BMS battery remote monitoring and data analysis system based on cloud platform

Abstract

The invention discloses a BMS battery remote monitoring and data analysis system based on a cloud platform, relates to the technical field of remote monitoring, and is used for solving the problem that in the prior art, due to the lack of detailed data monitoring, the performance and loss of each part of a battery cannot be deeply analyzed, and then the health assessment and management of the whole battery are affected. The battery state of the battery is usually a fixed and static value, and the dynamic influence of the management frequency and the electric energy loss of different functional parts on the battery state is ignored; by combining the management frequency and the electric energy loss, the battery state of each functional part can be accurately distributed, the use strategy of the battery can be optimized according to the actual conditions of different modules, and meaningless energy loss is reduced. Unnecessary interference on the battery is reduced, manual intervention on battery management can be reduced through an automatic monitoring and adjusting mechanism, and the battery management is more intelligent and efficient.

Description

Technical Field

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

[0002] The cloud platform's BMS battery remote monitoring technology realizes 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 use, and improve the safety and efficiency of the battery system. It is widely used in electric vehicles, energy storage systems, smart grids, and other fields. With the development of technology, the future BMS remote monitoring system will be more intelligent and precise, and play a greater role in all walks of life.

[0003] The prior art has the following deficiencies: In the existing technology, there is a lack of detailed data monitoring, which makes it impossible to deeply analyze the performance and loss of each part of the battery, which in turn affects the overall battery health assessment and management. In addition, the battery status of the battery is usually a fixed, static value, ignoring the dynamic impact of the management frequency and power loss of different functional parts on the battery status. There is a lack of a mechanism to adjust the monitoring strategy based on the actual use of the battery, and it is impossible to automatically adjust the monitoring frequency according to the real-time status of the battery, so as to more effectively monitor the battery status and detect potential problems in advance.

[0004] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, 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-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: The BMS battery remote monitoring and data analysis system based on the cloud platform includes a data monitoring module, a coefficient determination module, a battery status calculation module, and a 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.

[0007] In a preferred embodiment, 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: 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.

[0008] In a preferred embodiment, the coefficient determination module is used to obtain the management frequency of each functional part and to obtain the battery state distribution coefficient of each functional part in combination with the power consumption: 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.

[0009] The management frequency and the normalized power loss are added to obtain the battery state distribution coefficient of each functional part. The management frequency and the normalized power loss are marked as a and b respectively. 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.

[0010] In a preferred embodiment, the battery state calculation module uses the battery state distribution coefficient of each functional part to determine the weight value of the battery state of each functional part using the priority diagram method, and weightedly obtains the overall battery state of the battery: 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. The formula is expressed as: ,in: , They are the battery status values ​​of the Q, W, and E areas respectively. , , They are the weight values ​​corresponding to the Q, W, and E regions, 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.

[0011] In a preferred embodiment, the monitoring duration level is determined based on the overall battery status of the battery and the battery discharge duration: 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.

[0012] The technical effects and advantages of the BMS battery remote monitoring and data analysis system based on the cloud platform of the present invention are as follows: By combining the management frequency and power loss, the battery status of each functional part can be accurately allocated. Through this precise battery status allocation, the battery usage strategy can be optimized according to the actual situation of different modules to reduce unnecessary energy loss. Ensure that the frequency and method of monitoring are reasonably adjusted in different discharge stages of the battery. This means that when the battery status is large, the system can provide more frequent monitoring to avoid excessive discharge or loss, and when the battery status is small, the system can reduce the monitoring frequency, thereby reducing unnecessary interference to the battery. Through the automated monitoring and adjustment mechanism, manual intervention in battery management can be reduced, making battery management more intelligent and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 The figure is a schematic diagram of the structure of the BMS battery remote monitoring and data analysis system based on the cloud platform of the present invention. DETAILED DESCRIPTION

[0014] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example

[0015] The present invention discloses a BMS battery remote monitoring and data analysis system based on a cloud platform, such as Figure 1 As shown, it includes: a data monitoring module, a coefficient determination module, a battery state calculation module and a monitoring and adjustment module, and the modules are signal connected.

[0016] The functions of each module are as follows: 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 and battery discharge time.

[0017] The data monitoring module is used to monitor the management times and power consumption of the battery monitoring part, battery protection part and battery balancing part. The specific process is as follows: Step A1, determine the functional parts of the BMS battery, namely the battery monitoring part, the battery protection part and the battery balancing part.

[0018] Step A2: manage and partition each functional part into power consumption zones, and manage and monitor each functional part separately.

[0019] Step A3, recording the management times and power consumption data of each functional part.

[0020] It should be noted that the battery monitoring part, battery protection part and battery balancing part are the BMS battery used to ensure the safe, efficient and stable operation of the battery pack. The role of each functional part is as follows: Battery monitoring part: usually responsible for monitoring the basic status of the battery, such as voltage, current, temperature and other information, to ensure that the battery is within the safe working range; Battery protection part: mainly used to protect the battery from abnormal conditions such as overcharging, over-discharging, overheating, etc., and controlled by protection circuit or management system; Battery balancing part: when multiple battery cells are connected in parallel or series, by balancing the charge and discharge status of the battery, the power of each battery cell is ensured to be consistent, thereby improving the efficiency and life of the entire battery pack.

[0021] The coefficient determination module is used to obtain the management frequency of each functional part and to obtain the battery state distribution coefficient of each functional part in combination with the power loss. The specific working process is as follows: Step B1: After receiving the management times of each functional part, the coefficient determination module calculates the management frequency of each functional part. The specific calculation method is as follows: 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 can be expressed as: , where the total management times are the sum of the management times of each functional part. The above formula can ensure that the frequency is between [0, 1].

[0022] Step B2, evaluate the power loss of each functional part, 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.

[0023] 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 consistent with the general formula and will not be repeated here.

[0024] Step B3, add the management frequency and the normalized power loss to obtain the battery state allocation coefficient of each functional part, mark the management frequency and the normalized power loss as a and b respectively, then the battery state allocation coefficient calculation formula of each functional part is Int (i) = a + b. In the formula, Int (i) is the battery state allocation 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.

[0025] Obviously, the higher the management frequency per unit time and / or the greater the actual power loss, the more attention should be paid to the battery status distribution coefficient of each functional part, that is, the more important it is, the greater the consideration weight should be, and the unit time can be set according to actual needs, which will not be elaborated here.

[0026] The battery status calculation module is used to 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 weighted to obtain the overall battery status of the battery. The specific working process is as follows: Step C1, calculate the battery status distribution coefficient 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 status distribution coefficients of each functional part according to the priority diagram method to determine the weight value of the battery status of each functional part.

[0027] Specifically, the battery status distribution coefficient scores of each functional part are weighted according to the priority diagram method as shown in Table 1 below: Table 1

[0028] It should be noted that in Table 1, the Q, W, and E regions correspond to the three functional parts in this embodiment one by one. After sorting the functional parts according to the size of the battery status score coefficient, they correspond to the Q, W, and E regions in order from large to small. In this table, the TTL indicator is used as a weighting factor. By determining the relative importance of different regions in the battery health status, the relative importance of each region is compared and the sum of the scores is added, so as to reasonably calculate and evaluate the overall battery status. When calculating the weight of each region, the management frequency and power loss of each functional part are taken into account, which in turn affects the final battery status allocation coefficient.

[0029] Step C2, 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 can be expressed as: ,in: , They are the battery status values ​​of the Q, W, and E areas respectively. , , They are the weight values ​​corresponding to the Q, W, and E areas respectively.

[0030] For example: Assume that the battery status value of each area has been obtained. For the sake of explanation, assume that: Q area battery status ,W area battery status ,E area battery status , and the battery status of each area is calculated according to the weight: =5×0.5556+3×0.3333+2×0.1111=2.7778+1.0000+0.2222= 4.0000, then the overall battery status =4.

[0031] The monitoring and adjustment module determines the monitoring duration level based on the overall battery status of the battery and the battery discharge duration. The specific working process is as follows: Step D1, after the monitoring and regulating module receives the overall battery status and battery discharge duration of the BMS battery, it defines the overall battery status and battery discharge duration of the BMS battery as input variables and divides them into different fuzzy sets.

[0032] For example, "Good", "Medium", "Poor" for the overall battery status, and "Short", "Medium", "Long" for the battery discharge duration.

[0033] Step D2, define the monitoring duration level as an output variable and divide it into fuzzy sets, for example, "Low", "Medium", "High" for the monitoring duration level.

[0034] Step D3, formulate a set of fuzzy rules to describe the impact of different input variables on output variables. The definition of rules can be based on professional knowledge or obtained through data analysis and experiments. For example: If the overall battery status is marked as S, the battery discharge time is marked as T, and the monitoring time level is marked as Monitor, then we can define: Rule 1: IF (S is Good) AND (T is Long) THEN (Monitor is High) Rule 2: IF (S is Poor) AND (T is Short) THEN (Monitor is Low) ... Step D4, performing fuzzy reasoning according to fuzzy rules to determine the monitoring duration level plan.

[0035] It should be noted that the division of fuzzy sets can be adjusted according to actual conditions. For example, although the present embodiment takes three fuzzy sets as examples, in fact, the overall battery status, battery discharge time and monitoring time level of the battery can be divided into more than three sets to facilitate better precise adjustment according to different temperatures.

[0036] Furthermore, for the judgment of the overall battery status and the battery discharge time, the threshold can be set according to the actual situation. For example, when the overall battery status is lower than 10, it is marked as "Poor", and when the battery discharge time is higher than 24h, it is marked as "High", etc., which will not be elaborated here.

[0037] It should be noted that the discharge time refers to the time required for the battery to discharge from a full charge to the end of the discharge. If the battery's discharge time is shorter, it means that the battery releases energy faster under the same load, which is usually an important sign of battery aging. As the battery ages, the efficiency of the internal chemical reaction decreases, and the battery's capacity and energy density will decrease, resulting in a shorter discharge time. Therefore, the shorter the discharge time, the higher the degree of battery aging, and more frequent performance problems may occur, such as the battery's inability to provide power for a long time, overheating, or faster power depletion.

[0038] The battery state of a battery affects the voltage change of the battery during discharge. As the battery ages, the internal chemicals and structure gradually deteriorate, which increases the battery state. Poor battery state means that the battery will generate more heat during discharge, resulting in energy loss, and may also affect the discharge efficiency and safety of the battery. Poor battery state may also cause the operating temperature of the battery to increase, which in turn affects the stability and service life of the battery. Therefore, poor battery state is also a clear sign of battery aging.

[0039] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0040] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0041] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0042] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application of the technical solution and the invention constraints. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0043] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0044] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0045] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should 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.

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; The management frequency and the normalized power loss are added to obtain the battery state distribution coefficient of each functional part. The management frequency and the normalized power loss are marked as a and b respectively. 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 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. The formula is expressed as: ,in: , They are the battery status values ​​of the Q, W, and E areas respectively. , , They are the weight values ​​corresponding to the Q, W, and E regions, 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.

5. The BMS battery remote monitoring and data analysis system based on the cloud platform according to claim 4 is characterized in that: 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.

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