieve battery performance optimization and preventive maintenance.

[0004] The above-mentioned BMS management system sets the corresponding monitoring standard according to the current status of the battery, obtains monitoring data in real time, and adjusts the battery charging parameters according to the difference between the monitoring data and the monitoring standard; dynamically updates the monitoring standard only to improve the accuracy of monitoring; and when the system detects abnormal data, it often means that the battery has suffered a certain degree of damage; high-precision abnormality monitoring will only reduce the impact of each abnormality on the battery; during the battery's service life, unreasonable charging parameters may repeatedly trigger such abnormalities, resulting in increasingly serious accumulated damage to the battery during the charging process, and ultimately shortening the battery's service life; therefore, a BMS intelligent management system based on big data is needed. Summary of the invention

[0005] The present application aims to solve at least one of the technical problems existing in the prior art; to this end, the present application proposes a BMS intelligent management system based on big data, which is used 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 battery abnormalities during the charging process, which in turn leads to a reduction in the battery's service life.

[0006] To achieve the above-mentioned purpose, the first aspect of the present application provides a BMS intelligent management system based on big data, 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 is used to obtain the real-time detection values ​​of various charging parameters and various monitoring parameters in real time through the data acquisition device connected thereto;

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

[0009] The charging control module is used to obtain a charging strategy and charge the battery according to the charging strategy.

[0010] The charging monitoring module: obtains the real-time detection values ​​of various charging parameters and charging warning data, and generates a warning signal according to the real-time detection values ​​and charging warning data;

[0011] The early warning module is used to obtain early warning signals and to issue an alarm according to the early warning signals.

[0012] The present application obtains historical usage data of the battery to be controlled and standard charging data of the same signal battery, generates optimal charging data and charging warning data according to the historical charging data and the standard charging data; generates a current charging strategy according to the optimal charging data; charges the battery according to the charging strategy; obtains real-time detection values ​​of various monitoring parameters in real time; generates a warning signal according to the real-time detection values ​​and the charging warning data during the charging process; 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 uses the charging warning data to monitor various monitoring parameters during the charging process, 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 battery abnormalities, and at the same time uses targeted charging warning data to monitor the charging process, improves the accuracy and timeliness of monitoring, and thereby increases the service life of the battery.

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

[0014] Extract the optimal standard charging value corresponding to each charging parameter in the standard charging data, as well as the monitoring standard data under each charging duration; extract the charging duration and historical charging values ​​corresponding to several charging parameters in each historical charging data;

[0015] A correction coefficient is generated according to the charging time in each historical charging data, several historical charging values ​​and several optimal standard charging data in the standard charging data; the optimal charging data for this charging is generated according to the correction coefficient; and the charging warning data corresponding to this charging is generated according to the correction coefficient.

[0016] Preferably, the generating of the correction coefficient according to the charging duration in each historical charging data, a plurality of historical charging values ​​and a plurality of optimum standard charging data in the standard charging data comprises:

[0017] Each charging parameter is numbered, and the historical charging value corresponding to each charging parameter is marked 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] By formula The correction coefficient JXn of the nth charging is calculated; where α1 and α2 are adjustment coefficients; the specific values ​​are set according to expert experience; LTn is the charging time of the nth charging; DT is the unit charging time; ZBCni is the optimal standard charging value corresponding to the charging parameter numbered i in the nth charging, and the formula ZBC ni =β 1i ×JX (n-1) ×ZBC (n-1)i Calculated; wherein n is a positive integer greater than or equal to 2; β1i is the proportional 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-1th charging; ZBC(n-1)i is the optimal charging value corresponding to the charging parameter numbered i in the n-1th charging; ZBC1i is the optimal standard charging value corresponding to each charging parameter in the standard charging data.

[0019] Preferably, the step of generating the most suitable charging data for this charging according to the correction coefficient includes:

[0020] The optimum charging value corresponding to each charging parameter in the standard charging data is used as the first optimum standard charging value and marked as ZBC1i;

[0021] By formula The optimal charging value ZCi corresponding to the charging parameter numbered i in the nth charging is calculated; wherein β3i is the proportional 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] The optimal charging value corresponding to each charging parameter in this charging is obtained in sequence, and each charging parameter and its corresponding optimal charging value are integrated into the optimal charging data.

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

[0024] Obtain the total charging time of the charging time in each historical charging data, and obtain the monitoring standard data in which the charging time in the standard charging data matches the total charging time; 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] By formula Calculate the maximum safety value ZAj corresponding to the monitoring parameter numbered j in this charging; where β2j is the proportional 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] The maximum safety value of each monitoring parameter corresponding to this charging is obtained in turn, and each monitoring parameter and its corresponding maximum safety value corresponding to this charging are integrated into charging warning data.

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

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

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

[0030] By formula The optimal charging value RZCi of the capacity 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;

[0031] The optimal charging value of the capacity of each test charging parameter in each power segment is obtained in sequence; and a charging strategy is generated according to the optimal charging value of the capacity of each charging parameter in each power segment.

[0032] Preferably, the rated capacity is divided into several power segments, including:

[0033] Obtain the rated capacity and the set segment length; divide the rated capacity by the segment length to obtain a number of 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 comprises the following steps:

[0035] Step 1: Obtain the rated capacity and the current battery power of the battery, and determine whether the battery power is equal to the rated capacity; if yes, proceed to step 2; if no, proceed to step 5;

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

[0037] Step 3: Use the optimal charging value to adjust the current charging parameters; and generate a parameter adjustment interval time according to the battery power and the power upper limit of the corresponding battery group;

[0038] Step 4: Count down the time according to the parameter adjustment interval. When the parameter adjustment interval time is reached, jump to step 1.

[0039] Step 5: Stop charging.

[0040] Preferably, the step of generating a 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 upper limit of the power of the corresponding battery group and the current battery power and mark it as the required charging capacity; obtain the unit charging capacity under the optimal charging parameters corresponding to each charging parameter of the battery group; generate the required charging time according to the required charging capacity and the unit charging capacity; mark the sum of the required charging time and the set delay time as the parameter adjustment interval time.

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

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

[0044] When there is a monitoring parameter in the real-time detection value of each monitoring parameter that is not within its corresponding maximum safety value, an early warning signal of the corresponding monitoring parameter is generated.

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

[0046] 1. The present application obtains historical usage data of the battery to be controlled and standard charging data of the same signal battery, generates optimal charging data and charging warning data according to the historical charging data and the standard charging data; generates a current charging strategy according to the optimal charging data; charges the battery according to the charging strategy; obtains real-time detection values ​​of various monitoring parameters in real time; generates warning signals according to the real-time detection values ​​and the charging warning data during the charging process; 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 uses the charging warning data to monitor various monitoring parameters during the charging process, 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 battery abnormalities, and at the same time uses targeted charging warning data to monitor the charging process, improves the accuracy and timeliness of monitoring, and thereby increases the service life of the battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

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

[0049] Figure 2 This is a schematic diagram of the steps of the BMS intelligent management method for this application. DETAILED DESCRIPTION

[0050] The technical solution of the present application will be described clearly and completely below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0051] See also Figure 1-Figure 2 , the first embodiment of the present application provides a BMS intelligent management system based on big data, including: a data acquisition module, a central processing module, a charging control module, a charging monitoring module, an early warning module and a database;

[0052] Data acquisition module: The real-time detection values ​​of various charging parameters and monitoring parameters are obtained in real time through the data acquisition equipment connected to it; the charging parameters are the parameters that can be controlled when the energy storage battery is charged, such as the current, voltage and power during charging; the monitoring parameters are the monitoring items of the energy storage battery during the charging process, such as the charging current, voltage, power and temperature of the battery; the data acquisition equipment includes current, voltage acquisition equipment, power acquisition equipment and temperature acquisition equipment; the real-time detection values ​​are the values ​​obtained by real-time acquisition of the charging parameters and monitoring parameters;

[0053] Central processing module: obtains several historical charging data and standard charging data of the current battery through the database connected to it; the historical charging data is the data recorded when the energy storage battery was previously charged; the standard charging data is the charging data most suitable for the energy storage battery of the same model as the current energy storage battery obtained in the test; generates the optimal charging data and charging warning data based on the historical charging data and standard charging data; the optimal charging data is the value of the optimal charging parameter of the current battery obtained based on the current battery usage and the standard charging data of the corresponding model of battery; the charging warning data is the warning value of each monitoring parameter of the current battery during the charging process; generates the current charging strategy based on the optimal charging data;

[0054] Charging control module: obtains the charging strategy and charges the battery according to the charging strategy;

[0055] Charging monitoring module: obtains the real-time detection values ​​and charging warning data of each charging parameter, and generates a warning signal based on the real-time detection values ​​and charging warning data; the warning signal is a warning signal obtained based on the real-time detection values ​​corresponding to each monitoring parameter in each stage of the battery charging process and the safety values ​​corresponding to the charging warning data;

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

[0057] This embodiment obtains historical usage data of the battery to be controlled and standard charging data of the same signal battery, generates optimal charging data and charging warning data according to the historical charging data and the standard charging data; generates a current charging strategy according to the optimal charging data; charges the battery according to the charging strategy; obtains real-time detection values ​​of various monitoring parameters in real time; generates a warning signal according to the real-time detection values ​​and the charging warning data during the charging process; 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 uses the charging warning data to monitor various monitoring parameters during the charging process, 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 battery abnormalities, and at the same time uses targeted charging warning data to monitor the charging process, improves the accuracy and timeliness of monitoring, and thereby increases the service life of the battery.

[0058] Generate optimal charging data and charging warning data based on historical charging data and standard charging data, including:

[0059] Extract the optimum standard charging value corresponding to each charging parameter in the standard charging data. It can be understood that the optimum standard charging value of each charging parameter recorded in the standard charging data is the fastest and safest charging value obtained by the corresponding model battery during the test process; and the monitoring standard data under each charging time; the monitoring standard data is the various monitoring parameters and their corresponding maximum standard safety values ​​in the charging process under each charging time when charging is performed at the optimal charging value; extract the charging time 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 the historical charging data will be recorded once each time it is used. The charging time is the time when the battery is charged each time, and the historical charging value is the charging value of each charging parameter in the corresponding charging process;

[0060] A correction coefficient is generated based on the charging time in each historical charging data, several historical charging values ​​and several optimal standard charging data in the standard charging data; the correction coefficient is generated based on the current battery usage and is used to correct the optimal standard data to the optimal charging data under the current battery usage status; it can be understood as the difference between the current battery status and the status of the battery under the test condition with the same usage time using the optimal charging parameters; the optimal charging data for this charging is generated based on the correction coefficient; the charging warning data corresponding to this charging is generated based on the correction coefficient.

[0061] Generating correction coefficients according to the charging duration in each historical charging data, several historical charging values ​​and several optimal standard charging data in the standard charging data, including: 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 charging times, because each charging will parameterize a historical charging data, so n can also be understood as the number of the historical charging data;

[0062] By formula The correction coefficient JXn of the nth charging is calculated; wherein α1 and α2 are adjustment coefficients; the specific values ​​are set according to expert experience; LTn is the charging time of the nth charging; DT is the unit charging time; ZBCni is the optimal standard charging value corresponding to the charging parameter numbered i in the nth charging; this embodiment calculates the correction coefficient for each use through the above formula; when the difference between the historical charging value corresponding to each charging parameter in the corresponding charging process and the optimal standard charging value in the corresponding state is larger, the aging effect on the battery is more serious, and at the same time, the longer the charging time in this state, the more serious the aging effect on the battery is. 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, improper charging and use causes the battery state to deteriorate compared to the state of the battery in the optimal use state. Due to battery aging, the current, voltage, power and temperature that the corresponding battery can withstand will decrease, and the optimal charging parameters corresponding to the charging parameters and the maximum safety values ​​corresponding to the monitoring parameters will also decrease accordingly; the generated correction coefficient can be used to obtain the optimal charging parameters of each charging parameter in the current state and the maximum safety values ​​corresponding to each monitoring parameter;

[0063] The optimum standard charging value corresponding to the charging parameter numbered i in the nth charging is calculated by the formula ZBC ni =β 1i ×JX (n-1) ×ZBC (n-1)iCalculated; wherein n is a positive integer greater than or equal to 2; β1i is the proportional 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-1th charging; ZBC(n-1)i is the optimal charging value corresponding to the charging parameter numbered i in the n-1th charging; ZBC1i is the optimal standard charging value corresponding to each charging parameter in the standard charging data; if the value of the charging parameter of each charging is different from the optimal standard charging value, the optimal standard charging value corresponding to each charging will be different, which is generated according to the difference between the previous optimal standard charging value and the previous actual charging value and the optimal standard charging value, ensuring that each optimal standard charging parameter can be changed according to the actual charging situation of the battery, thereby increasing the accuracy of the correction coefficient.

[0064] This embodiment generates a corresponding correction coefficient according to the actual charging condition of the battery, so that the optimal charging data and charging warning data subsequently generated according to the correction coefficient are in accordance with the current usage condition of the battery.

[0065] Generating the most suitable charging data for this charging according to the correction coefficient, including: taking the most suitable charging values ​​corresponding to the corresponding charging parameters in the standard charging data as the first most suitable standard charging values, and marking them as ZC1i;

[0066] By formula The optimal charging value ZCi corresponding to the charging parameter numbered i in the nth charging is calculated; wherein β3i is the proportional 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] The optimal charging value corresponding to each charging parameter in this charging is obtained in turn, and each charging parameter and its corresponding optimal charging value are integrated into the optimal charging data; in fact, the optimal charging value corresponding to each charging parameter in the optimal charging data corresponding to this charging is consistent with the corresponding optimal standard charging value, and different symbols are used to mark them in order to distinguish their corresponding functions. The optimal standard charging value is used to participate in the calculation of the correction coefficient, and the optimal charging value is used to generate the next charging strategy.

[0068] Generate charging warning data corresponding to this charging according to the correction coefficient, including: obtaining the total charging time of each historical charging data, obtaining the monitoring standard data of the charging time matching the total charging time in the standard charging data; extracting the maximum standard safety value corresponding to each monitoring parameter in the monitoring safety data, and marking it as ZBAj; j is the number of the monitoring parameter;

[0069] By formula The maximum safety value ZAj corresponding to the monitoring parameter numbered j in this charging is calculated; where β2j is the proportional coefficient corresponding to the monitoring parameter numbered j, and 0<β2j<1, and the specific coefficient is set according to expert experience; its function is to adjust the maximum safety value corresponding to different monitoring parameters; N is the total number of historical charging data;

[0070] The maximum safety value of each monitoring parameter corresponding to this charging is obtained in turn, and each monitoring parameter and its corresponding maximum safety value corresponding to this charging are integrated into charging warning data.

[0071] This embodiment calculates the maximum safety value of each monitoring parameter under the current battery state through the above formula; when the actual charging value of each charging parameter of the battery during the charging process is larger than the corresponding optimal charging value, the more serious the impact on the aging of the battery is, and the longer the charging time in this state is, the more serious the impact on the aging of the battery is, and the greater the difference between the battery state after use and the state after using the optimal standard charging value is. Due to battery aging, the current, voltage, power and temperature that the corresponding battery can withstand will decrease, and the maximum safety value corresponding to the monitoring parameter will also decrease accordingly; this embodiment generates a corresponding correction coefficient according to the specific use state of the battery, and adjusts the maximum standard safety value under the corresponding use time according to the correction coefficient to obtain the maximum safety value corresponding to the current battery state, so that the maximum safety value is more matched with the current battery state, thereby improving the accuracy and timeliness of monitoring, and thus 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 into several power segments; obtaining the upper limit of each power segment and marking it as SD; extracting the optimal charging value ZCi corresponding to each charging parameter in the optimal charging data;

[0073] The rated capacity is divided into several power segments, including: obtaining the rated capacity and a 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 limit power of the power segment; when the rated capacity is less than the upper limit power of the last power segment, taking the rated capacity as the upper limit power of the last power segment.

[0074] By formula The optimal charging value RZCi of the capacity corresponding to the test charging parameter numbered i in each power segment is calculated; wherein 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] The optimal charging value of the capacity of each test charging parameter in each power segment is obtained in sequence; and a charging strategy is generated according to the optimal charging value of the capacity of each charging parameter in each power segment.

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

[0077] This embodiment uses the above formula to fine-tune the actual charging value of each charging parameter according to the rated capacity of each battery and the actual power of the battery in each charging stage. 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, the corresponding optimal charging value is slightly reduced. The set optimal charging value is fine-tuned according to the power of the battery in each stage, so that the impact of each stage on the battery during the charging process is smaller, thereby further increasing the battery life.

[0078] It is worth noting that the rated capacity in this embodiment is generated according to the aging of the battery. The more serious the aging, the more the corresponding rated capacity is reduced compared to the rated capacity at the factory. It can also be calculated by the formula It is calculated as follows: YED is the original rated capacity, δ is the adjustment coefficient, and the specific value is set based on experience.

[0079] Charging the battery according to the charging strategy includes the following steps:

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

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

[0082] Step 3: Use the optimal charging value to adjust the current charging parameters; and generate a parameter adjustment interval time according to the battery power and the power upper limit of the corresponding battery group;

[0083] Step 4: Count down the time according to the parameter adjustment interval. When the parameter adjustment interval time is reached, jump to step 1.

[0084] Step 5: Stop charging.

[0085] Generate a parameter adjustment interval time according to the battery power and the power upper limit of the corresponding battery group, including: 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 capacity; obtain the unit charging capacity under the optimal charging parameters corresponding to each charging parameter of the battery group; generate the required charging time according to the required charging capacity and the unit charging capacity; specifically, charging time = required charging capacity / unit charging capacity; mark the sum of the required charging time and the set delay time as the parameter adjustment interval time.

[0086] Generate a warning signal based on real-time detection values ​​and charging warning data, including: obtaining the real-time detection values ​​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; generate a warning signal for the corresponding monitoring parameter.

[0087] It is worth noting that in this embodiment, the real-time detection values ​​of various charging parameters are obtained in real time to extract the optimal charging parameters corresponding to each charging parameter in the corresponding charging amount segment in the charging strategy. When there are charging parameters in the real-time monitoring values ​​of various charging parameters that are not within their corresponding optimal charging values, a parameter adjustment signal of the corresponding parameter is generated, and the corresponding parameter is adjusted according to the parameter adjustment signal to the corresponding optimal charging value.

[0088] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0089] How this application works:

[0090] The present application obtains historical usage data of the battery to be controlled and standard charging data of the same signal battery, generates optimal charging data and charging warning data according to the historical charging data and the standard charging data; generates a current charging strategy according to the optimal charging data; charges the battery according to the charging strategy; obtains real-time detection values ​​of various monitoring parameters in real time; generates a warning signal according to the real-time detection values ​​and the charging warning data during the charging process; 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 uses the charging warning data to monitor various monitoring parameters during the charging process, 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 battery abnormalities, and at the same time uses targeted charging warning data to monitor the charging process, improves the accuracy and timeliness of monitoring, and thereby increases the service life of the battery.

[0091] The above embodiments are only used to illustrate the technical method of the present application and are not intended to limit it. Although the present application has been described in detail with reference to the preferred embodiments, a person of ordinary skill in the art should understand that the technical method of the present application may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present application.

Claims

1. A BMS intelligent management system based on big data, comprising: Data acquisition module, central processing module, charging control module, charging monitoring module, early warning module and database; characterized in that, The data acquisition module is used to obtain the real-time detection values ​​of various charging parameters and various monitoring parameters in real time through the data acquisition device connected thereto; The central processing module: obtains several historical charging data and standard charging data of the current battery through the database connected thereto; generates optimal charging data and charging warning data according to the historical charging data and standard charging data; generates the current charging strategy according to the optimal charging data; The charging control module is used to obtain a charging strategy and charge the battery according to the charging strategy. The charging monitoring module: obtains the real-time detection values ​​of various charging parameters and charging warning data, and generates a warning signal according to the real-time detection values ​​and charging warning data; The early warning module is used to obtain early warning signals and to issue an alarm according to the early warning signals.

2. A BMS intelligent management system based on big data according to claim 1, characterized in that: The generating of the optimal charging data and the charging warning data according to the historical charging data and the standard charging data includes: Extract the optimal standard charging value corresponding to each charging parameter in the standard charging data, as well as the monitoring standard data under each charging duration; extract the charging duration and historical charging values ​​corresponding to several charging parameters in each historical charging data; A correction coefficient is generated according to the charging time in each historical charging data, several historical charging values ​​and several optimal standard charging data in the standard charging data; the optimal charging data for this charging is generated according to the correction coefficient; and the charging warning data corresponding to this charging is generated according to the correction coefficient.

3. A BMS intelligent management system based on big data according to claim 2, characterized in that: The generating of the correction coefficient according to the charging duration in each historical charging data, a plurality of historical charging values ​​and a plurality of optimum standard charging data in the standard charging data includes: Each charging parameter is numbered, and the historical charging value corresponding to each charging parameter is marked as LCni; i is the number of the charging parameter; n is the number of the charging times; By formula The correction coefficient JXn of the nth charging is calculated; where α1 and α2 are adjustment coefficients; LTn is the charging time of the nth charging; DT is the unit charging time; ZBCni is the optimal standard charging value corresponding to the charging parameter numbered i in the nth charging. ni =β 1i ×JX (n-1) ×ZBC (n-1)i Calculated; wherein n is a positive integer greater than or equal to 2; β1i is the proportional coefficient corresponding to the test charging parameter numbered i; JX(n-1) is the correction coefficient corresponding to the n-1th charging; ZBC(n-1)i is the optimal charging value corresponding to the charging parameter numbered i in the n-1th charging; ZBC1i is the optimal standard charging value corresponding to each charging parameter in the standard charging data.

4. A BMS intelligent management system based on big data according to claim 3, characterized in that: The generating of the optimal charging data for this charging according to the correction coefficient includes: The optimum charging value corresponding to each charging parameter in the standard charging data is used as the first optimum standard charging value and marked as ZBC1i; By formula The optimum charging value ZCi corresponding to the charging parameter numbered i in the nth charging is calculated; wherein β3i is the proportional coefficient corresponding to the test charging parameter numbered i; N is the total number of historical charging data; The optimal charging value corresponding to each charging parameter in this charging is obtained in sequence, and each charging parameter and its corresponding optimal charging value are integrated into the optimal charging data.

5. The BMS intelligent management system based on big data according to claim 3 is characterized in that: The generating of charging warning data corresponding to the current charging according to the correction coefficient includes: Obtain the total charging time of the charging time in each historical charging data, and obtain the monitoring standard data in which the charging time in the standard charging data matches the total charging time; 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; By formula The maximum safety value ZAj corresponding to the monitoring parameter numbered j in this charging is calculated; where β2j is the proportional coefficient corresponding to the monitoring parameter numbered j; N is the total number of historical charging data; The maximum safety value of each monitoring parameter corresponding to this charging is obtained in turn, and each monitoring parameter and its corresponding maximum safety value corresponding to this charging are integrated into charging warning data.

6. The BMS intelligent management system based on big data according to claim 1 is characterized in that: The generating of the current charging strategy according to the optimal charging data includes: Obtain the current rated capacity of the battery, divide the rated capacity into several power segments; obtain the upper limit of each power segment and mark it as SD; Extract the optimal charging value ZCi corresponding to each charging parameter in the optimal charging data of this time; By formula The optimal charging value RZCi of the capacity 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; The optimal charging value of the capacity of each test charging parameter in each power segment is obtained in sequence; and a charging strategy is generated according to the optimal charging value of the capacity of each charging parameter in each power segment.

7. The BMS intelligent management system based on big data according to claim 6 is characterized in that: The rated capacity is divided into several power segments, including: Obtain the rated capacity and the set segment length; divide the rated capacity by the segment length to obtain a number of 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.

8. The BMS intelligent management system based on big data according to claim 1 is characterized in that: The step of charging the battery according to the charging strategy comprises the following steps: Step 1: Obtain the rated capacity and the current battery power of the battery, and determine whether the battery power is equal to the rated capacity; if yes, proceed to step 2; if no, proceed to step 5; Step 2: Find the corresponding power segment according to the battery power, and obtain the optimal charging value of each charging parameter corresponding to the power segment; Step 3: Use the optimal charging value to adjust the current charging parameters; and generate a parameter adjustment interval time according to the battery power and the power upper limit of the corresponding battery group; Step 4: Count down the time according to the parameter adjustment interval. When the parameter adjustment interval time is reached, jump to step 1. Step 5: Stop charging.

9. The BMS intelligent management system based on big data according to claim 8 is characterized in that: The step of generating a parameter according to the battery power and the upper limit of the power of the corresponding battery group to adjust the interval time includes: Obtain the difference between the upper limit of the power of the corresponding battery group and the current battery power and mark it as the required charging capacity; obtain the unit charging capacity under the optimal charging parameters corresponding to each charging parameter of the battery group; generate the required charging time according to the required charging capacity and the unit charging capacity; mark the sum of the required charging time and the set delay time as the parameter adjustment interval time.

10. The BMS intelligent management system based on big data according to claim 1, characterized in that: The generating of the warning signal according to the real-time detection value and the charging warning data includes: Obtain the real-time detection value of each monitoring parameter; extract 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 value of each monitoring parameter that is not within its corresponding maximum safety value, an early warning signal of the corresponding monitoring parameter is generated.

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