CN104375090A - Rechargeable lithium battery remaining capacity remote monitoring device and monitoring method thereof - Google Patents

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

A kind of charged lithium cells dump energy remote monitoring device and monitoring method thereof

Technical field

The present invention relates to battery status detection field, particularly a kind of remote monitoring device of charged lithium cells dump energy and method.

Background technology

At present, from electric power direct current cabinet power supply to telecommunications and mobile communication equipment back-up source, from electric bicycle to electric automobile, from mobile phone to notebook computer, the application of rechargeable battery is found everywhere.The maximum feature of rechargeable battery is exactly can discharge and recharge, and correct discharge and recharge can utilize battery fully, and incorrect discharge and recharge can be shortened battery and even damage battery.In order to reasonably discharge and recharge in time, the fuel instruction understanding automobile is needed as driver, user wishes the state-of-charge SOC (state of charge) understanding rechargeable battery, especially in some important occasions, the dump energy instruction of rechargeable battery has been the index of a particular importance.

But cannot be close for some particular band rechargeable battery equipment people possibly, or close to these equipment more at need, very difficult by utilizing classic method understanding or detecting battery dump energy in these equipment, such as along with energy saving and environment friendly solar recharging street lamp is fast-developing, this environmental-friendly street lamp is installed in many places, but due to the instability of sun power, also there is instability problem in street lamp top cell dump energy, therefore sometimes wish to monitor top cell dump energy situation, but because street lamp is higher, traditional wire monitoring battery dump energy method obviously can not satisfy the demands, therefore need to take a kind of remote monitoring mode could realize the monitoring of now battery dump energy, along with the fast development of wireless communication technology, for battery electric quantity remote monitoring provides possibility.

Summary of the invention

One object of the present invention is just to provide a kind of charged lithium cells dump energy remote monitoring device, and the charged lithium cells status information that monitoring calculation can obtain by it is sent to far-end by the mode of radio communication, is convenient to Real-Time Monitoring.

This object of the present invention is realized by such technical scheme, and it includes battery detection module, the first single-chip microcomputer, wireless transmission end, wireless interface receiving end, second singlechip, system supplymentary control alarm module and display;

The data monitoring end of battery detection module is connected with tested charged lithium cells, battery detection module monitors to data be sent to wireless interface receiving end by single-chip microcomputer and wireless transmission end, the data received are sent to second singlechip by wireless interface receiving end, after second singlechip process, send control information and control alarm module to system supplymentary, send display information to display simultaneously.

Further, described battery detection module is MAX177050 battery meter.

Further, described wireless transmission end and wireless interface receiving end are nrf24L01 wireless transceiver chip.

Further, described system supplymentary controls alarm module is hummer.

Another object of the present invention is just to provide a kind of charged lithium cells dump energy remote monitoring method, it can carry out monitoring analysis at the near-end of charged lithium cells to battery status information, obtain battery remaining power information, remaining battery temporal information and battery health information, and be sent to monitoring client at a distance by the mode of radio communication.

This object of the present invention is realized by such technical scheme, and concrete steps are:

1) status information of tested charged lithium cells is recorded by battery detection module monitors;

2) battery detection module carries out information processing to the charged lithium cells status information monitored, and obtains battery remaining power information, remaining battery temporal information and battery health information;

3) the battery remaining power information, remaining battery temporal information and the battery health information that obtain after process are sent to second singlechip by the first single-chip microcomputer, wireless transmission end, wireless interface receiving end;

4) second singlechip extracts battery remaining power information, remaining battery temporal information and battery health information, is sent to display, simultaneously compared with pre-set threshold value, sends steering order control system auxiliary control alarm module.

Further, step 1) the input information about power of described charged lithium cells status information when including the current output voltage information of battery, output current information, ambient temperature information, charging and electric discharge time output information about power.

Further, step 2) described in the concrete grammar that charged lithium cells status information carries out information processing be: adopt RBF neural to carry out on-line prediction to battery SOC.

Further, employing RBF neural to the concrete grammar that battery SOC carries out on-line prediction is: set up SOC forecast model based on RBF neural, mainly designs from network level, training level and node level three aspects, relevant input variable is being estimated with soc, comprise total voltage, total current, minimum monomer voltage, most high monomer voltage, minimum node temperature, most high node temperature, in modules in the magnitude of voltage of each cell and each module each node temperature value on the soc value in a moment, in voltage difference and temperature gap, choose a moment soc value, total voltage total current, minimum monomer voltage, most high monomer voltage, most high node temperature, minimum node temperature, medial temperature, total voltage variable quantity, SOC variable quantity totally 10 be input variable, with this moment SOC for output variable, arranging node in hidden layer is 30, the input matrix of its network is

X＝(x ₁,x ₂,x ₃,x ₄,x ₅,x ₆,x ₇,x ₈,x ₉,x ₁₀)

The output function of RBF neural is:

$f\left(x\right)=b+\underset{j=1}{\overset{m}{\mathrm{\Σ}}}{\mathrm{\ω}}_j{h}_j\left(x\right)$

Wherein, m represents hidden layer neuron nodes, i.e. the number at radial basis function center; Coefficient represents the connection weight of hidden layer to output layer, and b represents the threshold values of output layer;

Wherein, represent the radial basis function of hidden layer, || x-c _j|| represent Euclidean distance, c _j(c _j∈ R ⁿ) represent the center of hidden layer radial basis function; r _j(r _j∈ R) represent the width of radial basis function;

Obtain output layer matrix representation:

y＝HW+e

Wherein, y represents the desired output of output layer, and e represents that desired output y and network export the error between f (x), and W represents the connection weight of hidden layer and output layer, and H represents regression matrix.

In RBF network structure, for training sample, usually getting performance index is

$E=\frac{1}{2}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{(y_i-f\left(x_i\right))}^2$

Index E is the function about radial basis center, width and weights, and the training of RBF network is exactly for one group of sample, makes E be tending towards minimum;

The output obtaining output layer is:

Owing to have employed technique scheme, the present invention has following advantage:

The present invention is by being provided battery remaining power information, remaining battery temporal information and the battery health information under various condition of work by battery detection module.And by the wireless data transfer module be made up of single-chip microcomputer and wireless transceiver chip, gathered battery data is sent to far-end receiving terminal, the information such as remaining battery service time and battery health also show by receiving terminal processing signals in the display, and when finding dump energy lower than pre-set threshold value, buzzing is reported to the police, thus realizes battery electric quantity remote monitoring.The present invention is used for battery dump energy remote monitoring, and monitoring device forms simple, and monitoring method is easy to operate, can be as accurate as the concrete numerical value of the battery power storage quantity of electric charge, makes monitoring cell electricity quantity thoroughly realize remote online precise monitoring.

Other advantages of the present invention, target and feature will be set forth to a certain extent in the following description, and to a certain extent, based on will be apparent to those skilled in the art to investigating hereafter, or can be instructed from the practice of the present invention.Target of the present invention and other advantages can be realized by instructions below and claims and be obtained.

Accompanying drawing explanation

Accompanying drawing of the present invention is described as follows.

Fig. 1 is structural representation of the present invention;

Fig. 2 is method flow diagram of the present invention;

Fig. 3 is nRF24L01 chip wiring diagram.

Embodiment

Below in conjunction with drawings and Examples, the invention will be further described.

The state-of-charge SOC of battery is used to the residual capacity situation reflecting battery, and this is understanding more unified both at home and abroad at present, and it is numerically defined as the remaining electricity of battery accounts for the ratio of battery total capacity:

soc＝[Q _m-Q(I _n)]/Q _m

Q(I _n＝t∫I _ndt)

In formula: Qm is accumulator maximum discharge capacity, refer at ambient temperature, battery is started working till battery discharges completely after charging completely, its maximum ampere-hour numerical value that can release, the product of asking when being expressed as standard discharge current and electric discharge; The electricity that the battery that Q (In) is t under standard discharge current I0 discharges.

As shown in Figure 1, battery SOC data are calculated by MAX177050 battery meter in the present invention, after completing data acquisition calculating, by wireless transport module, institute's image data is transferred out, wireless transmission uses nrf24l01 chip to carry out wireless data transceiving, and nRF24L01 is the monolithic wireless transceiver chip being operated in the ISM band of 2.4GHz ~ 2.5GHz of being produced by NORDIC.Wireless transceiver comprises: frequency generator, enhancement mode SchockBurst mode controller, power amplifier, crystal oscillator, modulator and demodulator.Its chip wiring diagram as shown in Figure 3.

On distance terminal equipment, have wireless receiving end to receive send wireless data, by second singlechip Treatment Analysis, and data display is realized battery dump energy remote monitoring over the display.When discovery dump energy is lower than 10% trigger alarm device, realizes buzzing and report to the police.Its process flow diagram as shown in Figure 2.

What finally illustrate is, above embodiment is only in order to illustrate technical scheme of the present invention and unrestricted, although with reference to preferred embodiment to invention has been detailed description, those of ordinary skill in the art is to be understood that, can modify to technical scheme of the present invention or equivalent replacement, and not departing from aim and the scope of the technical program, it all should be encompassed in the middle of right of the present invention.

Claims (8)

1. a charged lithium cells dump energy remote monitoring device, is characterized in that: described device includes battery detection module, the first single-chip microcomputer, wireless transmission end, wireless interface receiving end, second singlechip, system supplymentary control alarm module and display;

The data monitoring end of battery detection module is connected with tested charged lithium cells, battery detection module monitors to data be sent to wireless interface receiving end by single-chip microcomputer and wireless transmission end, the data received are sent to second singlechip by wireless interface receiving end, after second singlechip process, send control information and control alarm module to system supplymentary, send display information to display simultaneously.

2. charged lithium cells dump energy remote monitoring device as claimed in claim 1, is characterized in that: described battery detection module is MAX177050 battery meter.

3. charged lithium cells dump energy remote monitoring device as claimed in claim 1, is characterized in that: described wireless transmission end and wireless interface receiving end are nrf24L01 wireless transceiver chip.

4. charged lithium cells dump energy remote monitoring device as claimed in claim 1, is characterized in that: it is hummer that described system supplymentary controls alarm module.

5. utilize device described in claim 1 to 4 any one to carry out the method for charged lithium cells dump energy remote monitoring, it is characterized in that, concrete steps are as follows:

1) status information of tested charged lithium cells is recorded by battery detection module monitors;

2) battery detection module carries out information processing to the charged lithium cells status information monitored, and obtains battery remaining power information, remaining battery temporal information and battery health information;

3) the battery remaining power information, remaining battery temporal information and the battery health information that obtain after process are sent to second singlechip by the first single-chip microcomputer, wireless transmission end, wireless interface receiving end;

4) second singlechip extracts battery remaining power information, remaining battery temporal information and battery health information, is sent to display, simultaneously compared with pre-set threshold value, sends steering order control system auxiliary control alarm module.

6. charged lithium cells dump energy remote monitoring method as claimed in claim 5, is characterized in that: step 1) the input information about power of described charged lithium cells status information when including the current output voltage information of battery, output current information, ambient temperature information, charging and electric discharge time output information about power.

7. charged lithium cells dump energy remote monitoring method as claimed in claim 6, it is characterized in that, step 2) described in the concrete grammar that charged lithium cells status information carries out information processing be: adopt RBF neural to carry out on-line prediction to battery SOC.

8. charged lithium cells dump energy remote monitoring method as claimed in claim 7, it is characterized in that, employing RBF neural to the concrete grammar that battery SOC carries out on-line prediction is: set up SOC forecast model based on RBF neural, mainly designs from network level, training level and node level three aspects, relevant input variable is being estimated with soc, comprise total voltage, total current, minimum monomer voltage, most high monomer voltage, minimum node temperature, most high node temperature, in modules in the magnitude of voltage of each cell and each module each node temperature value on the soc value in a moment, in voltage difference and temperature gap, choose a moment soc value, total voltage total current, minimum monomer voltage, most high monomer voltage, most high node temperature, minimum node temperature, medial temperature, total voltage variable quantity, SOC variable quantity totally 10 be input variable, with this moment SOC for output variable, arranging node in hidden layer is 30, the input matrix of its network is

X＝(x ₁,x ₂,x ₃,x ₄,x ₅,x ₆,x ₇,x ₈,x ₉,x ₁₀)

The output function of RBF neural is:

$f\left(x\right)=b+\underset{j=1}{\overset{m}{\mathrm{\Σ}}}{\mathrm{\ω}}_j{h}_j\left(x\right)$

Wherein, m represents hidden layer neuron nodes, i.e. the number at radial basis function center; Coefficient represents the connection weight of hidden layer to output layer, and b represents the threshold values of output layer;

Wherein, represent the radial basis function of hidden layer, || x-c _j|| represent Euclidean distance, c _j(c _j∈ R ⁿ) represent the center of hidden layer radial basis function; r _j(r _j∈ R) represent the width of radial basis function;

Obtain output layer matrix representation:

y＝HW+e

Wherein, y represents the desired output of output layer, and e represents that desired output y and network export the error between f (x), and W represents the connection weight of hidden layer and output layer, and H represents regression matrix.

In RBF network structure, for training sample, usually getting performance index is

$E=\frac{1}{2}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{(y_i-f\left(x_i\right))}^2$

Index E is the function about radial basis center, width and weights, and the training of RBF network is exactly for one group of sample, makes E be tending towards minimum;

The output obtaining output layer is: