KR20220159045A - Method and system for controlling temperature of battery pack - Google Patents

Abstract

The present invention relates to a method and a system for controlling the temperature of a battery pack by using a heater or a cooler. The present invention provides a method for controlling the temperature of a battery pack in which a temperature control unit operated when power is supplied is mounted. The method for controlling the temperature of a battery pack according to the present invention comprises the steps of: setting a target value for an available output of a battery pack; estimating an available output value of the battery pack, based on a peripheral temperature of the battery pack and state information on each of a plurality of batteries included in the battery pack; supplying power to the temperature control unit, based on the estimated available output value; re-estimating the available output value of the battery pack after a predetermined time passes from a time point when power is supplied to the temperature control unit; and shutting off the power supplied to the temperature control unit when the re-estimated available output value reaches the target value, wherein the state information on each of the batteries includes position information on each of the batteries included in the battery pack. Accordingly, the temperature of the battery pack can be controlled with optimal efficiency.

Description

Battery pack temperature control method and system {METHOD AND SYSTEM FOR CONTROLLING TEMPERATURE OF BATTERY PACK}

The present invention relates to a method and system for controlling the temperature of a battery pack using a heater or cooler.

Lithium ion batteries are widely used in various fields due to their light weight. Specifically, lithium ion batteries have been used in portable electronic devices such as mobile phones and laptops, and recently, their use in electric vehicles has been expanded. In order to secure a battery capacity for use in an electric vehicle or the like, a battery pack in which a plurality of lithium ion batteries are densely packed has been used.

Referring to FIG. 1 , an electric vehicle 10 may be equipped with a battery pack 12 for driving an engine 11 and devices included in the vehicle. The battery pack 12 may include a plurality of batteries 13 .

On the other hand, the internal chemical change of the battery appears differently during discharging or charging according to the temperature. Batteries may be exposed to various temperatures depending on their use environment. In particular, the battery 13 built into a vehicle may be frequently exposed to a low or high temperature environment. Battery performance may deteriorate at low or high temperatures, which may have a fatal effect on vehicle operation.

In addition, as the size of a battery pack in which the plurality of batteries 13 are densely packed increases, temperature imbalance occurs between cells inside the battery pack, and thus voltage imbalance may occur between battery cells. The aforementioned temperature imbalance inside the battery pack may lead to a safety problem. Specifically, referring to FIG. 2 , when the battery pack is driven in a natural convection state, the temperature of the battery 13a disposed in the deep portion of the battery pack is higher than that of the battery 13b disposed in the surface portion of the battery pack. .

Conventionally, in order to reduce the effect of temperature on the battery, a heating sheet or a temperature control device capable of adjusting the temperature of the entire battery pack has been used. Since the above-described temperature control means operates using power from a battery pack, when the temperature control means is excessively driven, energy efficiency of the battery is degraded.

Accordingly, research on methods and systems for accurately estimating the state of the battery pack using sensible information and heating or cooling the battery pack with optimum efficiency has recently been conducted.

One object of the present invention is to provide a data learning method capable of accurately estimating the current state of a battery pack in various environments.

Another object of the present invention is to provide a method and system capable of controlling the temperature of a battery pack with optimum efficiency according to the state of the battery pack.

In order to achieve the above object, the present invention provides a temperature control method of a battery pack equipped with a temperature controller that operates when power is supplied. The present invention includes the steps of setting a target value for the available output of a battery pack, estimating the available output value of the battery pack based on the ambient temperature of the battery pack and state information of each of a plurality of batteries included in the battery pack, and the estimated available output. supplying power to the temperature controller based on the output value, re-estimating an available output value of the battery pack after a predetermined time has elapsed since supplying power to the temperature controller, and re-estimating available output values and cutting off power supplied to the temperature controller when the target value is reached, wherein the state information of each of the plurality of batteries includes location information of each of the plurality of batteries included in the battery pack. It is possible to provide a temperature control method of a battery pack, characterized in that.

In an embodiment, the estimating the available output value of the battery pack and the re-estimating the available output value of the battery pack may include location information of each of the plurality of batteries, surface temperature of each of the plurality of batteries, and An available output value of the battery may be estimated based on at least one of the amount of charge of each of the plurality of batteries, the ambient temperature of the battery pack, the thermal energy consumed by the temperature controller, and the predetermined time.

In one embodiment, the present invention includes the steps of sensing the surface temperature of each of the plurality of batteries and the ambient temperature of the battery pack, and sensing the amount of change in current flowing through each of the plurality of batteries after dropping the voltage of each of the plurality of batteries. calculating an available output value of each of the plurality of batteries and an internal resistance value of each of the plurality of batteries using the amount of current change and the amount of voltage drop, and the surface temperature of each of the plurality of batteries, the plurality of The amount of charge of each battery, the ambient temperature of the battery pack, the energy consumed in the temperature controller, the time when power was supplied to the temperature controller, the location information of each of the plurality of batteries, the calculated available output of each of the plurality of batteries The step of performing machine learning for estimating the available output of the battery pack using at least one of the value and the internal resistance value as learning data, wherein the estimating the available output value of the battery is performed based on the machine learning result. It can be.

In an embodiment, the learning data may further include a heat loss calculated based on a surface temperature of each of the plurality of batteries and a temperature around the battery pack.

In one embodiment, the ambient temperature of the battery pack utilized in the step of estimating the available output value of the battery pack based on the ambient temperature of the battery pack and state information of each of the plurality of batteries included in the battery pack and the plurality of The method may further include calculating the predetermined time by using the surface temperature of each battery and the learning data.

In one embodiment, the calculating of the predetermined time may include the battery pack used in the step of estimating an available output value of the battery pack based on the ambient temperature of the battery pack and state information of each of a plurality of batteries in the learning data. Retrieving first data corresponding to the ambient temperature and the surface temperature of each of the plurality of batteries, retrieving second data corresponding to the target value from the learning data, utilizing the first and second data The method may further include calculating an estimated amount of thermal energy consumption and calculating the predetermined time based on the estimated amount of thermal energy consumption.

In addition, the present invention provides a temperature control system for a battery pack. The present invention relates to a battery pack including a plurality of batteries, a temperature control unit mounted on the battery pack and operated when power is supplied, when receiving a target value for the available output of the battery pack, the ambient temperature of the battery pack and the plurality of and a battery pack control unit estimating an available output value of the battery pack based on state information of each battery of the battery pack and supplying power to the temperature controller based on the estimated available output value, wherein the battery pack control unit: The available output value of the battery pack is re-estimated after a predetermined time elapses from the time when power is supplied to the temperature controller, and when the re-estimated available output value reaches the target value, the power supplied to the temperature controller is switched off. and the state information of each of the plurality of batteries includes location information of each of the plurality of batteries included in the battery pack.

As described above, according to the present invention, since machine learning is performed on battery pack output in various ambient environments and in various battery pack states, optimal battery pack output according to the current ambient temperature of the battery pack and the current battery pack state. can be calculated.

In addition, according to the present invention, since the battery pack is heated or cooled so that the battery pack can reach the optimal output calculated through machine learning, power consumption for heating or cooling is minimized and the battery pack is used in any environment. to keep it in optimum condition.

1 is a conceptual diagram illustrating an electric vehicle equipped with a battery pack.
2 is a conceptual diagram illustrating a temperature distribution of a battery pack in a natural convection state.
3 is a conceptual diagram illustrating a battery pack temperature control system according to the present invention.
4 is a flowchart illustrating a temperature control method of a battery pack according to the present invention.
5 and 6 are conceptual diagrams illustrating a machine learning method for available output of a battery pack according to the present invention.
7 is a conceptual diagram illustrating an embodiment of calculating the internal voltage of a battery according to the present invention.
8 is a conceptual diagram illustrating an embodiment of calculating a battery available output according to the present invention.

Hereinafter, the embodiments disclosed in this specification will be described in detail with reference to the accompanying drawings, but the same reference numerals will be assigned to the same or similar components regardless of reference numerals, and overlapping descriptions thereof will be omitted. The suffixes "module" and "unit" for components used in the following description are given or used together in consideration of ease of writing the specification, and do not have meanings or roles that are distinct from each other by themselves. In addition, in describing the embodiments disclosed in this specification, if it is determined that a detailed description of a related known technology may obscure the gist of the embodiment disclosed in this specification, the detailed description thereof will be omitted. In addition, the accompanying drawings are only for easy understanding of the embodiments disclosed in this specification, the technical idea disclosed in this specification is not limited by the accompanying drawings, and all changes included in the spirit and technical scope of the present invention , it should be understood to include equivalents or substitutes.

Terms including ordinal numbers, such as first and second, may be used to describe various components, but the components are not limited by the terms. These terms are only used for the purpose of distinguishing one component from another.

It is understood that when an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, but other elements may exist in the middle. It should be. On the other hand, when an element is referred to as “directly connected” or “directly connected” to another element, it should be understood that no other element exists in the middle.

Singular expressions include plural expressions unless the context clearly dictates otherwise.

In this application, terms such as "comprise" or "have" are intended to designate that there is a feature, number, step, operation, component, part, or combination thereof described in the specification, but one or more other features It should be understood that the presence or addition of numbers, steps, operations, components, parts, or combinations thereof is not precluded.

Hereinafter, a battery pack temperature control system according to the present invention will be described in detail. However, the present invention is not limited to the components described later, and may include more or less components than the components described later.

3 is a conceptual diagram illustrating a temperature control system of a battery pack according to the present invention.

As shown in FIG. 2 , the battery pack temperature control system according to the present invention includes a central controller 100, a battery pack controller 310, a battery pack 320, a temperature controller 330, and a battery temperature sensor 340. ) may include at least one of

Here, the central control unit 100 is a means for controlling the operation of a device (eg, an electric vehicle) equipped with a battery pack. Specifically, the central control unit 100 may include at least one of a communication unit 110, a storage unit 120, a display unit 130, an input unit 140, a sensor unit 150, and a control unit 160.

In the present specification, for convenience of description, the central control unit 100 is described as a control means for controlling the overall operation of an electric vehicle, but is not limited thereto, and the central control unit 100 is a control unit of a device equipped with a battery pack. It can be a control tool.

Components constituting the central control unit 100 will be described.

The communication unit 110 may be configured to communicate with various devices disposed in space by wire or wirelessly. Specifically, the communication unit 110 may be configured to communicate with at least one external server (or external storage, 200). Here, the external server 200, as shown, may be configured to include at least one of a cloud server 210 and a database 220. Machine learning data to be described later may be stored in the cloud server 210 or the database 220 .

Next, the storage unit 120 may be configured to store various information related to the present invention. In the present invention, the storage unit 120 may be included in the battery pack temperature control system itself. Alternatively, at least a portion of the storage unit 120 may mean at least one of the cloud server 210 and the database 220 . That is, it can be understood that the storage unit 120 suffices as long as it is a space for storing necessary information for controlling the battery pack according to the present invention, and there is no physical space limitation. Accordingly, hereinafter, the storage unit 120, the cloud server 210, and the database 220 are not separately distinguished, and are all expressed as the storage unit 120.

Next, the display unit 130 may be configured to output at least one of screen information related to the device in which the battery pack is mounted and screen information related to the state of the battery pack. The display unit 130 is not an essential component in the present invention, and the type of display unit is not limited.

Next, the input unit 140 is for inputting information input from the user, and the input unit 140 may serve as a medium between the user and the battery pack temperature control system. More specifically, the input unit 140 may refer to an input means for receiving a control command for controlling a device in which a battery pack is mounted from a user. For example, in the case of an electric vehicle, the input unit 140 may include at least one of an accelerator pedal and a deceleration pedal.

Next, the sensor unit 150 may include a sensor that senses information related to the device in which the battery pack is mounted. Taking the sensor unit 150 included in an electric vehicle as an example, the sensor unit 150 may include a temperature sensor configured to sense at least one of a vehicle internal temperature and a vehicle external temperature.

In this specification, the sensor unit 150 is used as a means for sensing the ambient temperature of the battery pack. In this specification, the ambient temperature of the battery pack means a temperature sensed while being separated from the battery by a predetermined distance. For example, the ambient temperature of the battery pack may be defined as the air temperature at a location spaced a predetermined distance from the battery pack. As another example, the ambient temperature of the battery pack may be defined as a temperature sensed by a temperature sensor disposed at a predetermined distance from the battery pack.

Next, the controller 160 may be configured to control the overall operation of the device in which the battery pack is mounted. The control unit 160 may process signals, data, information, etc. input or output through the components described above, or provide or process appropriate information or functions to the user.

In one embodiment, the control unit 160 included in the electric vehicle monitors the state of the battery pack while the vehicle is driving, and sets an available output value of the battery pack.

In this specification, the available output value means the maximum available power that a battery pack can produce in a current state. More specifically, the available output of the battery pack may be determined by the available output of each of a plurality of batteries constituting the battery pack.

In this specification, the available output value of a battery is an output value derived considering the size of a current that reaches a cut-off voltage assuming a situation in which the battery is instantaneously discharged. A detailed description of the formula for calculating the available output value will be described later.

The available output value of the battery is a value that varies depending on the amount of charge of the battery and the temperature of the battery, and is a value that is considered important when driving a device equipped with a battery. The controller 160 may calculate available output of the battery pack by calculating available output of each of a plurality of batteries constituting the battery pack.

The aforementioned central control unit 100 may include more or fewer components than the aforementioned components according to the type of device in which the battery pack is mounted.

Next, the battery pack controller 310 is configured to perform control related to driving of the battery. Specifically, the battery pack controller 310 controls at least one of whether the battery pack is driven, an output voltage, and an output current of the battery pack, and controls the temperature of the battery pack.

In this specification, for convenience, the above-described control unit 160 and the battery pack control unit 310 are separately described, but the above-described control unit 160 and the battery pack control unit 310 may be formed of the same control means. That is, it is apparent to those skilled in the art that the control of the battery pack controller 310 described below may also be performed by the controller 160 .

The battery pack 320 may include a plurality of secondary batteries capable of charging and discharging, and each of the plurality of secondary batteries may include an electrochemical cell. The battery pack 320 supplies power to a device in which the battery pack 320 is mounted, and the output voltage and output current of the battery pack 320 are controlled by the battery pack controller 310 .

Meanwhile, a temperature controller 330 may be mounted on the battery pack 320 . The temperature control unit 330 is made to operate when power is supplied. For example, the temperature controller 330 may be a heater or cooler. In this specification, for convenience of explanation, the case where the temperature controller 330 is either a heater or a cooler is described as an example, but the temperature controller 330 may include another one not described in the specification, Both a heater and a cooler may be included.

Meanwhile, the temperature controller 330 may be disposed on or inside the battery pack 320 , or disposed on each surface of a plurality of batteries constituting the battery pack 320 . In addition, the temperature controller 330 may be disposed on or inside the battery pack 320 and may also be disposed on the surface of each of the plurality of batteries. That is, the temperature controller 330 may be configured to adjust at least one of the temperature of the entire battery pack 320 and the temperature of each of a plurality of batteries constituting the battery pack 320 .

In one embodiment, the temperature controller 330 may be disposed on one side of the battery pack 320 to discharge air inside the battery pack 320 to the outside by forming convection inside the battery pack 320. have. However, the shape of the temperature controller 330 is not limited thereto.

As power is supplied to the temperature control unit 330, the temperature of the temperature control unit 330 is changed or the flow of ambient air is changed. For example, the temperature controller 330 may be at least one of a heating sheet, a cooling sheet, a heating fan, and a cooling fan.

Meanwhile, the battery pack controller 310 controls the battery pack so that power of the battery pack 320 is supplied to the temperature controller 330 . The battery pack controller 310 controls at least one of a voltage value and a current value applied to the temperature controller 330 and a time during which power is applied to the temperature controller 330 .

Meanwhile, the battery temperature sensor 340 is configured to sense the surface temperature of each of a plurality of batteries constituting the battery pack. The battery pack controller 310 controls the temperature controller 330 based on the temperature value sensed by the battery temperature sensor 340 .

Hereinafter, a temperature control method of a battery pack using the above-described system will be described.

4 is a flowchart illustrating a temperature control method of a battery pack according to the present invention, and FIGS. 5 and 6 are conceptual diagrams illustrating a machine learning method for available output of a battery pack according to the present invention.

Referring to FIG. 4 , a step of setting a target value for available output of the battery pack ( S110 ) is performed.

The controller 160 included in the central controller 100 may set a target value for the available output of the battery pack as needed while controlling the device.

In an embodiment, the controller 160 may set a target value for available output of the battery pack according to a user's request for acceleration while driving the vehicle. However, the situation in which the controller 160 sets a target value for the available output of the battery pack is not particularly limited.

Then, the controller 160 transmits the set target value to the battery pack controller 310 . When the target value is received, the battery pack controller 310 estimates the currently available power of the battery pack based on the ambient temperature of the battery pack and state information of each of the plurality of batteries.

Here, the ambient temperature of the battery pack means the temperature sensed by the sensor unit 150 described in FIG. 2 .

The state information of each of the plurality of batteries includes at least one of the surface temperature of each battery sensed by the battery temperature sensor 340 described in FIG. 3 and the current charge amount of each battery.

In addition to the above information, the present invention may utilize other information for estimating the available power of the battery pack. This will be described later.

Meanwhile, estimation of available power of the battery pack is performed using a model for estimating available power generated through machine learning. Hereinafter, the machine learning method according to the present invention will be described in detail.

Referring to FIG. 5 , in machine learning according to the present invention, an initial battery surface temperature (MCT0, per battery cell), a current battery surface temperature (MCT, per battery cell), a battery pack ambient temperature (AT), and energy consumption in a temperature controller At least one of (HA), the amount of charge (SOC) of each of the plurality of batteries, the position information of each of the plurality of batteries, and the amount of time change (Δt) may be used as the learning data 410 .

Meanwhile, additionally, according to the present invention, the amount of heat loss emitted from the battery pack to the outside may be used as learning data. This will be described later.

Here, the initial battery surface temperature MCT0 means the surface temperature of each of a plurality of batteries at the point in time when machine learning data is started to be collected.

The current battery surface temperature (MCT) means the surface temperature of each of a plurality of batteries at the time of measuring the available battery output.

The battery pack ambient temperature AT refers to the temperature sensed by the sensor unit 150 described in FIG. 3 .

Energy consumption (HA) in the temperature control unit means the energy consumed in the temperature control unit from the time machine learning data is started to the time when the available output of the battery pack is measured. This may be calculated through the voltage and current values applied to the temperature controller.

The battery charge amount is a value monitored by the battery pack controller 310, and the charge amount of each of a plurality of batteries is monitored.

The amount of change in time (Δt) means a time interval from when machine learning data is started to be collected to when the available output of the battery pack is measured.

The amount of time change (Δt) may be utilized to calculate the amount of heat loss.

In one embodiment, the present invention increases the energy consumption (HA) in the temperature controller for each battery pack temperature and for each charge amount of a plurality of batteries constituting the battery pack, and the internal resistance of each of the plurality of batteries and each of the plurality of batteries Calculate the available output of and turn it into a table as shown in FIG. The table is used as machine learning 420 data for estimating available power of the battery pack.

Meanwhile, the internal resistance and available output values included in the table are matched and stored along with battery location information corresponding to the values. Through this, the present invention can estimate the available output of the battery pack in consideration of the location of the battery disposed in the battery pack.

In this case, the learning data may include an amount of heat loss emitted to the outside from each of the plurality of batteries. The amount of heat loss may be calculated through a difference between the surface temperature of the battery and the ambient temperature of the battery pack.

Specifically, the amount of heat loss for each battery may be calculated as in Equation 1 below.

[Equation 1]

In Equation 1, α and β are constants and can be determined through machine learning. t denotes the time interval from the start of machine learning data collection to the measurement of the available output of the battery pack. On the other hand, MCT means the surface temperature of the battery, and AT means the temperature around the battery pack. The amount of heat loss in each of the plurality of batteries is calculated through Equation 1, and the amount of heat loss in the entire battery pack may be calculated by summing the amount of heat loss in each of the plurality of batteries.

According to Equation 1, the present invention utilizes thermal energy emitted from the battery to machine learning for estimating the available power of the battery, so that the influence of the external environment can be reflected when estimating the available power of the battery pack.

Meanwhile, the internal resistance of each battery and the available output of each battery may be calculated through a current change due to a voltage drop of the battery.

Specifically, when the voltage is dropped for a predetermined time, the amount of change in current output from the battery is monitored. By substituting the voltage drop amount (ΔVi) and the current change amount (ΔIt) into Equation 2 below, the battery internal resistance (DICR) can be calculated.

[Equation 2]

In one embodiment, referring to FIG. 7 , the battery output voltage is dropped so that the battery voltage drop becomes the voltage drop ΔVi. At this time, the battery internal voltage DCIR is calculated using the sensed current variation ΔIt.

Meanwhile, the battery available output is calculated using a battery output current value when the battery output voltage drops to a cut-off voltage. However, since it takes a lot of time to drop the battery to the cut-off voltage, the output current value at the cut-off voltage is calculated in a proportional manner after sensing the output current amount by dropping the battery output voltage to a specific voltage.

The battery available output can be calculated as in Equation 3 below.

[Equation 3]

In Equation 3 above, SOP(T) means the available power of the battery at the battery temperature T, Vlow bound is the cut-off voltage of the battery, OCVstate(T) is the battery open circuit voltage at the battery surface temperature T, Rstate(T) means the internal resistance of the battery at the battery temperature T.

For example, referring to FIG. 8 , the output current value 5A is sensed by dropping the battery output voltage to 3.1V for 1 second. Then, the current value (Imax, 6.36) at the cut-off voltage (Vmin, 2.8V) is calculated through a proportional formula. Since the battery available output (SOPsoc) is the output when the voltage drops to the cut-off voltage, it is calculated (17.8W) through the cut-off voltage and Imax.

The available power and internal resistance calculated in the above-described manner are values dependent on the temperature of the battery. In reality, since the surface temperature and core temperature of a battery are often different, it is difficult to accurately calculate the battery's usable output using only the surface temperature of the battery that can be sensed.

In order to compensate for this, the present invention provides not only the battery surface temperature, but also other sensible information (eg, battery pack ambient temperature (AT), energy consumption (HA) in the temperature controller, charge amount (SOC) of each of a plurality of batteries ) and at least one time variation (Δt) are used for machine learning, so that the available output of the battery pack can be accurately estimated even if the core temperature of each of a plurality of batteries constituting the battery pack cannot be sensed.

The above-described learning data and the battery usable output calculation model generated through machine learning may be stored in the above-described storage unit 120 or the external storage 200 . The available output of each battery constituting the battery pack may be calculated through the battery available output calculation model generated through machine learning, and the available output of the battery pack may be calculated based on the available output.

Calculation of the available power of the battery pack using the available power of each battery may vary depending on the electrical connection method between the plurality of batteries. A method of calculating the output value of the entire plurality of batteries using the output values of the plurality of batteries uses a previously known method, so a detailed description thereof will be omitted.

Meanwhile, the present invention relates to the surface temperature of each of the plurality of batteries, the amount of charge of each of the plurality of batteries, the ambient temperature of the battery pack, the energy consumed in the temperature controller, the time when power is supplied to the temperature controller, the plurality of To estimate the maximum temperature deviation 440 inside the battery pack according to the driving time of the temperature controller using at least one of the location information of each battery of the plurality of batteries, the calculated available output value of each of the plurality of batteries, and the internal resistance value as learning data can be machine learning.

Through this, in the present invention, when the temperature deviation inside the battery pack is greater than or equal to a reference value, the temperature controller may be operated for a predetermined time to lower the temperature deviation inside the battery pack to within a reference value.

The battery pack controller 310 calculates the current available power of the battery pack by using the battery pack output calculation model generated through machine learning.

In detail, the battery pack controller 310 calculates the current available power of the battery pack based on at least one of the surface temperature of each of the plurality of batteries, the amount of charge of each of the plurality of batteries, and the ambient temperature of the battery pack.

Then, the battery pack controller 310 supplies power to the temperature controller 330 based on the available output of the battery pack.

After supplying power to the temperature controller 330 for a predetermined time, the battery pack controller 310 re-estimates the available power of the battery pack and determines whether the re-estimated available power reaches a target value (S140). At this time, the battery pack control unit 310 re-estimates the available power of the battery pack using the same model as the model for estimating the available power of the battery pack used in step S120.

Meanwhile, the battery pack controller 310 may set the predetermined time by using learning data used for machine learning. Specifically, the battery pack control unit 310 includes the amount of charge of each of the plurality of batteries sensed during step S120, the ambient temperature of the battery pack, the surface temperature of each of the plurality of batteries, and a value within a predetermined range, learning that Δt is 0 Retrieve data. That is, the battery pack controller 310 searches for learning data in a situation similar to the situation in which step S120 is performed. The retrieved data is referred to as first data.

In addition, the battery pack controller 310 searches for a target available output value of the battery pack and a value within a predetermined range from the same table as the first data. The retrieved data is referred to as second data. The second data includes a value of heat energy consumed by the temperature controller.

The battery pack controller 310 calculates an estimated thermal energy consumption by using the first and second data, and calculates a temperature control time using the estimated thermal energy consumption and an output value of the temperature controller 330. .

Here, the estimated heat energy consumption may be the energy consumption (HA) in the temperature controller included in the learning data or may be calculated based thereon.

The battery pack controller 310 re-estimates the available power of the battery pack when the calculated temperature control time has elapsed since step S120 was performed.

As described above, the time to re-estimate the available power of the battery pack after performing step S120 may vary depending on the amount of charge of each of the plurality of batteries sensed during step S120, the ambient temperature of the battery pack, and the surface temperature of each of the plurality of batteries. Through this, it is possible to minimize the amount of energy consumed for temperature control of the battery pack, and it is possible to allow the battery to reach a target available power value by estimating the minimum available power.

Meanwhile, the battery pack control unit 310 controls the surface temperature of each of the plurality of batteries, the amount of charge of each of the plurality of batteries, the ambient temperature of the battery pack, the location information of each of the plurality of batteries, the heat energy consumed in the temperature controller, and the temperature control. Based on at least one of the times, the current battery pack available power is estimated.

Finally, when the battery pack reaches within a predetermined range of the target available power, the battery pack controller 310 cuts off the power applied to the temperature controller 330 (S150).

Meanwhile, when the battery pack does not reach within a predetermined range of the target available output, the battery pack controller 310 maintains the power applied to the temperature controller 330 and, after a predetermined time has elapsed, adjusts the available output of the battery pack. re-estimate The battery pack controller 310 repeatedly performs steps S120 and S130 until the re-estimated battery reaches within a predetermined range of the target available power.

As described above, according to the present invention, since machine learning is performed on the battery pack output in various surrounding environments and in various battery pack states, the optimal battery pack output according to the current battery pack ambient temperature and the current battery pack state is calculated. You will be able to do it.

In addition, according to the present invention, since the battery pack is heated or cooled so that the battery pack can reach the optimal output calculated through machine learning, power consumption for heating or cooling is minimized and the battery pack is used in any environment. to keep it in optimum condition.

Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art to which the present invention pertains can be implemented in other specific forms without changing the technical spirit or essential features of the present invention. you will be able to understand Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive.

Claims (10)

In the temperature control method of a battery pack equipped with a temperature controller that operates when power is supplied,
setting a target value for available output of the battery pack;
estimating an available output value of the battery pack based on an ambient temperature of the battery pack and state information of each of a plurality of batteries included in the battery pack;
supplying power to the temperature controller based on the estimated available output value;
re-estimating an available output value of the battery pack after a predetermined time has elapsed since power was supplied to the temperature controller; and
and cutting off power supplied to the temperature controller when the re-estimated available output value reaches the target value,
The battery pack temperature control method of claim 1 , wherein the state information of each of the plurality of batteries includes location information of each of the plurality of batteries included in the battery pack. According to claim 1,
Estimating the available output value of the battery pack and re-estimating the available output value of the battery pack include:
Based on at least one of the location information of each of the plurality of batteries, the surface temperature of each of the plurality of batteries, the amount of charge of each of the plurality of batteries, the ambient temperature of the battery pack, the thermal energy consumed in the temperature controller, and the predetermined time A method for controlling temperature of a battery pack, characterized in that estimating the available output value of the battery. According to claim 2,
sensing a surface temperature of each of the plurality of batteries and an ambient temperature of the battery pack;
sensing a change in current flowing through each of the plurality of batteries after dropping the voltage of each of the plurality of batteries;
calculating an available output value of each of the plurality of batteries and an internal resistance value of each of the plurality of batteries by using the amount of current change and the amount of voltage drop; and
The surface temperature of each of the plurality of batteries, the amount of charge of each of the plurality of batteries, the ambient temperature of the battery pack, the energy consumed in the temperature controller, the time when power was supplied to the temperature controller, the location of each of the plurality of batteries The step of performing machine learning for estimating the available power of the battery pack using at least one of the information, the calculated available output value of each of the plurality of batteries, and the internal resistance value as learning data;
The step of estimating the available output value of the battery is performed based on the machine learning result. According to claim 3,
The learning data,
The temperature control method of the battery pack, characterized in that it further comprises a heat loss calculated based on the surface temperature of each of the plurality of batteries and the ambient temperature of the battery pack. According to claim 4,
The ambient temperature of the battery pack and the surface temperature of each of the plurality of batteries used in the step of estimating the available output value of the battery pack based on the ambient temperature of the battery pack and state information of each of the plurality of batteries included in the battery pack The battery temperature control method further comprising calculating the predetermined time by utilizing the learning data. According to claim 5,
Calculating the predetermined time,
Corresponding to the battery pack ambient temperature and the surface temperature of each of the plurality of batteries used in the step of estimating the available output value of the battery pack based on the battery pack ambient temperature and state information of each of the plurality of batteries in the learning data Retrieving first data;
retrieving second data corresponding to the target value from the learning data;
calculating an estimated heat energy consumption by utilizing the first and second data; and
and calculating the predetermined time based on the estimated amount of thermal energy consumption. In the temperature control system of the battery pack,
a battery pack including a plurality of batteries;
a temperature controller mounted on the battery pack and operated when power is supplied;
When receiving a target value for the available output of the battery pack, estimating the available output value of the battery pack based on the ambient temperature of the battery pack and state information of each of the plurality of batteries;
A battery pack controller supplying power to the temperature controller based on an estimated available output value;
The battery pack control unit,
Re-estimating the available output value of the battery pack after a predetermined time has elapsed from the time when power is supplied to the temperature controller;
When the re-estimated available output value reaches the target value, power supplied to the temperature controller is cut off;
The temperature control system of a battery pack, characterized in that the state information of each of the plurality of batteries includes position information of each of the plurality of batteries included in the battery pack. According to claim 7,
The battery pack control unit,
The battery pack temperature control system of claim 1 , wherein an available output value of the battery pack is estimated based on at least one of a surface temperature of each of the plurality of batteries, an amount of charge of each of the plurality of batteries, and a temperature around the battery pack. According to claim 8,
The battery pack control unit,
The surface temperature of each of the plurality of batteries, the amount of charge of each of the plurality of batteries, the ambient temperature of the battery pack, the energy consumed in the temperature controller, the time when power was supplied to the temperature controller, the temperature of each of the plurality of batteries A temperature control system for a battery pack, characterized in that it is performed based on a result of machine learning performed using at least one of an available output value and an internal resistance value as learning data. According to claim 9,
The learning data,
The temperature control system of the battery pack, characterized in that further comprising a heat loss calculated based on the surface temperature of each of the plurality of batteries and the ambient temperature of the battery pack.

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