BMS system based on communication power remote communication

Abstract

The application discloses a BMS system based on communication power remote communication, relates to the battery management technical field, and solves the technical problem that in the prior art, a battery pack is used to supply power for the BMS system, once the battery pack is abnormal, the remote control center cannot acquire the battery pack state, and the stable work of the battery pack is affected; the application sets a communication power for supporting the work of the BMS system, can maintain the stable work of the remote communication module while ensuring that the BMS system is not powered off; and the maintenance time of the communication power is predicted based on a data transmission curve, so that the service life of the communication power can be shortened due to frequent charging; the application calculates the time that the electric energy can maintain first, and then counts the data transmission amount of the communication power within the charging duration on this basis; redundancy is set based on the data transmission amount, and the maintainable time of the appropriate residual electric energy is set, so that the case that the electric energy is not maintained and the data transmission stability is affected can be avoided.

Description

Technical Field

[0001] This application belongs to the field of battery management and relates to battery data acquisition and transmission technology, specifically a BMS system based on remote communication of a communication power supply. Background Technology

[0002] Currently, a BMS system consists of components including a battery management chip, an embedded microprocessor, and embedded software. The BMS system primarily uses real-time collected battery status data to protect the battery pack. Furthermore, with the development of the new energy sector, the BMS system needs stable remote communication capabilities to promptly transmit the collected battery status data to the control center.

[0003] Battery Management System (BMS) requires the collection of a large amount of battery status data, and frequent data communication reduces the real-time nature of the data, posing a risk to battery protection and making it prone to situations such as complete battery discharge or overcharging. Current BMS systems primarily draw power from the battery pack; if the battery pack malfunctions, the BMS system cannot function properly, and the remote control center cannot accurately obtain the battery pack's status, making timely intervention difficult.

[0004] This application provides a BMS system based on remote communication via communication power supply to solve the above-mentioned technical problems. Summary of the Invention

[0005] This application aims to solve at least one of the technical problems existing in the prior art; to this end, this application proposes a BMS system based on remote communication power supply to solve the technical problem in the prior art where the BMS system is powered by a battery pack, and once the battery pack malfunctions, the remote control center cannot obtain the status of the battery pack, which affects the stable operation of the battery pack.

[0006] To achieve the above objectives, the first aspect of this application provides a BMS system based on remote communication via a communication power supply, including a data analysis module, and a data acquisition module and a remote communication module connected thereto. It also includes communication power supplies; The communication power supply is used to support the remote communication module in data transmission and to provide additional power to the battery pack based on the charging time. The data analysis module is used to predict and obtain the data transmission curve, and plan the power replenishment time of the communication power supply based on the data transmission curve.

[0007] In existing technologies, the power supply for a Battery Management System (BMS) primarily comes from the battery packs it manages. Power is drawn from the battery packs, and the collected battery status data is transmitted to a remote control center via a remote communication module for remote monitoring of the battery packs. Obviously, if the battery pack malfunctions, such as being instantly destroyed by external force, it cannot supply power to the BMS system, thus preventing the timely transmission of battery status data to the control center. Furthermore, power supply from the battery pack is prone to instability, which affects the timeliness of battery status data transmission. To address these issues, this application provides a communication power supply for the BMS system. This power supply can store a certain amount of electrical energy, ensuring uninterrupted power supply to the BMS system while maintaining stable operation of the remote communication module.

[0008] In addition to the existing components, the BMS system in this application includes a communication power supply embedded within the BMS system. This power supply can be charged via the battery pack, ensuring that even if the battery pack malfunctions, the collected battery status data can be promptly transmitted to the control center. Furthermore, since the communication power supply can store a certain amount of electrical energy, the overall power supply is more stable than when directly powered by the battery pack.

[0009] Preferably, the prediction of the data transmission curve includes: The data analysis module acquires historical transmission data; this historical transmission data consists of data records that have been transmitted to the control center via the remote communication module. After denoising the historical transmission data, a data transmission curve is established based on the amount of data transmitted at each transmission time in the historical transmission data.

[0010] The communication power supply in this application is itself an energy storage device. Frequent recharging of the energy storage device will affect its service life. This application determines the amount of data to be transmitted in future time periods based on historical transmission data, estimates the electrical energy required for data transmission based on the data volume, and determines when recharging is needed by combining the electrical energy stored in the communication power supply itself. This can avoid the impact of untimely recharging on the remote transmission of battery status data, and can also prevent the communication power supply from being frequently charged, thus affecting its service life.

[0011] Preferably, the step of establishing and obtaining the data transmission curve based on the amount of data transmitted at each transmission time of historical transmission data includes: The historical transmission data is divided into several data segments according to a set period; the set period may be one hour, fifteen minutes, or one minute. The maximum data volume corresponding to each data segment is statistically obtained. The data transmission curve within the analysis period is established with the transmission time corresponding to each data segment as the independent variable and the maximum data volume as the dependent variable. The analysis period is one month, one week, or one day.

[0012] When the battery pack operates in a stable environment, the amount of battery status data that needs to be transmitted is relatively fixed. Therefore, this application predicts the amount of data that needs to be transmitted in the future based on the historical transmission data of the BMS system, and determines how long the current remaining power of the communication power supply can last based on the predicted amount of data.

[0013] This application segments historical transmitted data and selects the maximum data volume from each segment, associating it with a set period. Selecting the maximum data volume is a redundancy design, preventing the data volume from exceeding the average value due to various factors and thus maintaining the data for an excessively long period.

[0014] Of course, other methods can also be used to predict the amount of data that the battery pack managed by the BMS system will need to transmit in the future, such as deep learning algorithms and statistical models. When predicting the future amount of data, external influencing factors, such as environmental factors and operating conditions, can also be introduced.

[0015] Preferably, the power replenishment time for planning the communication power supply based on the data transmission curve includes: Determine the reliable power level of the communication power supply; where the reliable power level is the difference between the current power level and the guaranteed power level, and the guaranteed power level is sufficient to transmit the amount of data for a set period. The maintenance time corresponding to reliable power is calculated based on the data transmission curve, and the power replenishment time is determined by combining the current moment with the maintenance time.

[0016] After obtaining the data transmission curve, it is necessary to determine how long the current reliable power of the communication power supply can sustain operations in the future. First, the reliable power supply is converted to the amount of data transmitted. Based on the conversion result, the sustaining time is obtained from the data transmission curve. Adding the current moment to the sustaining time determines when the communication power supply needs to be recharged.

[0017] Preferably, the step of calculating the maintenance time corresponding to reliable power based on the data transmission curve includes: The data transmission curve is labeled F(t), and the reliable power is labeled KD; The first time is obtained by calculating using the formula α×∫F(t)≤KD; where α is the conversion coefficient between the data transmission amount and the electrical energy consumed; and ∫ is the integral sign, with the initial time of integration being the current time. When the communication power supply starts charging at the first moment, the data transmission amount CSL corresponding to the charging time of the communication power supply is obtained according to the data transmission curve; the maintenance time is calculated by the formula α×(∫F(t)+CSL)≤KD; where ∫ is the integral sign, which is consistent with the integration time when it is obtained at the first moment.

[0018] Considering that recharging the communication power supply also takes time, if the power supply's capacity is insufficient to support the data transmission volume for the corresponding time period during recharging, it will also lead to untimely data transmission of battery status data. Therefore, this application first calculates the first time based on the reliable power supply and data transmission curve, theoretically allowing the communication power supply to be charged at the first time. However, this application first assumes that the communication power supply charges at the first time to obtain the charging time, and then obtains the data transmission volume required within the charging time based on the data transmission curve. This data transmission volume is then used as the data transmission content to be supported by the reliable power supply, and recalculated into the formula to obtain the maintenance time. This maintenance time is definitely shorter than the first time, and this technical solution ensures as much stable transmission of battery status data as possible during the communication power supply charging period.

[0019] Preferably, the charging time of the communication power supply is obtained from historical charging data, including: Extract the average time from historical charging data to charge the communication power supply from its guaranteed charge level to full charge. The charging influencing factors are predicted based on environmental data, and the product of the charging influencing factors and the average duration is used as the charging duration; the charging time is determined based on the first time and the charging duration.

[0020] In this application, the charging time of the communication power supply needs to be calculated based on historical data. Specifically, it involves calculating the average time required for the communication power supply to charge from its initial capacity to full power (or 90% SOC) during historical charging processes. This average time is then added to the initial charging time to obtain the final charging time. However, considering that charging efficiency can be affected by the charging environment, a charging influence factor is introduced to calculate the final charging time, thereby improving the accuracy of the charging time.

[0021] Preferably, the step of predicting and obtaining charging influencing factors based on environmental data includes: Extract environmental data predicted when the communication power supply begins charging immediately; Environmental data is integrated into the impact analysis model to obtain the charging impact factor; the charging impact factor is the ratio of the actual charging time to the average charging time, and the impact analysis model is constructed based on an artificial intelligence model.

[0022] Charging impact factors are derived from environmental data on the charging duration of the communication power supply. This environmental data is preprocessed and then input into the impact analysis model to obtain the corresponding charging impact factors. This impact analysis module is built based on artificial intelligence models, such as deep convolutional neural network models and backpropagation neural network models, which can improve the accuracy and efficiency of obtaining charging impact factors.

[0023] Compared with the prior art, the beneficial effects of this application are: 1. This application provides a communication power supply to support the operation of the BMS system. This communication power supply can store a certain amount of electrical energy, ensuring that the BMS system is powered on without interruption and maintaining the stable operation of the remote communication module. Moreover, a data transmission curve is established based on historical transmission data, and the maintenance time of the communication power supply is predicted based on the data transmission curve, which can avoid frequent charging of the communication power supply and shorten its service life.

[0024] 2. This application incorporates redundancy settings when calculating the duration of the communication power supply. Specifically, it first calculates the duration that the power can sustain, then calculates the amount of data transmitted within the charging time of the communication power supply. Based on this data transmission amount, redundancy settings are implemented to ensure an appropriate remaining duration of power to prevent power shortages from affecting the stability of data transmission. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a schematic diagram of the system principle according to an embodiment of this application; Figure 2 This is a schematic diagram illustrating the acquisition of the data transmission curve in an embodiment of this application; Figure 3 This is a schematic diagram illustrating the acquisition of charging time in an embodiment of this application. Detailed Implementation

[0027] The technical solutions of this application will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0028] Please see Figures 1-3 The first aspect of this application provides a BMS system based on remote communication via a communication power supply, including a data analysis module, a data acquisition module and a remote communication module connected thereto; it also includes a communication power supply; the communication power supply is used to support the remote communication module in data transmission and to provide recharging via a battery pack based on recharging time; The data analysis module is used to predict and obtain the data transmission curve, and plan the power replenishment time of the communication power supply based on the data transmission curve.

[0029] In this embodiment, the communication power supply is primarily for storing electrical energy. This energy is used to transmit untransmitted battery status data to the control center in the event of a battery pack malfunction, preventing the remote communication module from being interrupted due to battery pack failure. Simultaneously, it ensures the stability of the remote communication module, preventing power instability from affecting data transmission. Therefore, the communication power supply can be selected from existing options, ensuring compatibility with the BMS system.

[0030] In this embodiment, the data analysis module collects battery status data from the battery pack through the data acquisition module. After data preprocessing, the data is sent to a remote control center via the remote communication module, enabling the control center to comprehensively monitor the battery pack. The communication power supply primarily supports data transmission via the remote communication module; however, it can also support the operation of the BMS system when necessary. Furthermore, the communication power supply requires charging and can draw power from the battery pack to ensure that its energy supply is always sufficient for the remote transmission of battery status data.

[0031] Communication power supplies are mostly independent power sources, similar in nature to traditional batteries. They need to be kept running normally while avoiding frequent charging to maximize lifespan and reduce costs. Therefore, it's necessary to determine when to recharge the communication power supply based on the amount of battery status data transmitted.

[0032] Next, the power replenishment process for the communication power supply in this embodiment will be described in detail.

[0033] The electrical energy in a communication power supply is primarily used for data transmission; therefore, its energy consumption is closely related to the amount of data transmitted. Thus, the future energy requirements of a BMS system can be predicted based on its historical transmission data.

[0034] In a preferred embodiment, the data transmission curve of the BMS system is predicted based on historical transmission data, and the data transmission demand is the predicted transmission volume of the BMS system at various future times. Specifically, this includes: The data analysis module acquires historical transmission data; this historical transmission data consists of data records that have been transmitted to the control center via the remote communication module. After denoising the historical transmission data, a data transmission curve is established based on the amount of data transmitted at each transmission time in the historical transmission data.

[0035] The data analysis module extracts stored historical transmission data, which records the amount of data transmitted by the remote communication module and the corresponding time points. After denoising the historical transmission data, a data transmission curve is established.

[0036] In a preferred embodiment, historical transmission data can be divided according to a set period. Specifically, the data volume for each set period is statistically analyzed, and the average data volume for each set period over the past days is taken as the data volume required to be transmitted in each set period over future days. A data transmission curve is established with transmission time as the independent variable and the required data volume as the dependent variable. Generally, the data volume corresponding to battery status data generated by the battery pack does not fluctuate significantly. This data transmission curve can accurately predict the amount of data that the remote communication module needs to transmit.

[0037] However, special circumstances may affect the amount of data generated by the battery pack. If the data volume is lower than usual, it is feasible to extract the future data volume using the aforementioned data transmission curve. If the data volume is higher than usual, the actual data volume will be significantly greater than the predicted data volume, affecting the calculation of the communication power supply charging time. Therefore, in another preferred embodiment, the following solution is provided: The step of establishing a data transmission curve based on the amount of data transmitted at each transmission time of historical transmission data includes: dividing the historical transmission data into several data segments according to a set period; wherein the set period includes one hour, fifteen minutes, or one minute; statistically obtaining the maximum data volume corresponding to each data segment, and establishing a data transmission curve within the analysis period with the transmission time corresponding to each data segment as the independent variable and the maximum data volume as the dependent variable; wherein the analysis period is one month, one week, or one day.

[0038] First, divide the historical data transmission into several data segments according to a set period. For example, if the set period is one hour, then each hour corresponds to one data segment. Calculate the data volume corresponding to each data segment in each set period, and select the maximum data volume. Establish a data transmission curve based on the maximum data volume corresponding to each set period within the analysis period (daily). For example: The historical transmission data for one year (365 days) is divided into several data segments according to each hour. Each time period in a day corresponds to 365 data segments. The maximum data volume is determined from the 365 data segments corresponding to each hour. That is, each hour in a day corresponds to a maximum data volume, thereby establishing a data transmission curve for one day (analysis period).

[0039] Compared to the previous embodiment, this embodiment selects the maximum data volume to construct the data transmission curve. Specifically, it filters the maximum data volume transmitted by the remote communication module in each time period from historical transmission data, avoiding the problem of inaccurate prediction of the data transmission curve due to sudden changes in data volume under special circumstances. Of course, the maximum data volume can also be directly set based on experience to construct the data transmission curve.

[0040] Once the data transmission curve is determined, the power replenishment time for the communication power supply can be planned, including: Determine the reliable power level of the communication power supply; where the reliable power level is the difference between the current power level and the guaranteed power level, and the guaranteed power level is sufficient to transmit the amount of data for a set period. The maintenance time corresponding to reliable power is calculated based on the data transmission curve, and the power replenishment time is determined by combining the current moment with the maintenance time.

[0041] The guaranteed power level refers to the minimum power level of the communication power supply. When the remaining power of the communication power supply is less than the guaranteed power level, it needs to be replenished. The guaranteed power level can be set based on the data volume of a set period or an analysis period. Its purpose is to ensure that the current data of the battery pack can be transmitted to the control center when the battery pack malfunctions and cannot charge the communication power supply.

[0042] The reliable power level is calculated based on the current power level of the communication power supply. Using this reliable power level as the result, the duration of data transmission that can be sustained is determined by the data volume in each time period of the data transmission curve. This gives the sustainment time. Example: The data transmission curve is labeled F(t), and the reliable power is labeled KD; The upper limit of integration is calculated using the formula α×∫F(t)≤KD, and the lower limit of integration is the current moment. Therefore, the upper limit of integration is the duration until which the reliable power supply can be maintained, i.e., the maintenance time. α is the conversion coefficient between data transmission volume and energy consumption, set based on experience or statistical data. The communication power supply can then be charged at the maintenance time calculated by this formula.

[0043] Theoretically, charging the communication power supply during the duration calculated by the above formula can ensure the data transmission volume is maintained while avoiding frequent charging of the communication power supply. However, the communication power supply also requires a certain amount of time to recharge. If the power supply cannot meet the data transmission requirements during the recharging time, data transmission instability will still occur.

[0044] Therefore, this application ensures the stability of data transmission by setting redundancy measures, specifically including: The data transmission curve is labeled F(t), and the reliable power is labeled KD; The first time is obtained by calculating using the formula α×∫F(t)≤KD; where α is the conversion coefficient between the data transmission amount and the electrical energy consumed; and ∫ is the integral sign, with the initial time of integration being the current time. When the communication power supply starts charging at the first moment, the data transmission amount CSL corresponding to the charging time of the communication power supply is obtained according to the data transmission curve; the maintenance time is calculated by the formula α×(∫F(t)+CSL)≤KD; where ∫ is the integral sign, which is consistent with the integration time when it is obtained at the first moment.

[0045] First, the initial time is calculated using a formula. Assuming the communication power supply begins charging at the initial time, the charging time is calculated, and the data transmission volume during this charging time is calculated using the data transmission curve. This data transmission volume is then substituted into the formula, and the sustaining time is calculated using the formula α×(∫F(t)+CSL)≤KD. This scheme effectively uses the data transmission volume during the charging time as a redundancy value, that is, shortens the original sustaining time by using this data transmission volume, ensuring that the communication power supply will not experience power shortage during the charging process.

[0046] The charging time of the communication power supply in this application can be calculated based on historical charging data, including: Extract the average time from historical charging data to charge the communication power supply from its guaranteed charge level to full charge. The charging influencing factors are predicted based on environmental data, and the product of the charging influencing factors and the average duration is used as the charging duration; the charging time is determined based on the first time and the charging duration.

[0047] The average time it takes for the communication power supply to charge from its guaranteed capacity to full capacity is calculated from historical charging data. Considering that the charging environment will affect the charging time, a charging influence factor is introduced to calculate the charging time. The charging time is obtained by adding the previous first time and the charging time.

[0048] The impact analysis model can be constructed based on artificial intelligence algorithms, establishing a mapping relationship between environmental data and charging impact factors during training. By inputting the environmental data corresponding to the aforementioned charging duration into the impact analysis model, the corresponding charging impact factors can be obtained. Multiplying this charging impact factor by the average duration yields the charging duration, which in turn allows for the calculation of the total charging time. Charging the communication power supply within this timeframe ensures data transmission stability and extends the power supply's lifespan.

[0049] Some of the data in the above formula are calculated by removing dimensions and taking their numerical values. The formula is the closest to the real situation obtained by software simulation of a large amount of collected data. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0050] The above embodiments are only used to illustrate the technical methods of this application and are not intended to limit it. Although this application has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of this application without departing from the spirit and scope of the technical methods of this application.

Claims

1. A BMS system based on remote communication via a communication power supply, comprising a data analysis module, and a data acquisition module and a remote communication module connected thereto; characterized in that, It also includes communication power supplies; The communication power supply is used to support the remote communication module in data transmission and to provide additional power to the battery pack based on the charging time. The data analysis module is used to predict and acquire the data transmission curve, and to plan the power replenishment time of the communication power supply based on the data transmission curve. The prediction of the data transmission curve includes: The data analysis module acquires historical transmission data; this historical transmission data consists of data records that have been transmitted to the control center via the remote communication module. After denoising the historical transmission data, a data transmission curve is established based on the amount of data transmitted at each transmission time in the historical transmission data. The power replenishment time for the communication power supply is planned based on the data transmission curve, including: Determine the reliable power level of the communication power supply; where the reliable power level is the difference between the current power level and the guaranteed power level, and the guaranteed power level is sufficient to transmit the amount of data for a set period. The maintenance time corresponding to reliable power is calculated based on the data transmission curve, and the current moment is combined with the maintenance time to determine the power replenishment time. The calculation of the maintenance time corresponding to reliable power based on the data transmission curve includes: The data transmission curve is labeled F(t), and the reliable power is labeled KD; The first time is obtained by calculating using the formula α×∫F(t)≤KD; where α is the conversion coefficient between the data transmission amount and the electrical energy consumed; and ∫ is the integral sign, with the initial time of integration being the current time. When the communication power supply starts charging at the first moment, the data transmission amount CSL corresponding to the charging time of the communication power supply is obtained according to the data transmission curve; the maintenance time is calculated by the formula α×(∫F(t)+CSL)≤KD; where ∫ is the integral sign, which is consistent with the integration time when it is obtained at the first moment.

2. The BMS system based on remote communication power supply according to claim 1, characterized in that, The step of establishing and obtaining data transmission curves based on the amount of data transmitted at each transmission time in historical data transmission includes: The historical transmission data is divided into several data segments according to a set period; the set period may be one hour, fifteen minutes, or one minute. The maximum data volume corresponding to each data segment is statistically obtained. The data transmission curve within the analysis period is established with the transmission time corresponding to each data segment as the independent variable and the maximum data volume as the dependent variable. The analysis period is one month, one week, or one day.

3. The BMS system based on remote communication power supply according to claim 1, characterized in that, The charging time of the communication power supply is obtained from historical charging data, including: Extract the average time from historical charging data to charge the communication power supply from its guaranteed charge level to full charge. The charging influencing factors are predicted based on environmental data, and the product of the charging influencing factors and the average duration is used as the charging duration; the charging time is determined based on the first time and the charging duration.

4. A BMS system based on remote communication power supply according to claim 3, characterized in that, The method of obtaining charging impact factors based on environmental data prediction includes: Extract environmental data predicted when the communication power supply begins charging immediately; Environmental data is integrated into the impact analysis model to obtain the charging impact factor; the charging impact factor is the ratio of the actual charging time to the average charging time, and the impact analysis model is constructed based on an artificial intelligence model.

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