KR-102965075-B1 - BATTERY CONDITIONING SYSTEM AND METHOD

KR102965075B1KR 102965075 B1KR102965075 B1KR 102965075B1KR-102965075-B1

Abstract

A battery conditioning system and method are introduced that utilize big data to collect customer charging trend data, generate charging scenarios that combine charging inducing factors based on this data, and perform preconditioning, thereby shortening charging time by performing battery preconditioning in a timely manner and blocking energy consumption caused by unnecessary conditioning.

Inventors

박현수
김태혁

Assignees

현대자동차주식회사
기아 주식회사

Dates

Publication Date: 20260513
Application Date: 20211118

Claims (15)

A big data server that collects and stores data on vehicle charging trend factors; A charging inducing factor extraction unit that selects a charging tendency factor with a high probability of performing charging as a charging inducing factor; A battery conditioning system comprising a scenario unit that generates a charging scenario with a high probability of performing a charge through a combination of charging inducing factors, and calculates a scenario reliability based on the actual charging probability for each charging scenario.
In claim 1, A battery conditioning system characterized by a charge-inducing factor extraction unit that calculates an actual charge performance probability value for each charge-tendency factor and selects a charge-tendency factor whose probability value is greater than or equal to a set value as a charge-inducing factor.
In claim 1, A battery conditioning system characterized by a scenario section that calculates conditions with a high probability of performing a charge for each charge-inducing factor as charge-inducing conditions, and constructs a scenario including the charge-inducing conditions.
In claim 1, A battery conditioning system characterized by further including a preconditioning unit that performs preconditioning when charging scenario conditions are satisfied.
In claim 1, A battery conditioning system characterized by a scenario section that calculates a scenario reliability value based on 'the number of actual charging operations after preconditioning / the number of preconditioning operations based on satisfaction of scenario conditions'.
In claim 5, A battery conditioning system characterized by a scenario section that repeats updating or creating new scenarios until the scenario reliability value approaches 1, and when the reliability value approaches 1, determines the charge-inducing factor of the corresponding scenario.
In claim 1, A battery conditioning system characterized by a scenario section that calculates a scenario coverage value corresponding to 'number of times preconditioning is performed according to scenario condition satisfaction / total number of times charging'.
In claim 7, A battery conditioning system characterized by a scenario section that repeatedly updates or creates a new charging scenario until the scenario coverage value approaches 1, and fixes the scenario to maintain the scenario when the coverage value approaches 1.
In claim 1, A battery conditioning system characterized by the fact that scenarios generated in the scenario section can be viewed by the driver through a display device, and specific scenarios can be disabled, modified, or added at the driver's will.
In claim 1, A battery conditioning system characterized by a big data server that can share scenarios generated from each vehicle with other vehicles, and enables other vehicles to perform preconditioning by utilizing the shared scenarios.
A step in which a big data server collects and stores vehicle charging trend factor data; A step in which a charging inducing factor extraction unit selects a charging tendency factor with a high probability of performing charging as a charging inducing factor; and A battery conditioning method comprising the step of: generating a charging scenario with a high probability of performing a charge through a combination of charging inducing factors in a scenario section, and determining the scenario by calculating a scenario reliability based on the actual charging probability for each charging scenario.
In claim 11, A battery conditioning method characterized by a step of determining the scenario, wherein conditions with a high probability of performing a charge are calculated as charge-inducing conditions for each charge-inducing factor, and reliability is calculated including the charge-inducing conditions to determine the scenario.
In claim 11, A battery conditioning method characterized by the step of determining the scenario, which calculates a scenario reliability value based on 'number of actual charge operations after preconditioning / number of preconditioning operations based on scenario condition satisfaction', repeats updating or creating a new scenario until the scenario reliability value approaches 1, and determines the charge-inducing factor of the corresponding scenario when the reliability value approaches 1.
In claim 11, A battery conditioning method characterized by the step of determining the scenario, which calculates a scenario coverage value corresponding to 'number of times preconditioning is performed based on scenario condition satisfaction / total number of times charging', repeats updating or creating a new charging scenario until the scenario coverage value approaches 1, and maintains the corresponding scenario when the coverage value approaches 1.
In claim 11, A battery conditioning method further comprising a charging scenario sharing step in which a big data server can share scenarios generated from each vehicle with other vehicles, and other vehicles can utilize the shared scenarios to enable preconditioning.

Description

Battery Conditioning System and Method The present invention relates to a battery conditioning system and method, and more specifically, to a system and method that utilizes big data to generate a scenario combining factors that induce charging of a vehicle and performs battery preconditioning accordingly. A battery is a device capable of freely converting chemical energy and electrical energy into each other using electrochemical reactions. Since electric vehicles utilize batteries directly or indirectly, the efficient use of battery power is directly related to driving time and performance. Furthermore, pre-conditioning technology, which preheats the battery, is an important technology that significantly impacts battery life by increasing charging efficiency. Meanwhile, conventional battery preconditioning technology generally pre-sets conditions based on the customer's charging tendencies and uniformly applies the same preconditioning conditions to the vehicle. For example, if a condition is applied to perform preconditioning only when a charging station is set as a destination on the navigation system, a driver heading to a charging station without using the navigation system will be unable to utilize the preconditioning function. As another example, unnecessary preconditioning may be performed even if the user has no intention of charging, simply because a charging station is located near the destination, as the condition may be satisfied. In other words, conventional technology had a problem with low accuracy in determining the customer's intention to charge or whether charging had actually taken place. The matters described above as background technology are intended only to enhance understanding of the background of the present invention and should not be construed as an acknowledgment that they constitute prior art already known to those skilled in the art. FIG. 1 is a conceptual diagram of a battery conditioning system according to the present invention. FIG. 2 is a flowchart illustrating the process of extracting a charging inducing factor according to one embodiment of the present invention. FIG. 3 is an illustrative diagram showing an example of the process of searching for a charging inducing factor of the present invention. FIG. 4 is a flowchart illustrating the process of updating a charging scenario according to an embodiment of the present invention. FIG. 5 is an illustrative diagram showing an example of calculating scenario reliability for each scenario according to an embodiment of the present invention. FIG. 1 is a conceptual diagram of a battery conditioning system according to the present invention. FIG. 2 is a flowchart illustrating a process of extracting a charge-inducing factor according to an embodiment of the present invention. FIG. 3 is an illustrative diagram illustrating an example of a process of searching for a charge-inducing factor according to the present invention, and FIG. 4 is a flowchart illustrating a process of updating a charging scenario according to an embodiment of the present invention. FIG. 5 is an illustrative diagram illustrating an example of calculating scenario reliability for each scenario according to an embodiment of the present invention. FIG. 1 is a conceptual diagram of a battery conditioning system according to the present invention. The battery conditioning system may be largely composed of a big data server (100) capable of collecting and storing data on the charging trend factors of a vehicle and a processor (200). Depending on the role, the processor (200) may be configured to include a charging inducing factor extraction unit (210), a scenario unit (230), and a pre-conditioning unit (250). A processor (200) according to an exemplary embodiment of the present invention may be implemented through a non-volatile memory (not shown) configured to store data relating to an algorithm configured to control the operation of various components of a vehicle or software instructions implementing said algorithm, and a processor (200) (not shown) configured to perform the operation described below using the data stored in said memory. Here, the memory and the processor (200) may be implemented as separate chips. Alternatively, the memory and the processor (200) may be implemented as a single chip integrated with each other, and the processor (200) may take the form of one or more processors (200). The big data server (100) can receive various data generated during the operation of the vehicle from the vehicle, and process and analyze the received data and store it. In particular, the big data server (100) can classify and store charging-related factors as charging trend factors based on the data received from the vehicle or secondary data generated therefrom. The charging inducing factor extraction unit (210) selects a charging tendency factor with a high probability of performing charging as a charging inducing factor. For example, data such as SOC (Stat