Patent ID: 12252036

DETAILED DESCRIPTION

Hereinafter, a system and method for heating a battery in a vehicle using big data according to exemplary embodiments of the present disclosure will be described in detail with reference to the attached drawings.

FIG.1is a diagram showing the configuration of a system for heating a battery in a vehicle using big data in some forms of the present disclosure.

Referring toFIG.1, the system for heating a battery in a vehicle using big data according to an embodiment of the present disclosure may include a big-data server100configured to receive driving information of a vehicle from a vehicle10and to derive an estimated driving start time of the vehicle10and a required output (or a required power) that is required at an initial driving stage of the vehicle10based on the received driving information, and a controller12that is installed in the vehicle10and is configured to receive the estimated driving start time and the required output provided from the big-data server100and to calculate a heating time of the battery, required to ensure the required output, based on a temperature and a state of charge (SoC) of a battery11installed in the vehicle10by being woken up prior to the estimated driving start time.

The big-data server100may receive driving information of a vehicle related to heating of the battery11in a vehicle from the vehicle10and may generate and store data obtained by processing and analyzing the received driving information of the vehicle.

In particular, according to an embodiment of the present disclosure, the big-data server100may receive driving information such as the driving start time of the vehicle10, driving power for driving a motor while driving the vehicle10, and a state of charge (SoC) of the battery11of the vehicle10from the controller12of the vehicle.

The big-data server100may generate various patterns related to driving of the vehicle10by accumulating the driving information of the vehicle received from the controller12of the vehicle10, and may then transmit the patterns to the controller12of the vehicle10if necessary.

For example, the big-data server100may accumulate and receive the driving start time of the vehicle and may derive an estimated driving start time for each day of the week. When the vehicle10is used by a driver for commuting, the driving start time of the vehicle may have a predetermined pattern for each day of the week, and the big-data server100may derive the estimated driving start time of the vehicle10for each day of the week based on the pattern of the driving start time.

In another example, the big-data server100may derive a driving power pattern and an SoC pattern of the vehicle10by receiving and accumulating information on the driving power and the SoC of the vehicle10. The big-data server100may determine the required output of the vehicle based on the driving power pattern and the SoC pattern.

Here, the required output needs to be received from a battery in order to drive the vehicle, may be determined according to the driving pattern of the vehicle, and may be a reference for calculating a temperature to which it is required to heat the battery by the controller12of the vehicle10.

In more detail, when the driver of the vehicle10has a driving tendency such that the driver enjoys rapid launch and acceleration while driving the vehicle, the big-data server100may infer that the corresponding vehicle has a driving power pattern using high driving power. In addition, when the driver has a driving tendency such that the battery is rapidly charged and discharged greatly while driving the vehicle, the big-data server100may infer that the vehicle has a charging pattern in which the SoC of the battery is greatly changed while driving the vehicle.

The big-data server100may derive the required output that is required at an initial driving stage of the vehicle based on such a driving power pattern and an SoC variation pattern of the battery. For example, the big-data server100may determine high required output for a vehicle having a pattern using high driving power compared with a vehicle in the opposite case. This is because a vehicle that uses high driving power from an initial driving stage needs to heat a battery sufficiently and to smoothly provide power.

In addition, the big-data server100may determine low required output for a vehicle having a pattern in which the SoC of a battery varies greatly while driving the vehicle compared with a vehicle in the opposite case. This is because the battery of the vehicle having the pattern in which the SoC of the battery varies greatly while driving the vehicle is rapidly heated by the heat generated from the battery itself due to charging and discharging of the battery while driving the vehicle, even if the temperature of the battery is low at an initial driving stage.

The required output determined by the big-data server100may refer to the power that needs to be output by the battery when the vehicle starts driving in consideration of the driving power of the vehicle and the SoC of the battery11in the vehicle. The required output may correspond to the minimum power for stably driving the vehicle based on the driving pattern of the vehicle, but not the maximum power to be output from the battery. The big-data server100may determine the minimum required output required to drive the vehicle at an initial driving stage in consideration of heat generated by the battery itself while driving the vehicle based on the driving power pattern of the vehicle10and the SoC variation pattern of the battery in the vehicle.

As such, according to an embodiment of the present disclosure, the battery may be heated only up to the minimum at an initial driving stage of the vehicle by determining the minimum required output while driving the vehicle based on the driving pattern of the vehicle10when the required output, as a reference for heating the battery is determined. Thus, according to an embodiment of the present disclosure, power consumption in a heater for heating the battery may be reduced by preventing the battery11from being excessively heated. In particular, power consumed to heat the battery may be further saved by calculating the required output in consideration of heating of the battery using heat generated by the battery itself through charging and discharging of the battery while driving the vehicle using the SoC variation pattern of the battery while driving the vehicle.

The big-data server100and the controller12of the vehicle10may be embodied as a communication device that supports various wireless communication protocols that are known to the art to which the art pertains. The detailed communication method is not related to the main features of the present disclosure, and thus, a detailed description thereof will be omitted.

As shown inFIG.1, the big-data server100may be embodied using a distributed cloud method of a hierarchical structure having cloud servers110,120, and130for respective layers.

For example, the first-layer cloud server110, belonging to the lowermost layer of a plurality of hierarchical structures, may communicate with the vehicle10, may log data generated by the vehicle10in real time, and may provide the logged data to the vehicle10if necessary, or may provide the data to the cloud servers120and130belonging to a high-ranking layer of the lowermost layer110.

The first-layer cloud server110may log raw data generated by a vehicle in real time via communication with the vehicle. The first-layer cloud server110may log and store vehicle data at as low a sampling rate as possible without data loss. The first-layer cloud server110may set a limit on the amount of data to be logged and stored per vehicle, that is, the communication target. Needless to say, if resources allow, all data logged from a vehicle may be stored, but the first-layer cloud server110communicates with and controls the vehicle mainly in real time, and thus the amount of data to be stored per vehicle may be limited in order to use resources efficiently.

The raw data logged by the first-layer cloud server110may be data that is generated and transmitted by various controllers of a vehicle. In particular, in the system for controlling heating of a battery according to the present disclosure, the first-layer cloud server110may receive information such as the time for starting driving the vehicle10, driving power while driving the vehicle10, and the SoC of the battery while driving the vehicle10, from the controller12of the vehicle10.

The second-layer cloud server120may receive information collected by the first-layer cloud server110and may accumulate and store the data, and the third-layer cloud server130may derive a predetermined pattern by analyzing the accumulated data using a statistical method and may determine data transmitted to the controller12of the vehicle10in order to heat the battery based on the derived pattern. That is, the third-layer cloud server130may determine an estimated driving start time of the vehicle for each day of the week and the required output required while driving the vehicle, and may transmit the estimated driving start time and the required output to the controller12of the vehicle10.

FIG.1is a diagram for explaining an example of an embodiment in which a total of three layers is embodied, in which case the number of layers may be appropriately adjusted as necessary and the function performed by each layer may also be appropriately changed.

The controller12of the vehicle10may receive the estimated driving start time and the required output from the big-data server100, and may control heating of the battery by driving a heater13added to the battery11based on the received estimated driving start time and required output.

The scheme for controlling heating of the battery performed by the controller12will be more clearly understood through a method of heating a battery in a vehicle using big data according to an embodiment of the present disclosure, which will be described below.

FIG.2is a flowchart showing a method of heating a battery in a vehicle using big data in some forms of the present disclosure.

The controller12of the vehicle10may transmit information required by the big-data server100in real time at a predetermined period in order to control heating of the battery while driving the vehicle (S11). As described above, information transmitted to the big-data server100by the controller12with regard to control of heating the battery may include the driving start time of the vehicle, driving power while driving the vehicle, and the SoC of the battery while driving the vehicle.

The big-data server100may accumulate the information provided from the vehicle, may generate a driving pattern of the corresponding vehicle (S21), may determine the estimated driving start time and the required output at an initial driving stage while driving the vehicle based on the generated driving pattern, and may transmit the determined estimated driving start time and required output to the corresponding vehicle (S22).

The controller12of the vehicle10may be woken up earlier than the estimated driving start time of the vehicle, provided from the big-data server100, by as much as a time A hours (S12) and may collect information required to control heating of the battery (S13).

Here, a value A may be determined in consideration of the case in which a battery needs to be heated at the lowest temperature for the maximum heating time. That is, the value A may be a value corresponding to a preset maximum heating time in order to maximally heat the battery.

Information required to control heating of the battery may include a temperature of the battery, an outdoor temperature of the vehicle, and the SoC of the battery. The temperature of the battery and the outdoor temperature may be detected by a temperature sensor installed in the battery and a temperature sensor for detecting an outdoor temperature of the vehicle, and the SoC of the battery11may be executed through an algorithm for determining the SoC of the battery, which is pre-stored in the controller12. The sensor or the algorithm is already known to the art to which the present disclosure pertains, and thus, a detailed description thereof will be omitted.

Then, the controller12may calculate the temperature to which it is required to heat the battery11for ensuring the required output provided from the big-data server100based on a battery output map for each preset temperature-battery SoC (S14).

FIG.3is a diagram showing an example of a battery output map for each temperature-SoC applied to calculate the temperature to which it is required to heat the battery in a system and method for heating a battery in a vehicle using big data in some forms of the present disclosure.

In operation S14, the controller12may calculate the temperature to which it is required to heat the battery using the battery output map for each temperature-SoC shown inFIG.3.

An example in which the required output provided from the big-data server100is 120 kW, the outdoor temperature and the temperature of the battery are −20° C., and the battery SoC is 20% will be described below. Referring to the map shown inFIG.3, when the SoC of the battery is 20% and the battery temperature is −20° C., the output of the battery may be 78.9. Referring to the map shown inFIG.3, when the SoC of the battery is 20%, the battery needs to be heated at least to a temperature between 10 and 15° C. in order to achieve output of 120 kW by a battery. An accurate heating target temperature may be calculated through interpolation between temperatures, or may also be set to a temperature equal to or greater than the required output.

The controller12may calculate a required heating temperature, to which it is required to heat the battery, as a temperature of about 15° C. by subtracting the current temperature of the battery from the heating target temperature. In this example, when the battery is heated by about 5° C., the battery at the SoC of 20% may be capable of outputting an output equal to or greater than 120 kW, and thus the required heating temperature may be determined to be about 5° C.

Then, the controller12may derive the heating time taken to heat the battery11by the required heating temperature (S15). The time taken to heat the battery11may be changed depending on a heating value of the heater13and the outdoor temperature. According to an embodiment of the present disclosure, the heating time may be determined using a battery temperature increase data map, which is dependent upon the outdoor temperature, and the driving time of the heater13in consideration of the heating value of the heater13. That is, the controller12may determine the driving time of the heater13, corresponding to the required heating temperature and the current outdoor temperature in the battery temperature increase data map, as the time taken for heating.

Then, when a heating time Heat time is equal to or greater than a time A (which is the time remaining until the estimated driving start time of the vehicle from the time at which the controller12is woken up) (S16), the controller12may immediately drive the heater13to control heating of the battery (S17), and when the heating time Heat time is shorter than the time A, power is turned off (S19), and then the controller12may be earlier woken up by as much as a time B corresponding to the heating time from the estimated driving start time (S19) and may control heating of the battery (S20).

As described above, the system and method for heating a battery in a vehicle using big data according to various embodiments of the present disclosure may calculate minimum/optimum power required by the battery at an initial driving stage of the vehicle and may prevent the battery from being heated more than necessary in consideration of the fact that the required output is different for each driver depending on their driving pattern/tendency and the fact that the battery is heated through charging and discharge while driving the vehicle rather than being heated using the heater of the battery. Accordingly, the energy efficiency of the vehicle may be improved by minimizing energy consumption of the battery of the vehicle.

The system and method for heating a battery in a vehicle using big data may calculate minimum/optimum power required by the battery at an initial driving stage of the vehicle and may prevent the battery from being heated more than necessary in consideration of the fact that the required output is different for each driver depending on their driving pattern/tendency and the fact that the battery is heated through charging and discharge while driving the vehicle rather than being heated using the heater of the battery. Accordingly, the driver input for heating the battery before the vehicle starts traveling may not be required, and the energy efficiency of the vehicle may be improved by minimizing energy consumption of the battery of the vehicle when heating the battery.

It will be appreciated by those skilled in the art that the effects achievable through the present disclosure are not limited to those that have been particularly described hereinabove and that other unmentioned effects of the present disclosure will be more clearly understood from the above detailed description.

Although the present disclosure has been shown and described with respect to specific embodiments, it will be apparent to those having ordinary skill in the art that the present disclosure may be variously modified and altered without departing from the spirit and scope of the present disclosure as defined by the following claims.