Intelligent charging and discharging equalization management method for standby battery based on dynamic management

Abstract

The invention discloses a standby battery intelligent charge and discharge equalization management method based on dynamic management, and relates to the technical field of equipment power supplies, and the method comprises the steps: collecting equipment power consumption demand data of a user when equipment is started in a set data collection period; comprehensively analyzing the power supply demand of the user when the user uses the equipment based on the equipment power consumption demand data of the user when the equipment is started; analyzing the health state value of each single battery based on the historical working data of the single batteries in the standby battery pack; and based on the health state index matrix of the standby battery pack and a power supply demand when a user uses the equipment, determining a charging and discharging state of the standby battery pack in a standing state of the equipment. Compared with the prior art, the method has the advantages that by analyzing the electricity demand habit of a user when the user uses the equipment, intelligent management is carried out on the standby power supply, the loss power consumption of the equipment standby power supply when the equipment stands can be effectively reduced, and energy-saving control over the equipment standby power supply is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment power supply, and in particular to a backup battery intelligent charge and discharge balance management method based on dynamic management. Background Art

[0002] A backup battery refers to a battery that is additionally prepared for electronic equipment and is used to replace or supplement the power of the main battery. There are two types of backup batteries: a replaceable backup battery and an integrated seamless backup battery. The existing management mode for integrated seamless backup batteries requires the backup power supply to be kept started at all times to meet the power demand of the equipment. This mode increases energy consumption on the one hand, and increases the no-load load of the backup power supply on the other hand, resulting in a reduced life of the backup power supply. Summary of the invention

[0003] In order to solve the above technical problems, a method for intelligent charging and discharging balancing management of backup batteries based on dynamic management is provided. This technical solution solves the above-mentioned existing management mode of integrated seamless backup batteries, which requires the backup power supply to be kept started at all times to meet the power demand of the equipment. This mode increases energy consumption on the one hand, and increases the no-load load of the backup power supply on the other hand, resulting in a reduced life of the backup power supply.

[0004] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0005] A backup battery intelligent charge and discharge balance management method based on dynamic management, comprising:

[0006] Collect the power consumption demand data of the user's device when the device is started within the set data collection period;

[0007] Comprehensively analyze the power demand of users when using the equipment based on the power consumption demand data of the equipment when the user starts the equipment;

[0008] Analyze the health status value of each single cell battery based on the historical working data of the single cells in the backup battery pack;

[0009] Based on the health status indicator matrix of the backup battery pack and the power supply requirements of the user when using the device, the charge and discharge status of the backup battery pack when the device is in a static state is determined.

[0010] Preferably, the comprehensive analysis of the power supply demand of the user when using the device based on the device power consumption demand data of the user when the device is started specifically includes:

[0011] Eliminate abnormal data from all device power consumption demand data of users when starting their devices collected in the data collection cycle closest to the current one to obtain standard power consumption demand data;

[0012] Intelligent learning and analysis of users' power requirements when using devices is performed based on standard power consumption demand data.

[0013] Preferably, the step of removing abnormal data from all the device power consumption demand data of the user when the device is started and collected in the data collection cycle closest to the current one to obtain the standard power consumption demand data specifically includes:

[0014] Construct an abnormal data determination formula based on the Grubbs criterion;

[0015] Based on the abnormal data determination formula, determine the abnormal data that does not conform to the normal distribution among all the power consumption demand data of the user's equipment when the equipment is started, which are collected in the data collection cycle closest to the current one;

[0016] Eliminate abnormal data and obtain standard power consumption demand data;

[0017] The abnormal data determination formula is:

[0018]

[0019] In the formula, x i The power consumption demand data of all devices when the user starts the i-th device. is the average value of the power consumption demand data of all devices when the device is started, s is the standard deviation of the power consumption demand data of all devices when the device is started, bpn is the critical value of the abnormal point, and the critical value of the abnormal point is obtained by checking the Grubbs table;

[0020] If the abnormal data judgment formula is satisfied, then x i is abnormal data.

[0021] Preferably, the intelligent learning and analysis of the power supply demand of the user when using the device based on the standard power consumption demand data specifically includes:

[0022] All standard power demand data are averaged to obtain a power demand benchmark value;

[0023] Analyze the standard power demand data to determine the standard deviation of power demand;

[0024] Calculate the power demand value of the user when using the equipment through the demand calculation formula;

[0025] The demand calculation formula is:

[0026] X=(1+σ)×X0

[0027] Where X is the power demand value when the user uses the equipment, X0 is the power demand benchmark value, and σ is the standard deviation of the power demand.

[0028] Preferably, analyzing the standard power demand data to determine the power demand standard deviation specifically includes:

[0029] Determine the maximum value, the minimum value and the value with the most occurrence in the standard power demand data respectively, and record them as the maximum power demand, the minimum power demand and the most likely power demand respectively;

[0030] Count the occurrence times of the maximum power consumption demand, the minimum power consumption demand and the most likely power consumption demand respectively;

[0031] Calculate the standard deviation of the power consumption demand of the equipment through the standard deviation calculation formula;

[0032] Wherein, the standard deviation calculation formula is:

[0033]

[0034] Wherein, Y1 is the maximum power demand, Y2 is the minimum power demand, Y3 is the most likely power demand, N1 is the number of occurrences of the maximum power demand, N2 is the number of occurrences of the maximum power demand, and N3 is the number of occurrences of the maximum power demand.

[0035] Preferably, the analyzing the health status value of each single cell in the backup battery pack based on the historical working data of the single cells specifically includes:

[0036] Collect historical working data of single cells in the backup battery pack;

[0037] Analyze the health value of each single cell based on the historical working data of the single cell.

[0038] Preferably, the analyzing the health value of each single cell based on the historical working data of the single cell specifically includes:

[0039] Determine the working time of each single battery in a normal energy storage state and a power-deficient energy storage state respectively;

[0040] Based on the Logistic regression model, a single battery health value model is established;

[0041] Substitute the working time of the single battery in the normal energy storage state and the working time of the low-power energy storage state into the health value model of the single battery to obtain the health value of the single battery;

[0042] The health value model of the single battery is:

[0043]

[0044] Where M is the health value of the single battery, α, β1 and β2 are the coefficients of the health value model of the single battery, T is the duration of normal energy storage working state, T - It refers to the working time in the power-deficient energy storage state.

[0045] Preferably, the determining the charge and discharge state of the backup battery pack in a stationary state of the device based on the health status indicator matrix of the backup battery pack and the power supply demand of the user when using the device specifically includes:

[0046] Based on the rated output power of the single battery and the power supply demand of the user when using the device, determine the number of single batteries that meet the power supply demand, which is recorded as the required number of batteries;

[0047] Collect the remaining power percentage of all single cells in the current backup battery pack;

[0048] Calculate the status value of each single cell battery based on the remaining power ratio of the single cell battery and the health value of the single cell battery;

[0049] According to the order of the state values ​​of the single cells from large to small, the single cells with the required number of batteries are selected and kept in the discharge state, and the remaining single cells are set to the charging state;

[0050] The calculation formula of the state value of the single battery is:

[0051] Z=M×P

[0052] Where Z is the state value of the single battery, and P is the remaining power percentage of the single battery.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] The present invention proposes a backup battery intelligent charge and discharge balance management method based on dynamic management. The method intelligently analyzes the demand for backup power supply based on the power consumption demand of the user when using the equipment, and intelligently adjusts and controls the number of batteries in the startup state in the backup power supply based on the health status of the backup power supply. On the basis of ensuring that the backup power supply meets the operating requirements, the overall comprehensive working time of the backup battery in the equipment can be intelligently reduced, thereby increasing the comprehensive life of the backup power supply. At the same time, the loss power consumption of the equipment backup power supply can be effectively reduced, thereby realizing energy-saving control of the equipment backup power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a structural block diagram of the backup battery intelligent charge and discharge balance management method based on dynamic management proposed by the present invention;

[0056] Figure 2 is a flow chart of a method for obtaining standard power consumption demand data in the present invention;

[0057] Figure 3 A flow chart of the method for learning and analyzing the power supply requirements of a user when using a device in the present invention;

[0058] Figure 4 A flow chart of a method for determining a standard deviation of power consumption demand in the present invention;

[0059] Figure 5 A flow chart of a method for analyzing the health value of each single cell in the present invention;

[0060] Figure 6 A schematic diagram of the architecture of the electronic device proposed by the present invention;

[0061] Figure 7 This is a schematic diagram of the computer-readable storage medium structure proposed by the present invention. DETAILED DESCRIPTION

[0062] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0063] Reference Figure 1 As shown, a backup battery intelligent charge and discharge balancing management method based on dynamic management includes:

[0064] Collect the power consumption demand data of the user's device when the device is started within the set data collection period;

[0065] Comprehensively analyze the power demand of users when using the equipment based on the power consumption demand data of the equipment when the user starts the equipment;

[0066] Analyze the health status value of each single cell battery based on the historical working data of the single cells in the backup battery pack;

[0067] Based on the health status indicator matrix of the backup battery pack and the power supply requirements of the user when using the device, the charge and discharge status of the backup battery pack when the device is in a static state is determined.

[0068] This solution intelligently analyzes the demand for backup power supply based on the user's need to start the backup power equipment when using the equipment, and intelligently adjusts and controls the number of batteries in the backup power supply that are in the startup state based on the health status of the backup power supply.

[0069] In some preferred embodiments, the power consumption demand of the user when using the device is comprehensively analyzed based on the device power consumption demand data of the user when the device is started, and the power supply demand of the user when using the device specifically includes:

[0070] Eliminate abnormal data from all device power consumption demand data of users when starting their devices collected in the data collection cycle closest to the current one to obtain standard power consumption demand data;

[0071] Intelligent learning and analysis of users' power requirements when using devices is performed based on standard power consumption demand data.

[0072] Reference Figure 2 As shown, the abnormal data in the power consumption demand data of all devices of the user collected in the data collection cycle closest to the current one when the device is started is removed, and the standard power consumption demand data is obtained, which specifically includes:

[0073] Construct an abnormal data determination formula based on the Grubbs criterion;

[0074] Based on the abnormal data determination formula, determine the abnormal data that does not conform to the normal distribution among all the power consumption demand data of the user's equipment when the equipment is started, which are collected in the data collection cycle closest to the current one;

[0075] Eliminate abnormal data and obtain standard power consumption demand data;

[0076] Among them, the abnormal data judgment formula is:

[0077]

[0078] In the formula, x i The power consumption demand data of all devices when the user starts the i-th device. is the average value of the power consumption demand data of all devices when the device is started, s is the standard deviation of the power consumption demand data of all devices when the device is started, bpn is the critical value of the abnormal point, and the critical value of the abnormal point is obtained by checking the Grubbs table;

[0079] If the abnormal data judgment formula is satisfied, then x i is abnormal data.

[0080] The user's car usage habits in the same period are usually fixed. For example, in summer and winter, the user will start the air conditioner after starting the vehicle. Therefore, the vehicle's standby power demand in the same period fluctuates within the range that conforms to the normal distribution. When the user occasionally has different vehicle startup behaviors, the device power demand data will increase or decrease abnormally. In order to avoid the influence of this part of the device power demand data, this solution uses the Grubbs criterion to eliminate the abnormal values ​​that do not conform to the normal distribution, and then obtain the standard power demand data. This value can effectively reflect the vehicle's standby power demand standard in this period, thereby effectively ensuring the calculation accuracy of the power demand.

[0081] Reference Figure 3As shown, the power supply requirements of users when using the device are specifically analyzed through intelligent learning based on standard power consumption demand data, including:

[0082] All standard power demand data are averaged to obtain a power demand benchmark value;

[0083] Analyze the standard power demand data to determine the standard deviation of power demand;

[0084] Calculate the power demand value of the user when using the equipment through the demand calculation formula;

[0085] The demand calculation formula is:

[0086] X=(1+σ)×X0

[0087] Where X is the power demand value when the user uses the equipment, X0 is the power demand benchmark value, and σ is the standard deviation of the power demand.

[0088] Reference Figure 4 As shown, the standard power demand data is analyzed to determine the standard deviation of power demand, specifically including:

[0089] Determine the maximum value, the minimum value and the value with the most occurrence in the standard power demand data respectively, and record them as the maximum power demand, the minimum power demand and the most likely power demand respectively;

[0090] Count the occurrence times of the maximum power consumption demand, the minimum power consumption demand and the most likely power consumption demand respectively;

[0091] Calculate the standard deviation of the power consumption demand of the equipment through the standard deviation calculation formula;

[0092] The standard deviation calculation formula is:

[0093]

[0094] Wherein, Y1 is the maximum power demand, Y2 is the minimum power demand, Y3 is the most likely power demand, N1 is the number of occurrences of the maximum power demand, N2 is the number of occurrences of the maximum power demand, and N3 is the number of occurrences of the maximum power demand.

[0095] By taking the average value of the standard power demand data that presents abnormal values ​​as the power demand benchmark value, the power demand benchmark value can accurately reflect the vehicle starting habits of users in the current period, and collect the maximum value, minimum value and the value with the highest number of occurrences in the standard power demand data to represent different power consumption states when the vehicle is started, as well as the frequency of occurrence in each state, to comprehensively calculate the fluctuation of the vehicle's standby power demand under normal starting conditions, and based on the fluctuations to comprehensively calculate the power supply demand value of the user when using the equipment, to effectively ensure that the subsequently set single-cell battery operating state can meet the equipment's starting power demand.

[0096] In some preferred embodiments, based on the historical working data of the single cells in the backup battery pack, analyzing the health status value of each single cell specifically includes:

[0097] Collect historical working data of single cells in the backup battery pack;

[0098] Analyze the health value of each single cell based on the historical working data of the single cell.

[0099] Reference Figure 5 As shown in the figure, based on the historical working data of the single battery, the health value of each single battery is analyzed, including:

[0100] Determine the working time of each single battery in a normal energy storage state and a power-deficient energy storage state respectively;

[0101] Based on the Logistic regression model, a single battery health value model is established;

[0102] Substitute the working time of the single battery in the normal energy storage state and the working time of the low-power energy storage state into the health value model of the single battery to obtain the health value of the single battery;

[0103] The health value model of a single battery is:

[0104]

[0105] Where M is the health value of the single battery, α, β1 and β2 are the coefficients of the health value model of the single battery, T is the duration of normal energy storage working state, T - It refers to the working time in the power-deficient energy storage state.

[0106] Running a battery in a low-power state will lead to accelerated battery loss and increase the probability of battery failure. Based on this, this solution divides the battery's operating state into a normal energy storage state and a low-power energy storage state, and uses the working time of the normal energy storage state and the working time of the low-power energy storage state as independent variables of the Logistic regression model to establish a single-cell battery health value model. The Logistic regression model is a generalized linear regression analysis model, which is often used in data mining, result prediction and other fields. This method can be used to predict the probability of a single-cell battery failure as a measure of the health status of the single-cell battery. The single-cell battery health value model uses the probability value of no failure of the single-cell battery as the output of the single-cell battery health value.

[0107] In some preferred embodiments, based on the health status indicator matrix of the backup battery pack and the power supply demand of the user when using the device, determining the charge and discharge status of the backup battery pack when the device is in a stationary state specifically includes:

[0108] Based on the rated output power of the single battery and the power supply demand of the user when using the device, determine the number of single batteries that meet the power supply demand, which is recorded as the required number of batteries;

[0109] Collect the remaining power percentage of all single cells in the current backup battery pack;

[0110] Calculate the status value of each single cell battery based on the remaining power ratio of the single cell battery and the health value of the single cell battery;

[0111] According to the order of the state values ​​of the single cells from large to small, the single cells with the required number of batteries are selected and kept in the discharge state, and the remaining single cells are set to the charging state;

[0112] The calculation formula of the state value of the single battery is:

[0113] Z=M×P

[0114] Where Z is the state value of the single battery, and P is the remaining power percentage of the single battery.

[0115] It can be understood that the higher the energy storage of the battery and the higher the health status of the battery, the better the current state of the battery. In this solution, the state value of the single cell is calculated by combining the above indicators. By screening out several single cells with the largest state values, the single cells are in the starting state when the equipment is stationary, and provide backup power supply during startup. This can intelligently reduce the overall comprehensive working time of the backup battery in the equipment, thereby increasing the comprehensive life of the backup power supply.

[0116] Furthermore, the method according to the embodiment of the present application can also be performed by Figure 6 The electronic device architecture shown in FIG. Figure 6As shown, the electronic device 500 may include a bus 501, one or more CPUs 502, a read-only memory (ROM) 503, a random access memory (RAM) 504, a communication port 505 connected to a network, an input / output component 506, a hard disk 507, etc. The storage device in the electronic device 500, such as the ROM 503 or the hard disk 507, may store a backup battery intelligent charge and discharge balancing management method based on dynamic management provided by the present application. The electronic device 500 may also include a user interface 508. Of course, Figure 6 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Figure 6 One or more components of an electronic device are shown.

[0117] Figure 7 Schematic diagram of a computer-readable storage medium structure provided by an embodiment of the present application. Figure 7 As shown, a computer-readable storage medium 600 according to an embodiment of the present application is shown. Computer-readable instructions are stored on the computer-readable storage medium 600. When the computer-readable instructions are executed by the processor, the intelligent charge and discharge balancing management method for backup batteries based on dynamic management according to the embodiment of the present application described with reference to the above figures can be executed. The storage medium 600 includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory (cache), etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0118] To sum up, the advantages of the present invention are: by analyzing the power consumption habits of users when using the equipment and intelligently managing the backup power supply, the loss power consumption of the equipment backup power supply when the equipment is stationary can be effectively reduced, thereby achieving energy-saving control of the equipment backup power supply.

[0119] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A backup battery intelligent charge and discharge balance management method based on dynamic management, characterized in that: include: Collect the power consumption demand data of the user's device when the device is started within the set data collection period; Comprehensively analyze the power demand of users when using the equipment based on the power consumption demand data of the equipment when the user starts the equipment; Analyze the health status value of each single cell battery based on the historical working data of the single cells in the backup battery pack; Based on the health status indicator matrix of the backup battery pack and the power supply requirements of the user when using the device, the charge and discharge status of the backup battery pack when the device is in a static state is determined.

2. According to the method of intelligent charge and discharge balance management of backup batteries based on dynamic management according to claim 1, it is characterized in that: The comprehensive analysis of the power consumption demand data of the device when the user starts the device to determine the power supply demand of the user when using the device specifically includes: Eliminate abnormal data from all device power consumption demand data of users when starting their devices collected in the data collection cycle closest to the current one to obtain standard power consumption demand data; Intelligent learning and analysis of users' power requirements when using devices is performed based on standard power consumption demand data.

3. According to claim 2, a backup battery intelligent charge and discharge balance management method based on dynamic management is characterized in that: The method of removing abnormal data from all the power consumption demand data of the user's devices when the device is started and collected in the data collection cycle closest to the current one to obtain the standard power consumption demand data specifically includes: Construct an abnormal data determination formula based on the Grubbs criterion; Based on the abnormal data determination formula, determine the abnormal data that does not conform to the normal distribution among all the power consumption demand data of the user's devices when the device is started, which are collected in the data collection cycle closest to the current one; Eliminate abnormal data and obtain standard power consumption demand data; The abnormal data determination formula is: In the formula, x i The power consumption demand data of all devices when the user starts the i-th device. is the average value of the power consumption demand data of all devices when the device is started, s is the standard deviation of the power consumption demand data of all devices when the device is started, bpn is the critical value of the abnormal point, and the critical value of the abnormal point is obtained by checking the Grubbs table; If the abnormal data judgment formula is satisfied, then x i is abnormal data.

4. The method for intelligent charge and discharge balance management of backup batteries based on dynamic management according to claim 3 is characterized in that: The intelligent learning and analysis of the power supply demand of the user when using the device based on the standard power consumption demand data specifically includes: All standard power demand data are averaged to obtain a power demand benchmark value; Analyze the standard power demand data to determine the standard deviation of power demand; Calculate the power demand value of the user when using the equipment through the demand calculation formula; The demand calculation formula is: X=(1+σ)×X0 Where X is the power demand value when the user uses the equipment, X0 is the power demand benchmark value, and σ is the standard deviation of the power demand.

5. The method for intelligent charge and discharge balance management of backup batteries based on dynamic management according to claim 4, characterized in that: The analyzing the standard power demand data to determine the standard deviation of the power demand specifically includes: Determine the maximum value, the minimum value and the value with the most occurrence in the standard power demand data respectively, and record them as the maximum power demand, the minimum power demand and the most likely power demand respectively; Count the occurrence times of the maximum power consumption demand, the minimum power consumption demand and the most likely power consumption demand respectively; Calculate the standard deviation of the power consumption demand of the equipment through the standard deviation calculation formula; Wherein, the standard deviation calculation formula is: Wherein, Y1 is the maximum power demand, Y2 is the minimum power demand, Y3 is the most likely power demand, N1 is the number of occurrences of the maximum power demand, N2 is the number of occurrences of the maximum power demand, and N3 is the number of occurrences of the maximum power demand.

6. The method for intelligent charge and discharge balance management of backup batteries based on dynamic management according to claim 5, characterized in that: The analyzing the health status value of each single cell based on the historical working data of the single cells in the backup battery pack specifically includes: Collect historical working data of single cells in the backup battery pack; Analyze the health value of each single cell based on the historical working data of the single cell.

7. The method for intelligent charge and discharge balance management of backup batteries based on dynamic management according to claim 6, characterized in that: The analysis of the health value of each single cell based on the historical working data of the single cell specifically includes: Determine the working time of each single battery in a normal energy storage state and a power-deficient energy storage state respectively; Based on the Logistic regression model, a single battery health value model is established; Substitute the working time of the single battery in the normal energy storage state and the working time of the low-power energy storage state into the health value model of the single battery to obtain the health value of the single battery; The health value model of the single battery is: Where M is the health value of the single battery, α, β1 and β2 are the coefficients of the health value model of the single battery, T is the duration of normal energy storage working state, T - It refers to the working time in the power-deficient energy storage state.

8. The method for intelligent charge and discharge balance management of backup batteries based on dynamic management according to claim 7, characterized in that: The determining of the charge and discharge state of the backup battery pack when the device is in a stationary state based on the health status indicator matrix of the backup battery pack and the power supply demand of the user when using the device specifically includes: Based on the rated output power of the single battery and the power supply demand of the user when using the device, determine the number of single batteries that meet the power supply demand, which is recorded as the required number of batteries; Collect the remaining power percentage of all single cells in the current backup battery pack; Calculate the status value of each single cell battery based on the remaining power ratio of the single cell battery and the health value of the single cell battery; According to the order of the state values ​​of the single cells from large to small, the single cells with the required number of batteries are selected and kept in the discharge state, and the remaining single cells are set to the charging state; The calculation formula of the state value of the single battery is: Z=M×P Where Z is the state value of the single battery, and P is the remaining power percentage of the single battery.

9. An electronic device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the intelligent charge and discharge balancing management method for backup batteries based on dynamic management as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer-readable program stored thereon, characterized in that: When the computer-readable program is executed by a processor, the method for intelligent charge and discharge balancing management of a backup battery based on dynamic management as described in any one of claims 1 to 7 is implemented.

Images