Battery life prediction device and method

Abstract

A battery life prediction device according to one embodiment of the present invention may include a storage unit that stores a plurality of power pattern data used in actual field situations, a pattern generation unit that generates a test pattern for battery life prediction based on the power pattern data, and a life prediction unit that predicts the remaining life of the battery based on the test pattern.

Description

[Technical field]

[0001] [CROSS REFERENCE TO RELATED APPLICATIONS] The present invention claims the benefit of priority based on Korean Patent Application No. 10-2020-0101865, filed on August 13, 2020, and all contents disclosed in the documents of the Korean patent application are incorporated herein by reference.

[0002] The present invention relates to an apparatus and method for generating a pattern for a charge / discharge test of a battery and predicting the battery life. [Background technology]

[0003] Recently, research and development into secondary batteries has been actively conducted. Here, secondary batteries are batteries that can be charged and discharged, and include conventional Ni / Cd batteries, Ni / MH batteries, and the latest lithium-ion batteries. Among secondary batteries, lithium-ion batteries have the advantage of having a much higher energy density than conventional Ni / Cd batteries, Ni / MH batteries, etc. In addition, lithium-ion batteries can be manufactured to be small and lightweight, and are therefore used as power sources for mobile devices. Furthermore, lithium-ion batteries are being used in an expanded range of applications as power sources for electric vehicles, and are drawing attention as the next generation of energy storage media.

[0004] 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 parallel, and the state and operation of the battery pack are managed and controlled by a battery management system.

[0005] Meanwhile, in the case of Energy Storage Systems (ESS), when predicting the battery remaining rate through charge and discharge cycles, the test is conducted by repeating the same cycle, so there was a problem that the accuracy is low when applied to actual sites where non-identical cycles are used. For example, in such a conventional method, the remaining rate test is conducted while changing the CP-rate in the range of battery voltage from 3V to 4.2V, and the remaining rate (lifespan) of the actual site is predicted using the data derived from this.

[0006] However, in an actual field, charging and discharging are performed while the CP-rate is changed in real time, so the predicted survival rate calculated by mixing survival rate data based on conventional tests that repeat the same cycle was inevitably significantly different from the actual survival rate based on the pattern used in the actual field. Summary of the Invention [Problem to be solved by the invention]

[0007] The present invention has been devised to solve the above problems, and aims to provide a battery life prediction device and method that can improve the accuracy of predicting battery remaining rate in an actual field by generating a test pattern to have parameter values ​​desired by a user based on power pattern data used in the actual field, thereby enabling charge and discharge tests to be performed through a power pattern similar to that in the actual field. [Means for solving the problem]

[0008] A battery life prediction device according to one embodiment of the present invention may include a storage unit that stores a plurality of power pattern data used in actual fields, a pattern generation unit that generates a test pattern for battery life prediction based on the power pattern data, and a life prediction unit that predicts the remaining life of the battery based on the test pattern.

[0009] A battery life prediction method according to one embodiment of the present invention may include a step of extracting at least one power pattern data from a plurality of power pattern data used in an actual field, a step of generating a test pattern for battery life prediction based on the extracted power pattern data, and a step of predicting a remaining life of the battery based on the test pattern. Effect of the Invention

[0010] According to the battery life prediction device and method of the present invention, a test pattern is generated based on power pattern data used in an actual field to have parameter values ​​desired by a user, so that a charge / discharge test can be performed through a power pattern similar to that in the actual field, thereby improving the accuracy of predicting the remaining battery rate in the actual field. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a block diagram showing a configuration of a typical battery pack. [Diagram 2] 1 is a block diagram showing a configuration of a battery life prediction device according to an embodiment of the present invention; [Diagram 3] FIG. 2 is a diagram showing actual PNNL pattern data used in a battery life prediction device according to an embodiment of the present invention. [Figure 4] 4 is a diagram illustrating an example of parameter values ​​and target parameter values ​​of a test pattern actually generated by a battery life prediction device according to an embodiment of the present invention. [Diagram 5] 4 is a diagram illustrating an example of a test pattern and a power histogram generated by a battery life prediction device according to an embodiment of the present invention. [Figure 6] 2 is a flowchart illustrating a battery life prediction method according to an embodiment of the present invention. [Figure 7] 1 is a block diagram showing a hardware configuration of a battery life prediction device according to an embodiment of the present invention; DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, various embodiments of the present invention will be described in detail with reference to the drawings. In this document, the same reference numerals are used for the same components in the drawings, and duplicated descriptions of the same components will be omitted.

[0013] With respect to the various embodiments of the present invention disclosed in this document, specific structural or functional descriptions are merely exemplified for purposes of describing the embodiments of the present invention, and the various embodiments of the present invention may be embodied in various forms and should not be construed as being limited to the embodiments described in this document.

[0014] The terms "first," "second," "primary," or "second" used in the various embodiments may modify various components regardless of order and / or importance, and do not limit the components. For example, a first component may be named as a second component, and similarly, a second component may be named instead of the first component, without departing from the scope of the present invention.

[0015] The terms used in this document are merely used to describe a particular embodiment and are not intended to limit the scope of other embodiments. A singular expression can include a plural expression unless it has a clearly different meaning in the context.

[0016] All terms used herein, including technical or scientific terms, may have the same meaning as commonly understood by a person of ordinary skill in the art of the present invention. Terms defined in commonly used dictionaries may be interpreted as having the same or similar meaning as the meaning they have in the context of the relevant art, and unless otherwise clearly defined in this document, they are not interpreted as ideal or overly formal. In some cases, even terms defined in this document should not be interpreted to exclude embodiments of the present invention.

[0017] FIG. 1 is a block diagram showing the configuration of a typical battery pack.

[0018] Referring to FIG. 1, there is shown a schematic diagram of a battery control system including a battery rack 1 according to an embodiment of the present invention and a host controller 2 included in the host system.

[0019] 1, the battery rack 1 includes a battery module 10 consisting of one or more battery cells and capable of being charged and discharged, a switching unit 14 connected in series to the (+) terminal side or (-) terminal side of the battery module 10 to control the flow of charge and discharge current of the battery module 10, and a battery management system 20 (e.g., MBMS) that monitors the voltage, current, temperature, etc. of the battery rack 1 to control and manage the system to prevent overcharging and overdischarging. In this case, the battery rack 1 may include a plurality of battery modules 10, sensors 12, switching units 14, and battery management systems 20.

[0020] Here, the switching unit 14 is a semiconductor switching element for controlling the flow of current for charging or discharging the multiple battery modules 10, and may be, for example, at least one MOSFET, relay, magnetic contactor, etc. depending on the specifications of the battery rack 1.

[0021] In addition, the battery management system 20 can measure or calculate the voltage and current of the gate, source, drain, etc. of the semiconductor switching element in order to monitor the voltage, current, temperature, etc. of the battery rack 1. In addition, the battery management system 20 can measure the current, voltage, temperature, etc. of the battery rack 1 using the sensor 12 provided adjacent to the semiconductor switching element.

[0022] The battery management system 20 is an interface for receiving input of measured values ​​of the various parameters described above, and may include a plurality of terminals, a circuit connected to the terminals for processing the input values, etc. The battery management system 20 may also control the ON / OFF of the switching unit 14, for example, a MOSFET, and may be connected to the battery modules 10 to monitor the status of each battery module 10.

[0023] Meanwhile, in the battery management system 20 of the present invention, as described below, a test pattern for predicting the remaining rate (life) of a battery can be generated based on power pattern data in an actual field, and the remaining life of the battery can be predicted based on such a test pattern.

[0024] The upper controller 2 can transmit a control signal for the battery module 10 to the battery management system 20. Thus, the operation of the battery management system 20 should be controlled based on the signal applied from the upper controller. Meanwhile, the battery cell of the present invention may be configured to be included in the battery module 10 used in an ESS (Energy Storage System). In this case, the upper controller 2 may be an ESS controller. However, the battery rack 1 is not limited to this use.

[0025] The configuration of the battery rack 1 and the configuration of the battery management system 20 are well known, so a more detailed description thereof will be omitted.

[0026] FIG. 2 is a block diagram showing a configuration of a battery life prediction device according to an embodiment of the present invention.

[0027] Referring to FIG. 2, a battery life prediction device 200 according to an embodiment of the present invention may include a storage unit 210, a pattern generation unit 220, and a life prediction unit 230.

[0028] The storage unit 210 may store a plurality of power pattern data used in an actual field. At this time, the power pattern data stored in the storage unit 210 may include a power pattern actually used in a power system. For example, the power pattern actually used in a power system may be a Frequency Regulation (FR) power pattern, which may include a Pacific Northwest National Laboratory (PNNL) pattern.

[0029] The pattern generating unit 220 may generate a test pattern for predicting the battery life based on the power pattern data stored in the storage unit 210. In this case, the pattern generating unit 220 may generate the test pattern to have target parameter values ​​related to charging and discharging of the battery. In this case, the target parameter values ​​may be values ​​input by a user, and may include, for example, maximum / minimum SOE (state of energy), initial / final SOE, and average CP (charging and discharging strength) in an entire period (e.g., 24 hours) of the power pattern data. However, the target parameter values ​​are not limited thereto, and various other parameter values ​​for defining the charging and discharging pattern of the battery may be used.

[0030] In addition, the pattern generating unit 220 may repeatedly generate the test pattern until the difference between the parameter value of the generated test pattern and the target parameter value becomes less than a preset reference value, which may be arbitrarily set by the user.

[0031] The pattern generating unit 220 may generate a test pattern by changing the scale of the power pattern data. Specifically, the pattern generating unit 220 may generate a test pattern by dividing the power pattern data into sections and adjusting the power value for each section. For example, the pattern generating unit 220 may divide the power pattern data into sections in which similar patterns appear. Also, in order to enable the test pattern to have a target parameter value, the pattern generating unit 220 may generate a test pattern by multiplying each divided section by a scaling value.

[0032] The life prediction unit 230 can predict the remaining life of the battery based on the test pattern. For example, when the test pattern generated by the pattern generation unit 220 and values ​​related to the pattern are input to the life prediction unit 230, the life prediction unit 230 can perform a charge / discharge test of the battery according to a preset algorithm and predict the remaining life of the battery.

[0033] Meanwhile, although not shown in Fig. 2, the battery life prediction device 200 according to an embodiment of the present invention may include an input unit. Thus, a user may directly set a desired target parameter value, etc. through the input unit. For example, the input unit may include a keyboard, a touch pad, a mouse, etc.

[0034] 2, a plurality of power pattern data used in an actual field is described as being stored in the storage unit 210, but the present invention is not limited thereto, and such power pattern data may be stored in a database of an external server. In such a case, the battery life prediction device 200 according to an embodiment of the present invention includes a communication unit (not shown), and can receive power pattern data from the external server via the communication unit and generate a test pattern based on the received power pattern data.

[0035] In this way, according to the battery life prediction device of one embodiment of the present invention, a test pattern can be generated to have parameter values ​​desired by the user based on power pattern data used in the actual field, thereby enabling charge and discharge tests to be performed through a power pattern similar to that in the actual field, thereby improving the accuracy of remaining rate prediction in the actual field.

[0036] 3 is a diagram showing actual PNNL pattern data used in a battery life prediction device according to an embodiment of the present invention. Referring to FIG. 3, the x-axis may be time (hours / minutes / seconds) and the y-axis may show SOE (%).

[0037] The PNNL pattern in Fig. 3 is a power pattern actually used in the European power market. Here, the PNNL pattern may be a power pattern that indicates that a power supply device such as a battery is charged and discharged for 24 hours. As shown in Fig. 3, the PNNL pattern actually used in the field may appear in a form that is repeated at regular time intervals.

[0038] Meanwhile, the PNNL pattern in Fig. 3 is merely an example, and the PNNL pattern used in the battery life prediction device according to an embodiment of the present invention is not limited to the one shown in Fig. 3. Also, in addition to the PNNL pattern, various power pattern data used in the actual power market may be used.

[0039] FIG. 4 is a diagram illustrating exemplary parameter values ​​and target parameter values ​​of a test pattern actually generated by a battery life prediction device according to an embodiment of the present invention.

[0040] 4, a test pattern is generated with a sampling time of 4 seconds in a battery life prediction device according to an embodiment of the present invention. As shown in FIG. 4, target parameters may be calculated for maximum / minimum SOE, initial / final SOE, and average CP.

[0041] In Fig. 4, normalized values ​​and scale-changed values ​​are calculation values ​​used to generate a test pattern in a battery life prediction device according to an embodiment of the present invention. Also, referring to Fig. 4, it can be seen that the target parameter values ​​are maximum / minimum SOE of 80% and 20%, initial / final SOE of 50%, and average CP of 0.5000 (1 / hr). Such target parameter values ​​may be directly input by a user.

[0042] On the other hand, the parameter values ​​of the test pattern generated to achieve such target parameter values ​​by the battery life prediction device according to one embodiment of the present invention are, as shown in FIG. 4, maximum / minimum SOE of 82.08% and 20.44%, initial / final SOE of 50 and 50.05%, respectively, and average CP of 0.5091 (1 / hr).

[0043] In this case, if each of the parameter values ​​of the generated test pattern is within a preset reference range for the target parameter value, the test pattern can be used to calculate the remaining life of the battery. However, if one or more of the parameter values ​​of the generated test pattern deviate from the preset reference range for the target parameter value, the test pattern can be repeatedly generated until the parameter values ​​are within the preset reference range.

[0044] FIG. 5 is a diagram illustrating an example of a test pattern and a power histogram generated by a battery life prediction device according to an embodiment of the present invention.

[0045] In Fig. 5, the upper graph shows the generated test patterns, with the x-axis showing time (hours / minutes / seconds) and the y-axis showing SOE (%). The lower graph in Fig. 5 shows a power histogram for the generated test patterns, with the x-axis showing the range of SOE and the y-axis showing the number of data for each magnitude of SOE. Each test pattern in Fig. 5 was generated using the PNNL pattern data in Fig. 3.

[0046] As shown in Fig. 5, various test patterns can be generated even if the same PNNL pattern data of Fig. 3 is used. That is, after dividing the PNNL pattern data of Fig. 3 into several sections according to similar patterns, scaling (e.g., repeatedly multiplying the power value of each section by a specific value) to have a target parameter value for each section can be performed to produce various test pattern shapes.

[0047] FIG. 6 is a flowchart illustrating a battery life prediction method according to an embodiment of the present invention.

[0048] Referring to FIG. 6, in a battery life prediction method according to an embodiment of the present invention, first, at least one power pattern data is extracted from a plurality of pre-stored power pattern data (S610). At this time, the plurality of power pattern data may be stored in the above-mentioned storage unit or an external server. In addition, the power pattern data may include a power pattern actually used in the power system. For example, the power pattern actually used in the power system is a frequency regulated (FR) power pattern, which may include a PNNL pattern.

[0049] Then, a test pattern for predicting battery life is generated based on the extracted power pattern data (S620). In this case, in step S620, the test pattern can be generated to have target parameter values ​​related to charging and discharging the battery. In this case, the target parameter values ​​can be values ​​input by the user, and for example, the target parameter values ​​can include maximum / minimum SOE, initial / final SOE, and average CP.

[0050] Also, in step S620, the test pattern may be generated by changing the scale of the power pattern data. Specifically, the test pattern may be generated by dividing the power pattern data into sections and adjusting the power value for each section. For example, the test pattern may be generated by multiplying each divided section by a scaling value so that the test pattern has a target parameter value.

[0051] Next, in step S630, it is determined whether the difference between the parameters of the generated test pattern and the target parameters is less than the reference value (S630). If the difference between the parameters of the generated test pattern and the target parameters is equal to or greater than the reference value (NO), the process returns to step S620. That is, the test pattern can be repeatedly generated until the difference between the parameter values ​​of the generated test pattern and the target parameter values ​​becomes less than the preset reference value.

[0052] On the other hand, if the difference between the parameters of the generated test pattern and the target parameters is less than the reference value (YES), the remaining life of the battery is predicted based on the generated test pattern (S640). For example, when the test pattern generated in step S620 and the numerical values ​​related to the test pattern are input, a charge / discharge test of the battery is performed according to a preset algorithm, and the remaining life of the battery can be predicted.

[0053] In this way, according to the battery life prediction method of the present invention, a test pattern is generated based on power pattern data used in an actual field to have parameter values ​​desired by the user, and thus a charge / discharge test can be performed through a power pattern similar to that in the actual field, thereby improving the accuracy of predicting the remaining battery rate in the actual field.

[0054] FIG. 7 is a block diagram showing a hardware configuration of a battery life prediction device according to an embodiment of the present invention.

[0055] Referring to FIG. 7, a battery life prediction device 700 according to an embodiment of the present invention may include an MCU 710, a memory 720, an input / output I / F 730, and a communication I / F 740.

[0056] The MCU 710 may 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, generates a battery test pattern through such programs, processes various data for predicting battery life, and performs the functions of FIG. 2 described above.

[0057] The memory 720 can store various programs related to generating battery test patterns, predicting battery life, etc. The memory 720 can also store various data such as power pattern data (e.g., PNNL data) and test pattern data used in the actual field.

[0058] A plurality of such memories 720 may be provided as necessary. The memories 720 may be volatile memories or non-volatile memories. The volatile memories 720 may be RAM, DRAM, SRAM, etc. The non-volatile memories 720 may be ROM, PROM, EAROM, EPROM, EEPROM, flash memory, etc. The examples of the memories 720 listed above are merely illustrative and are not limited to these examples.

[0059] The input / output I / F 730 can provide an interface that connects the MCU 710 to input devices (not shown) such as a keyboard, mouse, or touch panel, and output devices such as a display (not shown), enabling data to be transmitted and received.

[0060] The communication I / F 740 is configured to be capable of transmitting and receiving various data to and from a server, and may be any device capable of supporting wired or wireless communication. For example, power pattern data, programs for generating test patterns and predicting battery life, and various data may be transmitted and received from a separately provided external server via the communication I / F 740.

[0061] In this manner, a computer program according to an embodiment of the present invention may be recorded in the memory 720 and processed by the MCU 710, thereby realizing, for example, a module that performs each of the functional blocks shown in FIG.

[0062] Although all components constituting the embodiments of the present invention have been described as being combined into one or operating in combination, the present invention is not necessarily limited to these embodiments. That is, within the scope of the purpose of the present invention, all components may be selectively combined into one or more and operate.

[0063] In addition, the terms "comprise", "constitute" or "have" described above mean that the relevant element may be present unless otherwise specified, and should be interpreted as including other elements, not excluding other elements. All terms, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the present invention belongs, unless otherwise defined. Terms commonly used, such as terms defined in a dictionary, should be interpreted as consistent with the contextual meaning of the relevant art, and should not be interpreted in an idealized and overly formal sense unless clearly defined in the present invention.

[0064] The above description is merely illustrative of the technical idea of ​​the present invention, and a person having ordinary knowledge in the technical field to which the present invention belongs may make various modifications and variations without departing from the essential characteristics of the present invention. Therefore, the embodiments disclosed in the present invention are for illustrative purposes only, and are not intended to limit the technical idea of ​​the present invention. The scope of the present invention should be interpreted according to the following claims, and all technical ideas within the scope equivalent thereto should be interpreted as being included in the scope of the present invention. [Explanation of symbols]

[0065] 1 Battery Rack 2 Upper controller 10 Battery Module 12 Sensors 14 Switching section 20 Battery Management System 200 Battery life prediction device 210 Storage Unit 220 Pattern Generation Unit 230 Life Prediction Section S620 Step S630 Step 700 Battery Life Predictor 710 MCU 720 Memory 730 Input / Output Interface 740 Communication I / F

Claims

1. A storage unit for storing a plurality of power pattern data; a pattern generator that generates a test pattern for predicting a life span of the battery based on the power pattern data so as to have a target parameter value related to charging and discharging of the battery; and A life prediction unit that predicts a remaining life of the battery based on the test pattern. Including, The plurality of power pattern data are patterns that are repeated at regular time intervals. Battery life predictor.

2. The battery life prediction device according to claim 1 , wherein the target parameter value is a value input by a user.

3. The battery life prediction device according to claim 1 or 2, wherein the target parameter value includes at least one of a maximum SOE, a minimum SOE, a first SOE, and a final SOE.

4. The battery life prediction device according to any one of claims 1 to 3, 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.

5. A storage unit for storing a plurality of power pattern data; a pattern generator that changes the scale of the power pattern data to generate a test pattern for predicting a battery life; and A life prediction unit that predicts a remaining life of the battery based on the test pattern. Including, The plurality of power pattern data are patterns that are repeated at regular time intervals. Battery life predictor.

6. The battery life prediction device according to claim 5 , wherein the pattern generation unit generates the test pattern by dividing the power pattern data into sections and adjusting a power value for each section.

7. The battery life prediction device according to claim 6 , wherein the pattern generation unit generates the test pattern by multiplying each of the divided intervals by a scaling value so as to enable the test pattern to have a target parameter value.

8. The battery life prediction device according to any one of claims 1 to 7, wherein the power pattern data includes a power pattern actually used in a power system.

9. The battery life prediction device according to claim 8 , wherein the power pattern actually used in the power system includes a frequency regulated power pattern.

10. A step of extracting at least one power pattern data from the plurality of power pattern data; generating a test pattern for predicting the life of the battery based on the extracted power pattern data so as to have target parameter values ​​related to charging and discharging the battery; and predicting a remaining life of the battery based on the test pattern; Including, The plurality of power pattern data are patterns that are repeated at regular time intervals. Battery life prediction method.

11. A step of extracting at least one power pattern data from a plurality of power pattern data; rescaling the extracted power pattern data to generate a test pattern for battery life prediction; and predicting a remaining life of the battery based on the test pattern; Including, The plurality of power pattern data are patterns that are repeated at regular time intervals. Battery life prediction method.

12. The method of claim 11 , wherein the step of generating the test pattern comprises the steps of: dividing the power pattern data into sections; and adjusting a power value for each section to generate the test pattern.

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