KR20220052585A - Lithium Battery module applied to electric wheelchairs - Google Patents

Abstract

The present invention relates to a lithium battery module with a BMS applied to a medical electric assistive device, and more specifically, to a battery module for providing power to a medical electric assistive device, comprising: at least one battery part; a sensor part for measuring the voltage and current of the battery part and outputting measured values; an SOC estimation part for constituting a battery-equivalent circuit, calculating a first equation, which includes the state of charge (SOC) of the battery part and the polarization capacity of the battery-equivalent circuit as state variables, and a second equation, which includes the observed value of the SOC of the battery part as an output variable, and applying the voltage and current values outputted from the sensor part and the first and the second equation to extended Kalman filter algorithm to estimate the SOC of the battery part at a preset cycle; and a display part for displaying the SOC estimated by the SOC estimation part. Energy loss can be minimized and the overcharging and over discharging of a battery can be prevented, thereby improving the battery's operational life.

Description

BMS-mounted lithium battery module applied to electric wheelchairs for medical use

The present invention estimates a more reliable SOC of a battery using an Extended Kalman Filter algorithm and deep learning learning, and applies a cut-off mechanism for voltage and temperature to prevent overcharging and discharging And it relates to a BMS-mounted lithium battery module applied to a medical powertrain that minimizes energy loss by utilizing battery cell balancing.

In general, the electrochemical performance and lifespan of a battery cell may occur as a process deviation generated during the manufacturing process of a battery cell and a voltage difference between battery cells are maximized as charging/discharging is repeated. There is a need to manage these deviations to maintain battery life and performance.

To this end, a battery management system (BMS) for managing the state and performance of the battery is provided. BMS measures the current, voltage, and temperature of the battery, estimates the remaining capacity of the battery based on this, and controls the SOC to maximize fuel consumption efficiency. In order to accurately control the SOC, it is necessary to accurately measure the SOC of a battery that is charging and discharging.

As a method of measuring the SOC of a battery in the conventional BMS, there is a method of estimating the SOC of the battery by accumulating the charging and discharging currents flowing through the battery. There is an SOC estimation method called EKF (Extended Kalman Filter) SOC estimation algorithm as a method of estimating the SOC through active correction by modeling the battery electrically and comparing the theoretical output value and the actual output value of the battery model. The EKF SOC estimation algorithm has a low maximum SOC error of 3% at room temperature and is widely used to estimate the SOC of a battery because it enables stable SOC estimation regardless of power patterns. The contents of SOC estimation are disclosed in Korean Patent Registration No. 10-1915183.

Republic of Korea Patent No. 1920220

The problem to be solved by the present invention is to eliminate the risk factors for accidents caused by overcharging and discharging of lithium batteries applied to medical powertrains and the elimination of factors that decrease the service life. In addition, it is intended to prevent overheating due to overcharging by minimizing the voltage deviation. In addition, in order to maintain the efficient performance of the battery, the battery balancing module is applied to the BMS and a deep learning algorithm is added to the extended Kalman filter tracking technology to estimate the SOC of the battery more reliably.

The present invention provides a BMS-mounted lithium battery module applied to a medical power supply, comprising: a battery unit provided with at least one; a first sensor unit measuring the voltage and current of the battery unit and outputting a measured value; A first equation constituting a battery equivalent circuit, including the state of charge (SOC) of the battery unit and a polarization capacity of the battery equivalent circuit as state variables, and a second equation including the SOC observation value of the battery unit as an output variable Calculating, applying the voltage and current values output from the sensor unit and the first and second equations to an extended Kalman filter algorithm to estimate the state of charge (SOC) for the battery unit at a preset period an SOC estimator; and a display unit displaying the SOC estimated by the SOC estimator.

As an example, the SOC estimator sets the output data according to the measurement of the sensor unit and the SOC estimated by the SOC estimator as an input value and an output value, respectively, according to a deep learning algorithm, which is one of artificial neural network techniques. and constructing a residual amount prediction model trained by applying at least one selected from the number of times of charging/discharging of the battery unit, temperature, and period of use as a variable value, and estimating the SOC for the battery unit through this.

As an example, the battery cells constituting the battery unit are controlled to be charged in a constant current mode, and when the voltage output from the sensor unit reaches an end-of-charge voltage during charging in the constant current mode, It characterized in that it further comprises; a control unit for controlling to switch to a constant voltage mode (constant voltage mode).

As an example, a second sensor unit for measuring the voltage of individual battery cells constituting the battery unit and outputting a measured value is further included, wherein the control unit is configured to measure the voltage of each battery cell from the voltage output from the second sensor unit. A state of charge (SOC) is calculated, a reference cell and a target group are selected based on the calculated SOC, and one battery cell among battery cells belonging to the selected target group is selected as a target cell, so that the SOC of the reference cell and the target It is characterized in that a balancing time is calculated according to a difference in SOC of the cells, and balancing of the battery cells belonging to the target group is performed during the calculated balancing time.

In an embodiment of the present invention, it is possible to eliminate the risk factors for accidents caused by overcharging and discharging of the lithium battery and the factors for reducing the service life for the stable operation of the electric protection mechanism. In addition, it provides to prevent overheating due to overcharging by minimizing voltage deviation, applies a battery balancing module to the BMS to maintain efficient battery performance, and adds a deep learning algorithm to the extended Kalman filter tracking technology to provide a more reliable battery can provide an estimate of the SOC of

In addition, according to the present invention, the battery module can estimate the SOC of the battery more reliable by adding a deep learning algorithm to the extended Kalman filter tracking technology. In addition, by actively utilizing the balancing of battery cells based on the calculated SOC, it is possible to exclude accident risk factors accompanying overcharge and discharge and minimize energy loss.

1 is a perspective view of a power securing mechanism to which a battery module of the present invention is applied.
2 is a block diagram showing the configuration of the battery module of the present invention.

Hereinafter, the configuration and operation of the present invention will be described in more detail based on the accompanying drawings. In describing the present invention, the terms or words used in the present specification and claims are based on the principle that the inventor can appropriately define the concept of the term in order to best describe his invention. It should be interpreted as meaning and concept consistent with the technical idea of

The present invention relates to a lithium battery module 200 applied to the electric protection mechanism 1 and will be described in detail based on the drawings below.

First, the configuration of the transmission mechanism (1) will be described.

Referring to FIG. 1 , the electric securing mechanism 1 may have a structure of a conventional electric securing unit 1 in which a driving means, a battery 20 and a seat 50 are provided. As shown in FIG. 1, the electric securing mechanism 1 is an electric motor comprising a frame 10, a driving wheel 30, a driving means including a driving motor, and a battery 20 for supplying power to the driving means. It may be a guarantee (1).

The frame 10 forms the skeleton of the electric securing mechanism 1 and provides a space in which other components of the driving means or the battery 20 can be mounted or combined.

The traveling wheel 30 is mounted on the front or rear of the frame 10 . For example, the driving wheel 30 may include a pair of front wheels and a pair of rear wheels. Here, the pair of rear wheels may be a driving wheel 60 that is directly connected to the rotation shaft of the driving motor to receive driving force, and the pair of front wheels may be a steering wheel made of a caster capable of free rotation. .

The driving motor is coupled to be supported by the frame 10 and the rotating shaft is directly connected to a pair of rear wheels to transmit driving force.

The battery 20 may supply power to each of the components constituting the driving motor described above or the device of the invention described below.

The electric securing mechanism 1 may include components provided in a wheelchair having a general shape such as the seat 50, a backrest, an armrest, and the like, and the control manipulation unit 40 for driving control may be configured in a manner that is fastened to the armrest.

In addition, the driving means may further include a driving module for controlling driving by a user operation.

Although not shown in the drawing, the driving module includes a steering motor that rotates the steering wheel on a plane, a driving motor that transmits power to the driving wheel 60, and a battery 20 that supplies power to the steering wheel and the driving motor. It may include a power supply unit for supplying, and a manipulation unit 40 for controlling the traveling direction and speed of the electric securing mechanism 1 by selectively driving the steering motor and the traveling motor in association with an input signal according to a user manipulation.

The electric securing mechanism 1 of the present invention is not limited to the structure of the above-described embodiment, and it is natural that a suitable one can be selected from various electric structures according to the known art.

Hereinafter, the lithium battery module 200 for the electric protection device 1 of the present invention will be described.

The battery 20 applied to the electric protection mechanism 1 of the present invention is not particularly limited in type. For example, it may be composed of lithium ion, lithium polymer, nickel cadmium, nickel hydrogen, nickel zinc, and the like.

The lithium battery module 200 of the present invention may include at least one battery unit in which a plurality of battery cells are connected in series or in parallel. The voltages of the initial battery cells may be substantially the same. However, it may be difficult to technically produce exactly identical cells. Due to this, there may be some battery cells having different capacity and impedance values of the battery cells. Overcharge or overdischarge may occur in some battery cells, which may cause a difference in charging voltage among the battery cells.

Specifically, the lithium battery module 200 is to prevent the battery 20 from being shortened due to the voltage difference or the residual capacity difference, and includes at least one battery unit; A sensor unit 80 that measures the voltage and current of the battery unit and outputs a measured value; may be provided.

An SOC estimator 90 for the battery unit may be provided. The battery 20 equivalent circuit may be configured in the SOC estimator 90, and the first equation including the SOC of the battery part and the polarization capacity of the battery 20 equivalent circuit as state variables and the SOC observation value of the battery part are output variables A second equation including

The SOC estimating unit 90 applies an extended Kalman filter algorithm to the voltage and current values output from the sensor unit 80 and the first and second equations, so that the SOC ( State of Charge) can be estimated. In addition, a display unit 70 for displaying the SOC estimated by the SOC estimator 90 may be provided.

The extended Kalman filter is a method capable of performing SOC estimation even in a region in which the SOC of the battery 20 has nonlinearity. The nonlinear characteristic of the battery 20 is linearized every time and step by step to estimate the state of charge and discharge.

A linear Kalman filter (LKF) is widely used for estimating the SOC of a battery cell, but when estimating the state of the battery 20, which is a nonlinear system, using the LKF method, it may be greatly influenced by the surrounding environment. . The LKF method is inaccurate at a point less than about 20% and a region exceeding 80% near the charge/discharge end point where the nonlinearity of the battery 20 increases due to an increase in the number of charge/discharge of the battery 20 or deterioration and aging, etc. Battery 20 represents the SOC estimate.

The SOC estimator 90 may include an equivalent circuit of the battery 20 . The SOC estimator 90 calculates a first equation including the SOC of the battery unit and the polarization capacity of the battery 20 equivalent circuit as a state variable and a second equation including the SOC observation value of the battery unit as an output variable, and the sensor unit By applying the voltage and current values output in (80) and the first and second equations to the extended Kalman filter algorithm, the SOC for the battery unit may be estimated at a preset period.

The SOC estimator 90 can learn, as an input value and an output value, the measurement data according to the measurement of the sensor unit 80 and the measurement residual amount, respectively, according to a deep learning algorithm, which is one of the artificial neural network techniques, It may be characterized by estimating the remaining amount of charge through learning in which one or more variable data selected among the number of times of charge/discharge, temperature, and period of use is reflected by building the residual amount prediction model.

The SOC estimator 90 may estimate the SOC of the battery 20 by applying the data collected from the sensor unit 80 to a previously learned data analysis technique. To this end, the data analysis technique may analyze the degree of deterioration of the battery 20 and estimate the SOC state based on the AI deep learning algorithm. AI deep learning algorithm is a technology that allows a computer to judge and learn like a human, and to cluster or classify objects or data.

As an implementation to prevent overcharging or overdischarging, when an end-of-charge voltage is reached through a voltage sensor during constant current charging in a constant current mode, it is switched to a constant voltage mode. It may further include a control unit 100 to control. Therefore, when the final charging voltage is reached, the overcharge and overdischarge may be prevented by setting the final voltage by subtracting the negative threshold potential, which is a value causing a phase change of the negative active material of the battery 20 from the full charge voltage.

A means for balancing individual battery cells constituting the battery unit may be further included. To this end, the control unit 100 further includes a second sensor unit 80 that measures the voltage of individual battery cells constituting the battery unit and outputs a measured value, wherein the control unit 100 includes the second sensor unit A state of charge (SOC) for each battery cell is calculated from the voltage output in step 80 , a reference cell and a target group are selected based on the calculated SOC, and one battery cell among the battery cells belonging to the selected target group may be selected as a target cell to calculate a balancing time according to a difference between the SOC of the reference cell and the SOC of the target cell, and to perform balancing of battery cells belonging to the target group during the calculated balancing time.

The controller 100 may be configured to select, as a target group, a battery cell whose calculated SOC is out of a preset normal range from the SOC of the reference cell among the battery cells.

The control unit 100 may be configured to set any one of the SOC calculated by the second sensor unit 80 as a reference, and to select a battery cell that deviates from the standard as a target group. That is, the controller 100 may first select a reference cell from among the battery cells, and select one or more battery cells having an SOC outside a preset normal range from the SOC of the reference cell among the remaining battery cells as a target group.

The controller 100 may select, as a reference cell, a battery cell having the lowest calculated SOC among a plurality of battery cells. After selecting a battery cell having a minimum SOC as a reference cell, the controller 100 may select a target group. The controller 100 may select a battery cell having a minimum SOC among battery cells belonging to the selected target group as the target cell. That is, the controller 100 selects a battery cell having a minimum SOC among battery cells included in the lithium battery module 200 as a reference cell, and selects a battery cell having a minimum SOC among battery cells belonging to a target group as a target cell. there is.

The controller 100 may be configured to discharge the battery cells belonging to the target group during the balancing time calculated according to the difference between the SOC of the reference cell and the SOC of the target cell.

The controller 100 may select and discharge only the battery cells belonging to the target group among the battery cells included in the lithium battery module 200 during the calculated balancing time.

For example, among the battery cells included in the lithium battery module 200 , a battery cell not belonging to the target group may not be discharged during the balancing time, unlike the battery cell belonging to the target group.

After the repetitive balancing is finally completed, the SOC of the battery cell belonging to the target group is included within a preset normal range from the SOC of the reference cell, so that overdischarge in the battery cell by balancing the lithium battery module 200 is prevented in advance can do.

The controller 100 may select a battery cell having the maximum calculated SOC as a reference cell among the battery cells. After selecting the battery cell having the maximum SOC as the reference cell, the controller 100 may select a target group and select a battery cell having the maximum SOC among battery cells belonging to the selected target group as the target cell. That is, the control unit 100 selects a battery cell having the maximum SOC among the battery cells included in the lithium battery module 200 of the present invention as a reference cell, and selects a battery cell having the maximum SOC among the battery cells belonging to the target group as the target cell. can be selected

The controller 100 may be configured to charge the battery cells belonging to the target group during the balancing time calculated according to the difference between the SOC of the reference cell and the SOC of the target cell.

In addition, the control unit 100 may be configured to perform balancing of battery cells belonging to the target battery unit while reducing the balancing time, and the control unit 100 checks whether balancing is completed based on the balancing time, and the balancing is completed In this case, it may be configured to transmit a voltage measurement signal to the sensor unit 80 .

The present invention as described above has been described with reference to one embodiment shown in the drawings, but this is merely exemplary, and various modifications and equivalent other embodiments are possible by those skilled in the art. will understand Accordingly, the true technical protection scope of the present invention should be defined by the technical spirit of the appended claims.

1 : Electric protection device 10 : Frame
20: battery 30: driving wheel
40: control unit 50: seat
60: drive wheel 70: display unit
80: sensor unit 90: SOC estimation unit
100: control unit 200: battery module

Claims (4)

In the lithium battery module for providing electric power to the medical power equipment,
at least one battery unit provided;
a first sensor unit measuring the voltage and current of the battery unit and outputting a measured value;
A first equation constituting a battery equivalent circuit, including the state of charge (SOC) of the battery unit and a polarization capacity of the battery equivalent circuit as state variables, and a second equation including the SOC observation value of the battery unit as an output variable Calculating, applying the voltage and current values output from the sensor unit and the first and second equations to an extended Kalman filter algorithm to estimate the state of charge (SOC) for the battery unit at a preset period an SOC estimator; and
A BMS-mounted lithium battery module applied to a medical power supply device comprising a; a display unit for displaying the SOC estimated by the SOC estimating unit.
According to claim 1,
The SOC estimating unit,
According to a deep learning algorithm, which is one of artificial neural network techniques, the output data according to the measurement of the sensor unit and the SOC estimated by the SOC estimator are set as input values and output values, respectively, and the number of times of charging and discharging the battery unit BMS-equipped lithium battery applied to a medical powertrain, characterized in that by applying one or more selected from , temperature, and period of use as a variable value to build a learned residual quantity prediction model, and estimating the SOC for the battery unit through this module.
According to claim 1,
The battery cells constituting the battery unit are controlled to be charged in a constant current mode, and when the voltage output from the sensor unit reaches an end-of-charge voltage during charging in the constant current mode, the constant voltage mode ( BMS-mounted lithium battery module applied to a medical power supply device, characterized in that it further comprises; a control unit that controls to switch to constant voltage mode).
4. The method of claim 3,
Further comprising; a second sensor unit for measuring the voltage of the individual battery cells constituting the battery unit and outputting the measured value;
The control unit is
Calculates a state of charge (SOC) for each battery cell from the voltage output from the second sensor unit, selects a reference cell and a target group based on the calculated SOC, and selects one of the battery cells belonging to the selected target group. A battery cell is selected as a target cell, a balancing time is calculated according to a difference between the SOC of the reference cell and the SOC of the target cell, and balancing of battery cells belonging to the target group is performed during the calculated balancing time, characterized in that BMS-mounted lithium battery module applied to medical power supplies.

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