BMS Battery Remote Monitoring and Data Analysis System Based on Cloud Platform

Abstract

The present invention discloses a BMS battery remote monitoring and data analysis system based on a cloud platform, which relates to the technical field of remote monitoring and is used to solve the problems in the prior art that due to the lack of detailed data monitoring, it is impossible to deeply analyze the performance and loss of each part of the battery, thereby affecting the overall health assessment and management of the battery. Moreover, the battery state of the battery is usually a fixed and static value, ignoring the dynamic impact of the management frequency and power loss of different functional parts on the battery state; by combining the management frequency and power loss, the battery state of each functional part can be accurately allocated, the battery usage strategy can be optimized according to the actual situation of different modules, and unnecessary energy loss can be reduced. Unnecessary interference with the battery can be reduced. Through an automated monitoring and adjustment mechanism, manual intervention in battery management can be reduced, making battery management more intelligent and efficient.

Description

Technical Field

[0001] The present invention relates to the technical field of remote monitoring, and more specifically, to a BMS battery remote monitoring and data analysis system based on a cloud platform. Background Art

[0002] The BMS battery remote monitoring technology based on the cloud platform realizes the remote monitoring and intelligent management of the battery management system through technologies such as the Internet of Things, cloud computing, and big data analysis. It can help users monitor the operating status of the battery in real time, optimize battery usage, and improve the safety and efficiency of the battery system, and is widely used in fields such as electric vehicles, energy storage systems, and smart grids. With the development of technology, future BMS remote monitoring systems will be more intelligent and precise, and will play a greater role in all walks of life.

[0003] The existing technology has the following deficiencies:

[0004] In the existing technology, there is a lack of detailed data monitoring, resulting in the inability to deeply analyze the performance and loss of each part of the battery, thereby affecting the overall battery health assessment and management. And the battery state of the battery is usually a fixed and static value, ignoring the dynamic impact of the management frequency and power loss of different functional parts on the battery state. There is a lack of a mechanism to adjust the monitoring strategy based on the actual usage of the battery, and it is impossible to automatically adjust the monitoring frequency according to the real-time state of the battery, so as to more effectively monitor the battery state and discover potential problems in advance.

[0005] In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the existing technology, an embodiment of the present invention provides a BMS battery remote monitoring and data analysis system based on a cloud platform to solve the problems raised in the above background art.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A BMS battery remote monitoring and data analysis system based on a cloud platform includes a data monitoring module, a coefficient determination module, a battery state calculation module, and a monitoring adjustment module, and the modules are signal-connected to each other;

[0009] Data monitoring module: Monitor the management times and power consumption of the battery monitoring part, the battery protection part, and the battery balancing part;

[0010] Coefficient determination module: Obtain the management frequency of each functional part and combine the power consumption to obtain the battery state distribution coefficient of each functional part;

[0011] Battery status calculation module: Use the battery status distribution coefficients of each functional part to determine the weight values of the battery status of each functional part by the priority chart method, and obtain the overall battery status by weighting.

[0012] Monitoring and regulation module: Determine the monitoring duration level based on the overall battery status of the battery and the battery discharge duration.

[0013] In a preferred embodiment, the data monitoring module is used to monitor the management times and power losses of the battery monitoring part, the battery protection part, and the battery balancing part:

[0014] Determine each functional part of the BMS battery, namely the battery monitoring part, the battery protection part, and the battery balancing part, manage and partition the power losses of each functional part, and perform separate management and power loss monitoring respectively, and record the management times and power loss data of each functional part.

[0015] In a preferred embodiment, the coefficient determination module is used to obtain the management frequency of each functional part and combine the power loss to obtain the battery status distribution coefficient of each functional part:

[0016] Calculate the management frequency of each functional part per unit time, which is calculated and expressed as the ratio of the management times of each functional part to the total management times. The formula is: , where the total management times is the sum of the management times of each functional part;

[0017] Evaluate the power losses of each functional part. The specific evaluation method is as follows:

[0018] Use linear normalization to map the power losses of each functional part per unit time to the interval [0, 1]. The formula is: , where: is the value after power loss normalization; is the power loss value of each functional part per unit time, is the maximum power loss value of each functional part per unit time; is the minimum power loss value of each functional part per unit time.

[0019] Add the management frequency and the normalized power loss to obtain the battery status distribution coefficient of each functional part. Mark the management frequency and the normalized power loss as a and b respectively. Then the calculation formula for the battery status distribution coefficient of each functional part is Int(i) = a + b. In the formula, Int(i) is the battery status distribution coefficient of each functional part, and i represents the serial number of each functional part;

[0020] After calculating the battery status distribution coefficients of each functional part, compare the magnitudes of the battery status distribution coefficients of each functional part.

[0021] In a preferred embodiment, the battery state calculation module uses the battery state distribution coefficients of each functional part to determine the weight values of the battery states of each functional part by the preference chart method, and obtains the overall battery state by weighting:

[0022] Calculate the battery state distribution coefficients of each functional part according to the normalized management frequency per unit time of each functional part and the power loss of each functional part, and assign weights to the battery state distribution coefficients of each functional part respectively by the preference chart method to determine the weight values of the battery states of each functional part;

[0023] Perform weighted average calculation on the battery states of each functional part according to the weight values of the battery state distribution coefficients of each functional part to obtain the overall battery state, and the formula is expressed as: , where: , are the battery state values of the Q, W, and E regions respectively, , , are the weight values corresponding to the Q, W, and E regions respectively, where: the Q, W, and E regions correspond to the three functional parts in this embodiment one by one. After sorting each functional part according to the size of the battery state scoring coefficient, they correspond to the Q, W, and E regions in descending order.

[0024] In a preferred embodiment, determine the monitoring duration level based on the overall battery state of the battery and the battery discharge duration:

[0025] After the monitoring and adjustment module receives the overall battery state of the BMS battery and the battery discharge duration, define the overall battery state of the BMS battery and the battery discharge duration as input variables, and divide them into different fuzzy sets respectively;

[0026] Define the monitoring duration level as the output variable and divide it into a fuzzy set;

[0027] Formulate fuzzy rules to describe the influence of the definition of the overall battery state of the BMS battery and the battery discharge duration on the monitoring duration level;

[0028] Perform fuzzy inference according to the fuzzy rules to determine the monitoring duration level of the BMS battery.

[0029] The technical effects and advantages of the BMS battery remote monitoring and data analysis system based on the cloud platform of the present invention:

[0030] By combining the management frequency and power loss, the battery status of each functional part can be accurately allocated. Through this accurate battery status allocation, the battery usage strategy can be optimized according to the actual situation of different modules, reducing unnecessary energy loss. Ensure that the monitoring frequency and method are reasonably adjusted at different discharge stages of the battery. This means that when the battery status is relatively large, the system can provide more frequent monitoring to avoid over-discharge or loss, while when the battery status is relatively small, the system can reduce the monitoring frequency, thereby reducing unnecessary interference with the battery. Through the automated monitoring and adjustment mechanism, the manual intervention in battery management can be reduced, making the battery management more intelligent and efficient. Brief Description of the Drawings

[0031] Figure 1 It is a schematic structural diagram of the BMS battery remote monitoring and data analysis system based on the cloud platform of the present invention. Detailed Embodiments

[0032] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention. Embodiment

[0033] The present invention discloses a BMS battery remote monitoring and data analysis system based on the cloud platform, as Figure 1 shown, including: a data monitoring module, a coefficient determination module, a battery status calculation module, and a monitoring and adjustment module, and the modules are signal-connected to each other.

[0034] The functions of each module are specifically as follows:

[0035] Data monitoring module: Monitor the management times and power loss of the battery monitoring part, battery protection part, and battery equalization part;

[0036] Coefficient determination module: Obtain the management frequency of each functional part and combine the power loss to obtain the battery status allocation coefficient of each functional part;

[0037] Battery status calculation module: Use the battery status allocation coefficient of each functional part to determine the weight value of the battery status of each functional part by the preference chart method, and obtain the overall battery status by weighting;

[0038] Monitoring and adjustment module: Determine the monitoring duration level according to the overall battery and battery discharge duration of the battery.

[0039] The data monitoring module is used to monitor the management times and power consumption of the battery monitoring part, the battery protection part, and the battery balancing part. The specific process is as follows:

[0040] Step A1, determine the functional parts of the BMS battery, namely the battery monitoring part, the battery protection part, and the battery balancing part.

[0041] Step A2, manage and partition the power consumption of each functional part, and perform separate management and power consumption monitoring respectively.

[0042] Step A3, record the management times and power consumption data of each functional part.

[0043] It should be noted that the battery monitoring part, the battery protection part, and the battery balancing part of the BMS battery are used to ensure the safe, efficient, and stable operation of the battery pack. The functions of each functional part are as follows: Battery monitoring part: Usually responsible for monitoring the basic state of the battery, such as voltage, current, temperature, etc., to ensure that the battery is within the safe operating range; Battery protection part: Mainly used to protect the battery, prevent abnormal conditions such as overcharging, over-discharging, and overheating, and control through a protection circuit or management system; Battery balancing part: In the case of multiple battery cells in parallel or series, by balancing the charge and discharge states of the batteries, ensure that the power of each battery cell is consistent, and improve the use efficiency and life of the entire battery pack.

[0044] The coefficient determination module is used to obtain the management frequency of each functional part and combine the power consumption to obtain the battery state distribution coefficient of each functional part. The specific working process is as follows:

[0045] Step B1, after the coefficient determination module receives the management times of each functional part, calculate the management frequency of each functional part. The specific calculation method is as follows:

[0046] Calculate the management frequency of each functional part per unit time, and calculate and express it as the ratio of the management times of each functional part to the total management times. The formula can be expressed as: , where the total management times is the sum of the management times of each functional part. Using the above formula can ensure that the frequency is between [0, 1].

[0047] Step B2, evaluate the power consumption of each functional part. The specific evaluation method is as follows:

[0048] Use linear normalization to map the power consumption per unit time of each functional part to the interval [0, 1]. The formula is: , where: is the value after power consumption normalization; is the power consumption value per unit time of each functional part, is the maximum power consumption value per unit time of each functional part; The minimum value of the power loss of each functional part per unit time.

[0049] It should be noted that the normalization formula given in this embodiment is a general formula and does not involve the power loss information obtained by the data monitoring module of this application. The process of normalizing the power loss information is the same as the general formula and will not be elaborated here.

[0050] Step B3: Add the management frequency and the normalized power loss to obtain the battery state distribution coefficient of each functional part. Mark the management frequency and the normalized power loss as a and b respectively. Then the calculation formula for the battery state distribution coefficient of each functional part is Int(i) = a + b. In the formula, Int(i) is the battery state distribution coefficient of each functional part, and i represents the serial number of each functional part.

[0051] After calculating the battery state distribution coefficients of each functional part, compare the magnitudes of the battery state distribution coefficients of each functional part.

[0052] Obviously, the higher the management frequency per unit time and / or the greater the actual power loss, the more the battery state distribution coefficients of each functional part need to be focused on, that is, the more important they are, and the greater the consideration weight they should occupy. And the unit time can be set according to actual needs and will not be elaborated here.

[0053] The battery state calculation module is used to use the battery state distribution coefficients of each functional part to determine the weight values of the battery states of each functional part by the preference ranking organization method (PROMETHEE), and weighted to obtain the overall battery state. The specific working process is as follows:

[0054] Step C1: Calculate the battery state distribution coefficients of each functional part according to the management frequency per unit time of each normalized functional part and the power loss of each functional part, and respectively assign weights to the battery state distribution coefficients of each functional part by the preference ranking organization method (PROMETHEE) to determine the weight values of the battery states of each functional part.

[0055] Specifically, the scoring of the battery state distribution coefficients of each functional part and the weight assignment by the preference ranking organization method (PROMETHEE) are as shown in Table 1 below:

[0056] Table 1

[0057]

[0058] It should be noted that in Table 1, the Q, W, and E regions correspond one-to-one to the three functional parts in this embodiment. After sorting the functional parts according to the battery state scoring coefficients, they are respectively corresponded to the Q, W, and E regions in descending order. In this table, the TTL index is used as a weighting factor. By determining the relative importance of different regions in the battery health state, the sum of the scores assigned to each region after comparing the relative importance is calculated, so as to reasonably calculate and evaluate the overall battery state. When calculating the weights of each region, the management frequency and power loss of each functional part are taken into account, which in turn affects the final battery state distribution coefficient.

[0059] Step C2, perform a weighted average calculation on the battery states of each functional part according to the weight values of the battery state distribution coefficients of each functional part to obtain the overall battery state. The formula can be expressed as: , where: , are the battery state values of the Q, W, and E regions respectively, , , are the weight values corresponding to the Q, W, and E regions respectively.

[0060] For example: Suppose the battery state values of each region have been obtained. For the sake of illustration, assume that: the battery state of the Q region , the battery state of the W region , the battery state of the E region , and perform a weighted calculation on the battery states of each region according to the weights: =5×0.5556 + 3×0.3333 + 2×0.1111 = 2.7778 + 1.0000 + 0.2222 = 4.0000, then the overall battery state = 4.

[0061] The monitoring and adjustment module determines the monitoring duration level based on the overall battery state of the battery and the battery discharge duration. The specific working process is as follows:

[0062] Step D1, after the monitoring and adjustment module receives the overall battery state of the BMS battery and the battery discharge duration, define the overall battery state of the BMS battery and the battery discharge duration as input variables, and divide them into different fuzzy sets respectively.

[0063] For example, "Good", "Medium", "Poor" for the overall battery state of the battery, and "Short", "Medium", "Long" for the battery discharge duration.

[0064] Step D2: Define the monitoring duration level as the output variable and divide it into fuzzy sets. For example, for the monitoring duration level, use "Low", "Medium", "High".

[0065] Step D3: Develop a set of fuzzy rules to describe the influence of different input variables on the output variable. The rules can be defined based on professional knowledge or obtained through data analysis and experiments. For example:

[0066] Mark the overall battery state of the battery as S, the battery discharge duration as T, and the monitoring duration level as Monitor. Then, it can be defined as:

[0067] Rule 1: IF (S is Good) AND (T is Long) THEN (Monitor is High)

[0068] Rule 2: IF (S is Poor) AND (T is Short) THEN (Monitor is Low) ...

[0069] Step D4: Perform fuzzy reasoning based on the fuzzy rules to determine the solution for the monitoring duration level.

[0070] It should be noted that the division of the fuzzy sets can be adjusted according to the actual situation. For example, although this embodiment takes three fuzzy sets as an example, in fact, the overall battery state of the battery, the battery discharge duration, and the monitoring duration level can be divided into more than three sets to facilitate more accurate adjustment according to different temperatures.

[0071] Furthermore, for the judgment of high, medium, and low of the overall battery state of the battery and the battery discharge duration, thresholds can be set for judgment according to the actual situation. For example, when the overall battery state of the battery is lower than 10, it is marked as "Poor", and when the battery discharge duration is higher than 24h, it is marked as "High", etc., which will not be elaborated here.

[0072] It should be noted that the discharge duration refers to the time required for the battery to discharge from being fully charged to the end of discharge. If the discharge duration of the battery is short, it means that under the same load, the energy release of the battery is faster, which is usually an important sign of battery aging. As the battery ages, the internal chemical reaction efficiency decreases, and both the capacity and energy density of the battery will decrease, resulting in a shorter discharge duration. Therefore, the shorter the discharge duration, the higher the degree of battery aging usually is, and more frequent performance problems may occur, such as the battery being unable to supply power continuously, overheating, or more rapid power depletion.

[0073] The battery state of a battery affects the voltage change during discharge. As the battery ages, the internal chemical substances and structure gradually deteriorate, which increases the battery state. A poor battery state means that the battery generates more heat during discharge, resulting in energy loss and may also affect the discharge efficiency and safety of the battery. A poor battery state may also cause the operating temperature of the battery to rise, thereby affecting the stability and service life of the battery. Therefore, a poor battery state is also an obvious sign of battery aging.

[0074] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula that is closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0075] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0076] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0077] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and invention constraints of the technical solution. Those skilled in the art can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present application.

[0078] In addition, in each embodiment of the present application, the various functional modules can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0079] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present application, and all should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0080] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. BMS battery remote monitoring and data analysis system based on cloud platform, characterized by: include: Data monitoring module, coefficient determination module, battery status calculation module and monitoring and adjustment module, and signal connections between the modules; Data monitoring module: monitors the management times and power consumption of the battery monitoring part, battery protection part and battery balancing part; Coefficient determination module: derives the management frequency of each functional part and derives the battery state distribution coefficient of each functional part in combination with the power loss; Battery status calculation module: Use the battery status distribution coefficient of each functional part to determine the weight value of the battery status of each functional part using the priority diagram method, and weight the battery status of the whole battery; Monitoring and adjustment module: determines the monitoring time level according to the overall battery status of the battery and the battery discharge time; The battery state distribution coefficient of each functional part is calculated according to the normalized management frequency per unit time of each functional part and the power loss of each functional part, and the battery state distribution coefficient of each functional part is weighted according to the priority diagram method to determine the weight value of the battery state of each functional part; The battery status of each functional part is calculated by weighted average according to the weight value of the battery status distribution coefficient of each functional part to obtain the overall battery status; After receiving the overall battery status and battery discharge duration of the BMS battery, the monitoring and regulating module defines the overall battery status and battery discharge duration of the BMS battery as input variables, and divides them into different fuzzy sets respectively; The monitoring duration level is defined as the output variable and divided into fuzzy sets; Formulate fuzzy rules to describe the overall battery status of the BMS battery and the impact of the battery discharge duration definition on the monitoring duration level; Fuzzy reasoning is performed based on fuzzy rules to determine the monitoring time level of the BMS battery.

2. The BMS battery remote monitoring and data analysis system based on the cloud platform according to claim 1 is characterized in that: Determine the functional parts of the BMS battery, namely the battery monitoring part, battery protection part and battery balancing part, manage and partition the power loss of each functional part, and manage and monitor the power loss separately, and record the management times and power loss data of each functional part.

3. The BMS battery remote monitoring and data analysis system based on the cloud platform according to claim 2 is characterized in that: Calculate the management frequency per unit time of each functional part, and use the ratio of the management times of each functional part to the total management times for calculation and expression. The formula is: , where the total number of management times is the sum of the management times of each functional part; The power loss of each functional part is evaluated. The specific evaluation method is as follows: Linear normalization is used to map the energy loss per unit time of each functional part to the interval [0, 1], the formula is: ,in: It is the value after normalization of power loss; is the power loss per unit time of each functional part, The maximum value of the power loss per unit time of each functional part; It is the minimum value of power loss per unit time of each functional part; Will The battery state distribution coefficients of each functional part are obtained by adding the normalized power loss. The normalized power loss is marked as a and b, and the battery state distribution coefficient calculation formula of each functional part is Int (i) = a + b, where Int (i) is the battery state distribution coefficient of each functional part, and i represents the serial number of each functional part; After the battery state distribution coefficients of the functional parts are obtained by calculation, the battery state distribution coefficients of the functional parts are compared.

4. The BMS battery remote monitoring and data analysis system based on the cloud platform according to claim 3 is characterized in that: The battery status of each functional part is calculated by weighted average according to the weight value of the battery status distribution coefficient of each functional part to obtain the overall battery status. The formula is expressed as: ,in: , , They are the battery status values ​​of the Q, W, and E areas respectively. , are weight values ​​corresponding to the Q, W, and E regions, respectively, wherein: the Q, W, and E regions correspond one by one to the three functional parts in this embodiment, and after sorting the functional parts according to the size of the battery status scoring coefficient, they correspond to the Q, W, and E regions in descending order.

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