Battery life prediction apparatus and method

Abstract

Provided is a battery life prediction device including a storage unit that stores a plurality of pieces of power pattern data used in an actual site, a pattern generation unit that generates a test pattern for predicting a life of a battery based on the power pattern data, and a life prediction unit that predicts a remaining life of the battery based on the test pattern.

Description

TECHNICAL FIELD

[0001] Cross Reference to Related Applications

[0002] This application claims priority to and the benefit of Korean Patent Application No. 10-2020-0101865, filed on August 13, 2020, in the Korean Intellectual Property Office, the entire contents of which are incorporated herein by reference. TECHNICAL FIELD

[0004] The present application relates to an apparatus and method for predicting the life of a battery by generating a pattern for testing the charge and discharge of the battery. BACKGROUND

[0005] Recently, research and development of secondary batteries have been actively conducted. In this context, the secondary battery, which is a chargeable / dischargable battery, can include all conventional nickel (Ni) / cadmium (Cd) batteries, Ni / metal hydride (MH) batteries, etc., and recent lithium ion batteries. Among the secondary batteries, the lithium ion battery has a much higher energy density than that of the conventional Ni / Cd battery, Ni / MH battery, etc. In addition, the lithium ion battery can be manufactured to be small and light, such that the lithium ion battery has been used as a power source of a mobile device. In addition, as the use range of the lithium ion battery is extended to a power source of an electric vehicle, the lithium ion battery is attracting attention as a next-generation energy storage medium.

[0006] In addition, the secondary battery is generally used as a battery pack including a battery module in which a plurality of battery cells are connected in series and / or in parallel to each other. The battery pack can be managed and controlled in terms of its state and operation by a battery management system.

[0007] Meanwhile, in the prediction of the state of health (SOH) of a battery through a charge and discharge cycle, an energy storage system (ESS) performs a test in the form of repeating the same cycle, such that the accuracy is deteriorated when applied to an actual site using inconsistent cycles in a mixed manner. For example, such a conventional scheme performs a SOH test while changing a CP rate in a battery voltage range of 3V to 4.2V, and predicts the SOH of an actual site by using data derived from the test.

[0008] However, in an actual site, charging and discharging are performed as the CP rate is changed in real time, such that the predicted SOH calculated by using the SOH data based on the conventional test in which the same cycle is repeated in a mixed manner can be completely different from the actual SOH based on the pattern used in the actual site. SUMMARY

[0009] [Technical Problem]

[0010] The present invention has been designed to solve the above problems, and is intended to provide a battery life prediction device and method in which, by generating a test pattern having a parameter value desired by a user based on power pattern data used in an actual site, charging / discharging tests can be performed through a power pattern similar to that of the actual site, thereby improving the accuracy of battery remaining rate prediction of the actual site.

[0011] [Technical Solution]

[0012] According to an embodiment of the present invention, a battery life prediction device includes a storage unit storing a plurality of pieces of power pattern data used in an actual site, a pattern generation unit generating a test pattern for predicting a life of a battery based on the power pattern data, and a life prediction unit predicting a remaining life of the battery based on the test pattern.

[0013] According to another embodiment of the present invention, a battery life prediction method includes extracting at least one piece of power pattern data from a plurality of pieces of power pattern data used in an actual site, generating a test pattern for predicting a life of a battery based on the extracted power pattern data, and predicting a remaining life of the battery based on the test pattern.

[0014] [Advantageous Effects]

[0015] With the battery life prediction device and method according to the present invention, by generating a test pattern having a parameter value desired by a user based on power pattern data used in an actual site, charging / discharging tests can be performed through a power pattern similar to that of the actual site, thereby improving the accuracy of battery remaining rate prediction of the actual site. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is a block diagram of a general battery pack.

[0017] Figure 2 is a block diagram illustrating a structure of a battery life prediction device according to an embodiment of the present invention.

[0018] Figure 3 illustrates actual PNNL pattern data used in a battery life prediction device according to an embodiment of the present invention.

[0019] Figure 4 illustrates parameter values of a test pattern actually generated in a battery life prediction device according to an embodiment of the present invention and target parameter values.

[0020] Figure 5 illustrates parameter values of a test pattern generated in a battery life prediction device according to an embodiment of the present invention and a power histogram.

[0021] Figure 6is a flowchart illustrating a battery life prediction method according to an embodiment of the present application.

[0022] Figure 7 is a block diagram illustrating a hardware structure of a battery life prediction device according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] Hereinafter, various embodiments of the present application will be described in detail with reference to the accompanying drawings. In this document, the same reference numbers are used for the same components and redundant descriptions can be omitted.

[0024] For various embodiments of the present application disclosed in this document, specific structural or functional descriptions are exemplified only for the purpose of describing embodiments of the present application, and various embodiments of the present application can be implemented in various forms, and should not be interpreted as being limited to the embodiments described in this document.

[0025] As used in various embodiments, the terms "1st", "2nd", "first", "second", and the like can modify various components regardless of their importance and do not limit the components. For example, a first component can be named a second component without departing from the scope of the present disclosure, and similarly, a second component can be named a first component.

[0026] The terms used in this document are used only to describe particular exemplary embodiments of the present disclosure and can not have the intent to limit the scope of other exemplary embodiments of the present disclosure. It should be understood that, unless the context clearly dictates otherwise, the singular form includes the plural reference.

[0027] All terms used herein including technical or scientific terms have the same meaning as those commonly understood by a person of ordinary skill in the relevant art. It should further be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and should not be interpreted in an idealized or overly formal sense unless expressly so defined herein. In some cases, terms defined herein can be interpreted to exclude embodiments of the present disclosure.

[0028] Figure 1 is a block diagram of a general battery pack

[0029] Referring to Figure 1 , a battery control system according to an embodiment of the present application including a battery rack 1 and a superior controller 2 included in a superior system is schematically illustrated.

[0030] As Figure 1As shown, the battery rack 1 may include: a battery module 10, which includes one or more battery cells and is chargeable / dischargeable; a switch unit 14, which is connected in series to the positive (+) terminal side or the negative (-) terminal side of the battery module 10 to control the flow of charge / discharge current of the battery module 10; and a battery management system (e.g., MBMS) 20, which is used to control and manage the battery rack 1 by monitoring the voltage, current, temperature, etc. to prevent overcharging and overdischarging. The battery rack 1 may include a plurality of battery modules 10, sensors 12, switch units 14, and battery management system 20.

[0031] Herein, as the switching unit 14 as a semiconductor switching element for controlling the flow of current for charging or discharging the plurality of battery modules 10 , for example, at least one MOSFET, relay, magnetic contactor, etc. may be used according to the specifications of the battery rack 1 .

[0032] The battery management system 20 can measure or calculate the voltage and current of the gate, source, and drain of the semiconductor switching elements to monitor the voltage, current, and temperature of the battery rack 1. The battery management system 20 can measure the current, voltage, and temperature of the battery rack 1 using sensors 12 disposed adjacent to the semiconductor switching elements.

[0033] The battery management system 20, which serves as an interface for receiving the measurement values ​​of the various parameter values ​​described above, may include a plurality of terminals and circuits connected thereto for processing the input values, etc. The battery management system 20 may control the ON / OFF switching of the switching unit 14, such as a MOSFET, and may be connected to the battery modules 10 to monitor the status of each battery module 10.

[0034] Meanwhile, the battery management system 20 according to the present invention can generate a test pattern for predicting the remaining rate (life) of the battery based on the power pattern data of the actual site, as will be described below. Based on the test pattern, the remaining life of the battery can be predicted.

[0035] The superior controller 2 can transmit control signals regarding the battery module 10 to the battery management system 20. Therefore, the battery management system 20 can also be controlled in terms of its operation based on the signals applied from the superior controller. Meanwhile, the battery cells according to the present invention can be included in the battery module 10 used in an energy storage system (ESS). In this case, the superior controller 2 can be an ESS controller. However, the battery rack 1 is not limited to this purpose.

[0036] This configuration of the battery rack 1 and the battery management system 20 is a well-known configuration, and thus will not be described in detail.

[0037] Figure 2is a block diagram illustrating a structure of a battery life prediction device according to an embodiment of the present application.

[0038] Referring to Figure 2 The battery life prediction device 200 according to an embodiment of the present application can include a storage unit 210, a pattern generation unit 220, and a life prediction unit 230.

[0039] The storage unit 210 can store a plurality of power pattern data used in an actual site. In this case, the power pattern data stored in the storage unit 210 can include a power pattern actually used in a power system. For example, a power pattern actually used in a power system, i.e., a frequency regulation (FR) power pattern, can include a Pacific Northwest National Laboratory (PNNL) pattern.

[0040] The pattern generation unit 220 can generate a test pattern for predicting a life of a battery based on the power pattern data stored in the storage unit 210. In this case, the pattern generation unit 220 can generate a test pattern having target parameter values regarding charging and discharging of the battery. In this case, the target parameter values can be values input by a user, for example, the target parameter values can include a maximum / minimum state of energy (SOE), a first / last SOE, and an average CP (charge / discharge intensity) in an entire period (for example, 24 hours) of the power pattern data. However, the target parameter values are not limited thereto, and various parameter values for defining a charge / discharge pattern of the battery can also be used.

[0041] The pattern generation unit 220 can repeatedly generate the test pattern until a difference between a parameter value of the generated test pattern and the target parameter value becomes less than a preset reference value. The reference value can be randomly set by a user.

[0042] The pattern generation unit 220 can generate the test pattern by changing a scale of the power pattern data. More specifically, the pattern generation unit 220 can generate the test pattern by dividing the power pattern data into a plurality of sections and adjusting power values for each section. For example, the pattern generation unit 220 can divide the power pattern data into sections showing similar patterns. The pattern generation unit 220 can also generate the test pattern by multiplying a scale value by each section to allow the test pattern to have the target parameter values.

[0043] The life prediction unit 230 can predict a remaining life of the battery based on the test pattern. For example, when the test pattern generated in the pattern generation unit 220 and numerical values regarding the pattern are input to the life prediction unit 230, the life prediction unit 230 can perform a charge / discharge test of the battery using a preset algorithm, and predict a remaining life of the battery.

[0044] Meanwhile, althoughFigure 2 The battery life prediction device 200 according to the embodiment of the present application can include an input unit, not shown. Accordingly, a user can directly set a desired target parameter value, etc. through the input unit. For example, the input unit can include a keyboard, a touch pad, a mouse, etc.

[0045] Further, referring to Figure 2 It is described that a plurality of power pattern data used in an actual site is stored in the storage unit 210, but the present application is not limited thereto, and such power pattern data can be stored in a database of an external server. In this case, the battery life prediction device 200 according to the embodiment of the present application can include a communication unit (not shown) to receive the power pattern data from the external server therethrough, and to generate a test pattern based on the received power pattern data.

[0046] In this way, with the battery life prediction device according to the present application, by generating a test pattern having a parameter value desired by a user based on power pattern data used in an actual site, a charging / discharging test can be performed through a power pattern similar to the actual site, thereby improving the accuracy of the prediction of the remaining rate of the actual site.

[0047] Figure 3 Actual PNNL pattern data used in the battery life prediction device according to the embodiment of the present application is illustrated. Referring to Figure 3 , the x-axis indicates time (hour / minute / second), and the y-axis indicates SOE (%).

[0048] Figure 3 The PNNL pattern of Figure 3 is a power pattern actually used in the European power market. Herein, the PNNL pattern can refer to a power pattern indicating that charging and discharging are performed for 24 hours in a power source device such as a battery, etc. As

[0049] Meanwhile, Figure 3 The PNNL pattern of Figure 3 is an example, and the PNNL pattern used in the battery life prediction device according to the embodiment of the present application is not limited to

[0050] Figure 4 Parameter values of a test pattern actually generated in the battery life prediction device according to the embodiment of the present application and a target parameter value are illustrated.

[0051] Referring to Figure 4 , it is shown that the battery life prediction device according to the embodiment of the present application generates a test pattern by using a sampling time of 4 seconds. AsFigure 4 As shown, the target parameter values of the maximum / minimum SOE, the first / last SOE, and the average CP can be calculated.

[0052] In Figure 4 , the normalized values and the scaling change values are calculated values for generating test patterns in the battery life prediction device according to embodiments of the present application. Referring to Figure 4 , it can be seen that the target parameter values are the maximum / minimum SOE of 80% and 20% respectively, the first / last SOE of 50% respectively, and the average CP of 0.5000 (1 / hr). Such target parameter values can be directly input by a user.

[0053] Meanwhile, the parameter values of the test patterns generated by the battery life prediction device according to embodiments of the present application as target parameter values can be the maximum / minimum SOE of 82.08% and 20.44% respectively, the first / last SOE of 50% and 50.05% respectively, and the average CP of 0.5091 (1 / hr), as shown in Figure 4 .

[0054] In this case, when each parameter value of the generated test patterns falls within a preset reference range from each target parameter value, the test patterns can be used to calculate the remaining life of the battery. However, when one or more of the parameter values of the generated test patterns fall outside the preset reference range from the target parameter values, the test patterns can be generated until they fall within the reference range.

[0055] Figure 5 FIGS. 1 to 3 illustrate test patterns generated in the battery life prediction device according to embodiments of the present application and power histograms.

[0056] In Figure 5 , the above graphs are graphs showing the generated test patterns, in which the x-axis indicates time (hour / minute / second) and the y-axis indicates SOE (%). Figure 5 The below graphs of FIGS. 4 to 6 are power histograms of the generated test patterns, in which the x-axis indicates an SOE range and the y-axis indicates the number of data for each SOE amplitude. In this case, the PNNL pattern data of Figure 3 can be used to generate each test pattern of Figure 5 .

[0057] As shown in Figure 5 , it can be seen that various test patterns can be generated even when the same PNNL pattern data of Figure 3 is used. That is, in Figure 3The scaling of the PNNL pattern data depends on whether the similar pattern is divided into several segments, after which scaling (e.g., multiplying a certain value repeatedly by the power value of each segment) can be performed to have a target parameter value for each segment, thereby differently representing the form of the test pattern.

[0058] Figure 6 is a flowchart illustrating a battery life prediction method according to an embodiment of the present application.

[0059] Referring to Figure 6 In operation S610, the battery life prediction method according to an embodiment of the present application extracts at least one piece of power pattern data from among a plurality of pieces of power pattern data stored in advance. In this case, the plurality of pieces of power pattern data can be stored in the above-described storage unit or an external server. In addition, the power pattern data can include power patterns actually used in a power system. For example, the power patterns actually used in the power system as FR power patterns can include PNNL patterns.

[0060] In operation S620, a test pattern for predicting the life of the battery can be generated based on the extracted power pattern data. In this case, in operation S620, the test pattern can be generated to have target parameter values regarding charging and discharging of the battery. In this case, the target parameter values can be values input by a user, for example, the target parameter values can include maximum / minimum SOE, first / last SOE, and average CP.

[0061] In operation 620, the test pattern can be generated by changing the scaling of the power pattern data. More specifically, the test pattern can be generated by dividing the power pattern data into a plurality of segments and adjusting the power value for each segment. For example, the test pattern can be generated by multiplying the scaling value by each segment to allow the test pattern to have the target parameter values.

[0062] Next, in operation S630, it is determined whether the difference between the parameter value of the generated test pattern and the target parameter value is less than a reference value. When the difference between the parameter value of the generated test pattern and the target parameter value is greater than or equal to the reference value (No), the battery life prediction method can return to operation S620. That is, the test pattern can be repeatedly generated until the difference between the parameter value of the generated test pattern and the target parameter value becomes less than the preset reference value.

[0063] On the other hand, when the difference between the parameter value of the generated test pattern and the target parameter value is less than the reference value (Yes), in operation S640, the remaining life of the battery can be predicted based on the generated test pattern. For example, when the test pattern generated in operation S620 and the numerical values regarding the pattern are input, a charging / discharging test of the battery can be performed using a preset algorithm, and the remaining life of the battery can be predicted.

[0064] Thus, with the battery life prediction method according to the present application, by generating a test pattern having a parameter value desired by a user based on power pattern data used in an actual site, charging / discharging tests can be performed with a power pattern similar to that of the actual site, thereby improving the accuracy of the prediction of the battery remaining rate of the actual site.

[0065] Figure 7 is a block diagram illustrating a hardware structure of a battery life prediction device according to an embodiment of the present application.

[0066] Referring to Figure 7 , the battery life prediction device 700 according to an embodiment of the present application can include a micro controller unit (MCU) 710, a memory 720, an input / output interface (I / F) 730, and a communication I / F 740.

[0067] The MCU 710 can be a processor that executes various programs (e.g., a test pattern generation program, a battery life prediction program, etc.) stored in the memory 720, processes various data for generating a test pattern of a battery and predicting a life of the battery through the programs, and performs the above-described functions of the Figure 2 .

[0068] The memory 720 can store various programs regarding test pattern generation, life prediction of a battery, etc. The memory 720 can store various data used in an actual site, such as power pattern data (e.g., PNNL data), test pattern data, etc.

[0069] A plurality of memories 720 can be provided as needed. The memory 720 can be a volatile or non-volatile memory. For the memory 720 as a volatile memory, a random access memory (RAM), a dynamic RAM (DRAM), a static RAM (SRAM), etc. can be used. For the memory 720 as a non-volatile memory, a read only memory (ROM), a programmable ROM (PROM), an electrically alterable ROM (EAROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), a flash memory, etc. can be used. The above-described examples of the memory 720 are merely examples, and are not limited thereto.

[0070] The input / output I / F 730 can provide an interface for transmitting and receiving data by connecting an input device (not shown), such as a keyboard, a mouse, a touch panel, etc., and an output device, such as a display (not shown), etc., to the MCU 710.

[0071] The communication I / F 740, which is a component capable of transmitting various data to a server and receiving various data from the server, can be various types of devices capable of supporting wired or wireless communication. For example, a program or various data, etc. for generating power mode data or test mode and predicting battery life can be transmitted to or received from an externally provided server through the communication I / F 740.

[0072] As such, the computer program according to the embodiment of the present application can be recorded in the memory 720 and processed by the MCU 710, thereby being implemented as a module performing the functions of the blocks shown. Figure 2

[0073] Even if all components constituting the embodiment of the present application are combined into one or operated in combination as described above, the present application is not necessarily limited to the embodiment. That is, within the scope of the object of the present application, all components can be operated by being selectively combined into one or more.

[0074] Further, the above-described terms such as "include", "consist of", or "have" can mean that the corresponding components can be inherent, unless otherwise specified, and thus should be interpreted as further including other components rather than excluding other components. Unless otherwise defined, all terms including technical or scientific terms have the same meaning as those commonly understood by one of ordinary skill in the art. Terms commonly used, such as terms defined in a dictionary, should be interpreted to have the same meaning as the context of the relevant technology, and should not be interpreted to have an ideal or excessively formal meaning, unless they are clearly defined in the present application.

[0075] The above description is merely an illustration of the technical idea of the present application, and various modifications and changes are possible without departing from the basic characteristics of the present application of those of ordinary skill in the art to which the present application pertains. Therefore, the disclosed embodiments of the present application are intended to describe rather than limit the technical spirit of the present application, and the scope of the technical spirit of the present application is not limited by these embodiments. The scope of protection of the present application should be interpreted by the following claims, and all technical spirits within the same scope should be understood to be included within the scope of the present application.​

Claims

1. A battery life prediction apparatus comprising: a storage unit configured to store a plurality of power pattern data used in an actual site; a pattern generation unit configured to generate a test pattern for predicting a life of a battery based on the power pattern data; and a life prediction unit configured to predict a remaining life of the battery based on the test pattern, wherein the pattern generation unit generates the test pattern having a target parameter value regarding charging and discharging of the battery. The target parameter value is a value input by a user.

2. The battery life prediction apparatus according to claim 1, wherein The target parameter value includes at least one of a maximum and minimum state of energy (SOE), a first and last SOE, or an average CP.

3. The battery life prediction apparatus according to claim 1, wherein The pattern generation unit repeatedly generates the test pattern until a difference between a parameter value of the generated test pattern and the target parameter value becomes less than a preset reference value.

4. The battery life prediction apparatus according to claim 1, wherein The pattern generation unit generates the test pattern by changing a scaling of the power pattern data. The battery life prediction device according to claim 1 , wherein: The pattern generation unit generates the test pattern by dividing the power pattern data into at least one section and adjusting a power value of each section.

6. The battery life prediction device according to claim 5, wherein: The pattern generation unit generates the test pattern by multiplying a scaling value by each section to allow the test pattern to have the target parameter value.

7. The battery life prediction apparatus according to claim 6, wherein The power pattern data includes a power pattern actually used in a power system.

8. The battery life prediction apparatus according to claim 1, wherein The power pattern actually used in the power system includes a Pacific Northwest National Laboratory (PNNL) pattern.

9. The battery life prediction apparatus according to claim 8, wherein 10.A battery life prediction method comprising: extracting at least one piece of power pattern data from a plurality of power pattern data used in an actual site; generating a test pattern for predicting a life of a battery based on the extracted power pattern data; and predicting a remaining life of the battery based on the test pattern, wherein the generating of the test pattern includes generating a test pattern having a target parameter value regarding charging and discharging of the battery. The generating of the test pattern includes generating the test pattern by changing a scaling of the power pattern data. The generating of the test pattern includes generating the test pattern by dividing the power pattern data into a plurality of sections and adjusting a power value for each section.

11. The battery life prediction method of claim 10, wherein, ​ 12. The battery life prediction method of claim 11, wherein, ​

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