CN104375090B - Rechargeable lithium battery remaining capacity remote monitoring method - Google Patents
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 36
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 19
- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 19
- 238000000034 method Methods 0.000 title claims abstract description 18
- 230000036541 health Effects 0.000 claims abstract description 10
- 210000004027 cell Anatomy 0.000 claims description 9
- 238000013528 artificial neural network Methods 0.000 claims description 8
- 239000011159 matrix material Substances 0.000 claims description 8
- 238000012549 training Methods 0.000 claims description 6
- 238000007599 discharging Methods 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 4
- 230000010365 information processing Effects 0.000 claims description 3
- 239000000284 extract Substances 0.000 claims description 2
- 210000002569 neuron Anatomy 0.000 claims description 2
- 238000012806 monitoring device Methods 0.000 abstract description 6
- 230000005540 biological transmission Effects 0.000 abstract description 4
- 230000005611 electricity Effects 0.000 abstract description 2
- 230000008569 process Effects 0.000 abstract description 2
- 238000004891 communication Methods 0.000 description 3
- 239000000446 fuel Substances 0.000 description 3
- 238000013459 approach Methods 0.000 description 2
- 238000007796 conventional method Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 1
- 239000013078 crystal Substances 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000003862 health status Effects 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
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Abstract
The invention provides a rechargeable lithium battery remaining capacity remote monitoring device and a monitoring method of the rechargeable lithium battery remaining capacity remote monitoring device. The monitoring method comprises the steps that battery remaining capacity information, battery remaining time information and battery health condition information on various working conditions are provided through a battery monitoring module; battery data collected by a wireless data transmission module composed of a single-chip microcomputer and a wireless transceiver chip are sent to a far-end receiving terminal; the receiving terminal processes signals, displays the battery remaining usage time information, the battery health condition information and other information in a displayer and gives a buzzer alarm when detecting that the remaining capacity is lower than a preset threshold value, so that battery capacity remote monitoring is achieved. The monitoring method is used for battery remaining capacity remote monitoring, the monitoring device is simple in structure, the monitoring method is convenient to operate, and the charge quantity of electricity stored in a storage battery can be accurate to a specific numerical value, so remote online accurate monitoring of the battery capacity is achieved completely.
Description
Technical Field
The invention relates to the field of battery state detection, in particular to a remote monitoring device and method for the residual electric quantity of a rechargeable lithium battery.
Background
At present, rechargeable battery applications are seen throughout from electric dc screen power supplies to telecommunication and mobile communication equipment back-up power supplies, from electric bicycles to electric cars, from cell phones to notebook computers. The rechargeable battery has the biggest characteristic of being capable of charging and discharging, the battery can be fully utilized through correct charging and discharging, and the service life of the battery can be shortened even the battery is damaged through incorrect charging and discharging. In order to charge and discharge reasonably in time, just as drivers need to know the fuel indication of automobiles, users want to know the state of charge (SOC) of rechargeable batteries, and especially in some important occasions, the indication of the residual capacity of rechargeable batteries is a particularly important index.
However, for some special devices with rechargeable batteries, people may not be able to approach the devices or it is difficult to approach the devices, it is very difficult to know or detect the remaining battery capacity of the devices by using the conventional method, for example, with the rapid development of energy-saving and environment-friendly solar rechargeable streetlamps, the environment-friendly streetlamps are installed in many places, however, the remaining battery capacity of the top battery of the streetlamps is unstable due to the instability of solar energy, so the situation of the remaining battery capacity of the top battery is sometimes desired to be monitored, but because the streetlamps are high, the conventional method for monitoring the remaining battery capacity by wire obviously cannot meet the requirements, so a remote monitoring method is required to monitor the remaining battery capacity at the moment, and with the rapid development of wireless communication technology, the remote monitoring of the battery capacity.
Disclosure of Invention
The invention aims to provide a remote monitoring device for the residual electric quantity of a rechargeable lithium battery, which can send the state information of the rechargeable lithium battery obtained by monitoring and calculating to a remote end in a wireless communication mode, and is convenient for real-time monitoring.
The invention aims to realize the purpose through the technical scheme, which comprises a battery monitoring module, a first single chip microcomputer, a wireless transmitting end, a wireless receiving end, a second single chip microcomputer, a system auxiliary control alarm module and a display, wherein the battery monitoring module is connected with the first single chip microcomputer;
the data monitoring end of the battery monitoring module is connected with the measured rechargeable lithium battery, the data monitored by the battery monitoring module is sent to the wireless receiving end through the single chip microcomputer and the wireless sending end, the wireless receiving end sends the received data to the second single chip microcomputer, and the second single chip microcomputer sends control information to the system auxiliary control alarm module and sends display information to the display simultaneously after processing.
Further, the battery monitoring module is a MAX177050 battery fuel gauge.
Further, the wireless transmitting end and the wireless receiving end are nrf24L01 wireless transceiver chips.
Furthermore, the system auxiliary control alarm module is a buzzer.
Another objective of the present invention is to provide a method for remotely monitoring the remaining capacity of a rechargeable lithium battery, which can monitor and analyze the battery status information at the near end of the rechargeable lithium battery to obtain the remaining capacity information, remaining time information and health status information of the battery, and send the information to a remote monitoring end in a wireless communication manner.
The purpose of the invention is realized by the technical scheme, which comprises the following specific steps:
1) monitoring and recording the state information of the tested rechargeable lithium battery through a battery monitoring module;
2) the battery monitoring module carries out information processing on the monitored state information of the rechargeable lithium battery to obtain battery residual capacity information, battery residual time information and battery health condition information;
3) the battery residual capacity information, the battery residual time information and the battery health condition information obtained after processing are sent to a second single chip microcomputer through a first single chip microcomputer, a wireless sending end and a wireless receiving end;
4) the second singlechip extracts the battery residual capacity information, the battery residual time information and the battery health condition information, sends the information to the display, and simultaneously compares the information with a preset threshold value to send a control instruction to control the system auxiliary control alarm module.
Further, the state information of the rechargeable lithium battery in the step 1) comprises the current output voltage information, the current output information, the ambient temperature information, the input electric quantity information during charging and the output electric quantity information during discharging of the battery.
Further, the specific method for performing information processing on the state information of the rechargeable lithium battery in the step 2) is as follows: and (4) adopting an RBF neural network to carry out online prediction on the SOC of the battery.
Further, the specific method for online predicting the SOC of the battery by adopting the RBF neural network comprises the following steps: establishing an SOC prediction model based on an RBF neural network, and designing mainly from three aspects of a network level, a training level and a node level; in the input variables related to SOC estimation, including total voltage, total current, lowest cell voltage, highest cell voltage, lowest node temperature, highest node temperature, voltage value of each cell in each module and SOC value, voltage difference value and temperature difference value of the last moment of temperature value of each node in each module, 10 of the SOC value, total voltage total current, lowest cell voltage, highest node temperature, lowest node temperature, average temperature, total voltage variation and SOC variation at the last moment are selected as input variables, the SOC at the moment is taken as an output variable, the number of hidden layer nodes is set to be 30, and an input matrix of a network is an input matrix of the network
X＝(x1,x2,x3,x4,x5,x6,x7,x8,x9,x10)
The output function of the RBF neural network is:
wherein m represents the number of hidden layer neuron nodes, namely the number of radial basis function centers; the coefficient represents the weight of the connection of the hidden layer to the output layer, and b represents the threshold value of the output layer;
wherein,radial basis function, x-c, representing the hidden layerjI denotes the Euclidean distance, cj(cj∈Rn) Representing the center of the hidden layer radial basis function; r isj(rj∈ R) represents the width of the radial basis function;
resulting in an output layer matrix representation:
y＝HW+e
where y represents the desired output of the output layer, e represents the error between the desired output y and the network output f (x), W represents the connection weight of the hidden layer to the output layer, and H represents the regression matrix.
In the RBF network structure, for training samples, the performance index is usually taken as
The index E is a function of radial basis center, width and weight, and the training of the RBF network aims at a group of samples to make E tend to be minimum;
the output of the output layer is obtained as:
due to the adoption of the technical scheme, the invention has the following advantages:
the battery monitoring module provides the battery residual capacity information, the battery residual time information and the battery health condition information under various working conditions. And the acquired battery data is transmitted to a far-end receiving terminal through a wireless data transmission module consisting of a single chip microcomputer and a wireless transceiver chip, the receiving terminal processes signals and displays information such as the remaining service time of the battery and the health condition of the battery in a display, and buzzing alarm is given when the remaining electric quantity is found to be lower than a preset threshold value, so that the remote monitoring of the electric quantity of the battery is realized. The invention is used for remote monitoring of the residual electric quantity of the battery, the monitoring device has simple structure, the monitoring method is convenient to operate, and the specific numerical value of the electric charge storage quantity of the storage battery can be accurately obtained, so that the remote online accurate monitoring of the electric quantity of the battery is thoroughly realized.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof.
Drawings
The drawings of the present invention are described below.
FIG. 1 is a schematic structural view of the present invention;
FIG. 2 is a flow chart of a method of the present invention;
fig. 3 is a wiring diagram of nRF24L01 chip.
Detailed Description
The invention is further illustrated by the following figures and examples.
The SOC of the battery is used to reflect the remaining capacity of the battery, which is a relatively unified understanding at home and abroad, and is defined numerically as the ratio of the remaining capacity of the battery to the total capacity of the battery:
soc＝[Qm-Q(In)]/Qm
Q(In＝t∫Indt)
wherein Qm is the maximum discharge capacity of the storage battery, and refers to the maximum ampere-hour value which can be released from the operation of the battery after the battery is fully charged to the full discharge of the battery under the room temperature condition, and is expressed as the product of the standard discharge current and the discharge time; q (in) is the amount of electricity discharged from the battery at time t under the standard discharge current I0.
As shown in figure 1, in the invention, the battery SOC data is calculated through a MAX177050 battery fuel gauge, after the data acquisition calculation is completed, the acquired data is transmitted by a wireless transmission module, nRF24L01 chips are used for wireless data transceiving in wireless transmission, and the nRF24L01 is a single-chip wireless transceiver chip which is produced by NORDIC and works in ISM frequency band of 2.4 GHz-2.5 GHz. The wireless transceiver includes: a frequency generator, an enhanced SchockBurst mode controller, a power amplifier, a crystal oscillator, a modulator, and a demodulator. The chip wiring diagram is shown in fig. 3.
On the remote terminal equipment, a wireless receiving end receives the transmitted wireless data, the data are processed and analyzed by the second single chip microcomputer, and the data are displayed on a display to realize remote monitoring of the residual electric quantity of the battery. When the residual electric quantity is lower than 10%, the alarm device is triggered to realize buzzing alarm. The flow chart is shown in fig. 2.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (1)
1. A remote monitoring method for residual electric quantity of a rechargeable lithium battery is characterized by comprising the following specific steps:
1) monitoring and recording the state information of the tested rechargeable lithium battery through a battery monitoring module;
2) the battery monitoring module carries out information processing on the monitored state information of the rechargeable lithium battery to obtain battery residual capacity information, battery residual time information and battery health condition information;
3) the battery residual capacity information, the battery residual time information and the battery health condition information obtained after processing are sent to a second single chip microcomputer through a first single chip microcomputer, a wireless sending end and a wireless receiving end;
4) the second singlechip extracts the battery residual capacity information, the battery residual time information and the battery health condition information, sends the information to the display, compares the information with a preset threshold value, and sends a control instruction to control the system auxiliary control alarm module;
step 1) the state information of the rechargeable lithium battery comprises the current output voltage information, the current output information, the ambient temperature information, the input electric quantity information during charging and the output electric quantity information during discharging of the battery;
the specific method for processing the information of the state information of the rechargeable lithium battery in the step 2) is as follows: adopting an RBF neural network to carry out online prediction on the SOC of the battery;
the specific method for online predicting the SOC of the battery by adopting the RBF neural network comprises the following steps: establishing an SOC prediction model based on an RBF neural network, and designing mainly from three aspects of a network level, a training level and a node level; in the input variables related to SOC estimation, including total voltage, total current, lowest cell voltage, highest cell voltage, lowest node temperature, highest node temperature, voltage value of each cell in each module and SOC value, voltage difference value and temperature difference value of the last moment of temperature value of each node in each module, 10 of the SOC value, total voltage total current, lowest cell voltage, highest node temperature, lowest node temperature, average temperature, total voltage variation and SOC variation at the last moment are selected as input variables, the SOC at the moment is taken as an output variable, the number of hidden layer nodes is set to be 30, and an input matrix of a network is an input matrix of the network
X＝(x1,x2,x3,x4,x5,x6,x7,x8,x9,x10)
The output function of the RBF neural network is:
wherein m represents the number of hidden layer neuron nodes, namely the number of radial basis function centers; the coefficient represents the weight of the connection of the hidden layer to the output layer, and b represents the threshold value of the output layer;
wherein,radial basis function, x-c, representing the hidden layerjI denotes the Euclidean distance, cj(cj∈Rn) Representing the center of the hidden layer radial basis function; r isj(rj∈ R) represents the width of the radial basis function;
resulting in an output layer matrix representation:
y＝HW+e
wherein y represents the expected output of the output layer, e represents the error between the expected output y and the network output f (x), W represents the connection weight of the hidden layer and the output layer, and H represents the regression matrix;
in the RBF network structure, for training samples, the performance index is usually taken as
The index E is a function of radial basis center, width and weight, and the training of the RBF network aims at a group of samples to make E tend to be minimum;
the output of the output layer is obtained as:
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| CN105823989A (en) * | 2016-05-04 | 2016-08-03 | 安徽工程大学 | Improved battery SOC prediction method for RBF neural network |
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| CN112793461B (en) * | 2019-11-13 | 2022-04-29 | 上海度普新能源科技有限公司 | Battery management system, electric vehicle and initial state determination method of lithium battery |
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