Battery diagnostic device

Abstract

This invention provides a battery diagnostic device that can accurately analyze the vehicle's usage status and effectively utilize this information for diagnosing the vehicle's battery. [Solution] A battery diagnostic device for diagnosing a battery mounted on a vehicle, comprising: a first processing unit that acquires parking data from the vehicle indicating the period during which the vehicle was parked; a second processing unit that performs frequency analysis of the parking data using a plurality of specific periods of different lengths as window functions; a third processing unit that maps a plurality of results obtained from the frequency analysis performed for each of the plurality of window functions onto a four-dimensional map represented by elapsed time, frequency, specific period, and intensity; and a fourth processing unit that utilizes the mapping results to the four-dimensional map for battery diagnosis.

Description

Technical Field

[0001] The present disclosure relates to a battery diagnosis device capable of diagnosing a battery mounted on a vehicle.

Background Art

[0002] Patent Document 1 discloses a device for improving the prediction accuracy when predicting the remaining life of a vehicle battery (battery). In this device, it is described that the remaining life of the battery is predicted by learning time-series data of battery degradation indicators and the like.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the neural network used for battery degradation estimation in the device described in Patent Document 1 above, values are extracted daily, and on days without vehicle driving (trips), they are interpolated with previous values, and treated as data for the past 30 days with no missing values, one value per day. However, with such a way of handling values, even when there are multiple trips on the same day, only one value is extracted. Therefore, for example, even if there are 10 trips in a day, only the value of one of the trips is targeted, and even if the values of the remaining 9 trips are values that affect degradation estimation, they are not reflected at all.

[0005] Therefore, there is room for further consideration in the analysis method of data used for in-vehicle battery degradation diagnosis in order to improve accuracy.

[0006] This disclosure was made in view of the above-mentioned problems, and aims to provide a battery diagnostic device that can accurately analyze the usage status of a vehicle and effectively utilize this information for diagnosing the on-board battery. [Means for solving the problem]

[0007] To solve the above problems, one aspect of the disclosed technology is a battery diagnostic device for diagnosing a battery mounted on a vehicle, comprising: a first processing unit that acquires parking data from the vehicle indicating the period during which the vehicle was parked; a second processing unit that performs frequency analysis of the parking data using a plurality of specific periods of different lengths as window functions; a third processing unit that maps a plurality of results obtained from the frequency analysis performed for each of the plurality of window functions to a four-dimensional map represented by elapsed time, frequency, specific period, and intensity; and a fourth processing unit that utilizes the mapping results to the four-dimensional map for diagnosing the battery. [Effects of the Invention]

[0008] The battery diagnostic device of this disclosure can suitably extract and accurately analyze situations in which a vehicle is repeatedly subjected to usage conditions that are estimated to cause significant damage to the vehicle battery. This makes it possible to effectively utilize the results of this accurate analysis for battery diagnosis. [Brief explanation of the drawing]

[0009] [Figure 1] Schematic diagram of a battery diagnostic device according to one embodiment of the present disclosure. [Figure 2A] Process flowchart for battery diagnostic control performed by the battery diagnostic device. [Figure 2B] Process flowchart for battery diagnostic control performed by the battery diagnostic device. [Figure 3] An example of a binary sequence of ON / OFF values. [Figure 4] An illustrative diagram showing how the ON / OFF ratio changes depending on the location during a specific period. [Figure 5] Conceptual diagram of a 4D map [Modes for carrying out the invention]

[0010] Vehicle batteries are prone to damage and deterioration due to repeated ignition on / off cycles (IG-ON / OFF) during daily driving, which can cause multiple trips. Furthermore, the number of trips per day is not constant, and the timing of trips is unpredictable and depends on the driver. Therefore, this disclosure proposes a method that uses frequency analysis such as Fast Fourier Transform (FFT) to represent situations such as repeated short-term IG-ON / OFF cycles and long-term parking on the same axis. The embodiments of this disclosure will be described in detail below with reference to the drawings.

[0011] <Embodiment> [composition] Figure 1 is a functional block diagram showing a schematic configuration of a battery diagnostic device 20 and a vehicle 10 according to one embodiment of the present disclosure. The battery diagnostic device 20 illustrated in Figure 1 is connected to the vehicle 10 in a communicative manner.

[0012] (1) Vehicle The vehicle 10 is, for example, an automobile such as a hybrid electric vehicle (HEV), a plug-in hybrid electric vehicle (PHEV), or a battery electric vehicle (BEV). This vehicle 10 is equipped with at least a data acquisition unit 11 and a data transmission unit 12. Although Figure 1 shows an example in which one vehicle 10 is connected to the battery diagnostic device 20 in a communicative manner, multiple vehicles 10 may be connected to the battery diagnostic device 20 in a communicative manner.

[0013] The data acquisition unit 11 acquires data related to the state of the vehicle 10 as needed. In this embodiment, the data acquisition unit 11 acquires at least "parking data" indicating the period during which the vehicle 10 was parked, as data related to the state of the vehicle 10. Examples of this parking data include the IG signal indicating the ON state (IG-ON) / OFF state (IG-OFF) of the ignition switch of the vehicle 10, and information on the time when the ON / OFF state of the IG signal switches.

[0014] The data transmission unit 12 communicates between the vehicle 10 and the battery diagnostic device 20. This data transmission unit 12 transmits data regarding the state of the vehicle 10 acquired by the data acquisition unit 11 to the battery diagnostic device 20. In this embodiment, the data transmission unit 12 transmits at least a predetermined amount of parking data (for example, one day's worth) as data regarding the state of the vehicle 10. The timing of the transmission can be exemplified by when the ignition switch of the vehicle 10 transitions to IG-ON or IG-OFF (i.e., each trip).

[0015] (2) Battery diagnostic device The battery diagnostic device 20 is installed, for example, in a center that comprehensively manages the status of multiple vehicles 10. This battery diagnostic device 20 comprises a first processing unit 21, a second processing unit 22, a third processing unit 23, and a fourth processing unit 24.

[0016] The first processing unit 21 receives data regarding the state of the vehicle 10 transmitted from the vehicle 10 (including parking data for a predetermined period). The parking data received by the first processing unit 21 is stored in a storage unit (not shown) or the like. The second processing unit 22 performs frequency analysis on the parking data received and stored by the first processing unit 21, using a plurality of specific periods with different lengths (sizes) as window functions. The third processing unit 23 maps the plurality of results obtained by the frequency analysis respectively performed by the second processing unit 22 for the plurality of window functions to a four-dimensional map expressed by elapsed time, frequency, specific period, and intensity. The fourth processing unit 24 utilizes the results mapped to the four-dimensional map by the third processing unit 23 for diagnosing the in-vehicle battery. Details of the processing performed by the first processing unit 21, the second processing unit 22, the third processing unit 23, and the fourth processing unit 24 will be described below.

[0017] [Control] Next, referring further to FIGS. 2A, 2B, 3, 4, and 5, the control performed by the battery diagnosis device 20 according to an embodiment of the present disclosure will be described.

[0018] FIGS. 2A and 2B are flowcharts for explaining the processing procedures of the battery diagnosis control executed by each component of the battery diagnosis device 20. The processing in FIG. 2A and the processing in FIG. 2B are respectively connected by connectors X, Y, and Z. The battery diagnosis control shown in FIGS. 2A and 2B is started, for example, when the first processing unit 21 of the battery diagnosis device 20 receives parking data from the vehicle 10. When there are a plurality of vehicles 10 communicably connected to the battery diagnosis device 20, the battery diagnosis control is performed for each vehicle 10.

[0019] (Step S201) The second processing unit 22 extracts parking data for a predetermined period from the parking data transmitted from the vehicle 10 and stored in a storage unit (not shown). The predetermined period is not particularly limited, but can be set to any value (such as several months or one year) depending on the computation load and required accuracy. Once the second processing unit 22 has extracted the parking data for the predetermined period, the process proceeds to step S202.

[0020] (Step S202) The second processing unit 22 converts the extracted past parking data for a predetermined period into a binary sequence of ON / OFF values. Specifically, the second processing unit 22 converts the past parking data for a predetermined period into a binary sequence in which the period of driving with the vehicle 10's ignition switch in the ON state (IG-ON) is represented by the value "1", and the period of parking with the vehicle 10's ignition switch in the OFF state (IG-OFF) is represented by the value "0". An example of this binary sequence is shown in Figure 3. The switching unit between 1 and 0 in the binary sequence can be, for example, 1 second. Once the second processing unit 22 has converted the past parking data for a predetermined period into a binary sequence of ON / OFF values, the process proceeds to step S203.

[0021] (Step S203) The second processing unit 22 extracts data for a predetermined period, known as a "specific period," from the binary sequence of ON / OFF values ​​obtained by converting the parking data. Here, the specific period (initial value) from which data is initially extracted should preferably be a period (for example, 1 day) that is sufficiently shorter than the predetermined past period extracted in step S201 above. Once the second processing unit 22 has extracted data for the specific period from the binary sequence of ON / OFF values, the process proceeds to step S204.

[0022] (Step S204) The second processing unit 22 calculates the ratio of the period in which the value is "1 (ON)" (the period of operation) relative to the specific period, based on the data (extracted data) extracted from the binary sequence of ON / OFF values. This calculated ratio (= total ON period / specific period) is used to determine whether or not an effective frequency analysis can be performed. For example, as shown in Figure 4(a), if the vehicle 10 is driven and parked at a moderate rate and the ratio becomes large, the data has periodicity and the intensity of nearby frequencies in the spectrum becomes large, so an effective frequency analysis can be expected. On the other hand, as shown in Figure 4(b), if the vehicle 10 is parked for a long period and the ratio becomes small, the spectrum becomes zero and an effective frequency analysis cannot be expected. Once the second processing unit 22 has calculated the ratio of the period in which the value is "1 (ON)" relative to the specific period, the process proceeds to step S205.

[0023] (Step S205) The second processing unit 22 determines whether the ratio of the period with the value "1 (ON)" to the calculated specific period exceeds the first threshold. This determination is made to determine whether or not frequency analysis should be performed. Therefore, this first threshold is set to an arbitrary value that allows it to determine that a valid frequency analysis can be performed using the data for the specific period. If the second processing unit 22 determines that the ratio of the period with the value "1 (ON)" to the specific period exceeds the first threshold (step S205, yes), the process proceeds to step S206. On the other hand, if the second processing unit 22 determines that the ratio of the period with the value "1 (ON)" to the specific period does not exceed the first threshold (step S205, no), the process proceeds to step S209.

[0024] (Step S206) The second processing unit 22 performs frequency analysis using a specific period from which data exceeding a first threshold ratio has been extracted as a window function. This frequency analysis uses well-known methods such as the Fast Fourier Transform (FFT). Once the second processing unit 22 has performed frequency analysis using the specific period as a window function, the process proceeds to step S207.

[0025] (Step S207) The third processing unit 23 maps the results obtained from the frequency analysis performed in step S206 onto a four-dimensional map represented by elapsed time, frequency, specific period, and intensity. Figure 5 shows an image of the four-dimensional map. In the example in Figure 5, the elapsed time is represented on the first axis, the frequency on the second axis, the specific period on the third axis, and the arrangement (distribution) of spectral intensity on each specific period is represented as the fourth axis, thereby forming a four-dimensional map. In other words, the state in which values ​​with intensity (magnitude) exist on a three-dimensional coordinate system is expressed as four dimensions. The mapped values ​​serve as indicators representing the characteristics of how the vehicle 10 is turned on and off. Once the results obtained from the frequency analysis are mapped onto the four-dimensional map by the second processing unit 22, the process proceeds to step S208.

[0026] (Step S208) The second processing unit 22 determines whether or not to terminate the frequency analysis. That is, the second processing unit 22 determines whether or not a sufficient number of frequency analysis results have been obtained for vehicle 10 to diagnose the vehicle battery. This determination can be arbitrarily set based on the desired number of results, sampling range, etc. If the second processing unit 22 determines to terminate the frequency analysis (step S208, yes), the process proceeds to step S212. On the other hand, if the second processing unit 22 determines not to terminate the frequency analysis (step S208, no), the process proceeds to step S209.

[0027] (Step S209) The second processing unit 22 sets the specific period for extracting data from the ON / OFF binary sequence to be longer (larger) than the current setting. For example, if the current specific period is set to 1 day, the second processing unit 22 changes and resets the specific period to 2 days or 1 week. The length (size) of the reset specific period is not particularly limited. Once the second processing unit 22 sets the specific period to be longer than the current one, the process proceeds to step S210.

[0028] (Step S210) The second processing unit 22 determines whether the reset specific period exceeds the second threshold. This determination is made to determine whether accurate processing is not possible, such as when the reset specific period cannot be specified. Therefore, this second threshold is set to, for example, a predetermined past period. If the second processing unit 22 determines that the specific period exceeds the second threshold (step S210, yes), the process proceeds to step S211. On the other hand, if the second processing unit 22 determines that the specific period does not exceed the second threshold (step S210, no), the process proceeds to step S203.

[0029] (Step S211) The second processing unit 22 performs a predetermined error handling because it is unable to perform accurate processing. Examples of this error handling include displaying on a predetermined screen that the set specific period cannot be specified. Once the error handling is performed by the second processing unit 22, the process proceeds to step S212.

[0030] (Step S212) The fourth processing unit 24 determines whether the results of the frequency analysis performed on the parking data for a predetermined period of time extracted in step S201 are standard, based on the results of the multiple mappings (indicators representing the characteristics of how the vehicle 10 turns the IG-ON / OFF) performed on the four-dimensional map in step S207. This determination can be made, for example, as follows.

[0031] These results (indicators) can be further divided into groups such as normal / abnormal using machine learning or discriminant formulas. Alternatively, a 4D map of a standard model created based on the usage conditions of vehicle 10 estimated to cause little damage to the vehicle battery, and a 4D map of an error model created based on the usage conditions of vehicle 10 estimated to cause significant damage to the vehicle battery, could be prepared in advance. The distance (e.g., Euclidean distance) between the 4D map obtained in this process and each of the standard model 4D map and error model 4D map could then be calculated to determine which one is closer. This would allow for accurate determination of whether the vehicle battery is in a standard state or approaching an error state. The results of such determinations can be effectively used for diagnosing the vehicle battery.

[0032] When the fourth processing unit 24 determines whether the results of the frequency analysis of the parking data are within the standard range, this battery diagnostic control is terminated.

[0033] <Effects and Actions> According to the battery diagnostic device 20 of one embodiment of the present disclosure described above, parking data of the vehicle 10 is acquired from the vehicle 10, the length (size) of a specific period is changed based on the ratio indicating the ratio of driving (ON) / parking (OFF) in the parking data, frequency analysis of the parking data is performed on multiple specific periods of different lengths as window functions, and multiple results obtained from the frequency analysis performed on each of the multiple window functions are mapped to a four-dimensional map (elapsed time, frequency, specific period, and intensity).

[0034] This process allows for the accurate extraction and analysis of situations in which vehicle 10 is repeatedly subjected to usage conditions that are estimated to cause significant damage to the vehicle's battery. The results of this accurate analysis (mapping results to a 4D map) can then be effectively utilized for diagnosing the vehicle's battery.

[0035] Furthermore, in this embodiment, the durability limit range can be separately determined from the behavior of other vehicles (how they are used in the market) and development data, and a fourth value can be used for comparison in the dimensions of the first to third axes. Alternatively, the distance (Euclidean distance, etc.) to maps with many short trips where the ignition is turned on and off repeatedly in short cycles, or to maps where the vehicle is parked for extended periods, can be calculated based on the normal state, and the possibility of future malfunctions can be indicated. Alternatively, it can be linked to a system that prevents battery drain by sending notifications prompting changes in driving frequency or notifications prompting maintenance (checking) at a dealership.

[0036] Furthermore, the map used to map the results of the frequency analysis may be a map represented in five or more dimensions.

[0037] Although one embodiment of the disclosed technology has been described above, the disclosure can be understood not only as a battery diagnostic device, but also as a method performed by the battery diagnostic device, a program for that method, a computer-readable non-temporary storage medium storing that program, a vehicle equipped with the battery diagnostic device, and so on. [Industrial applicability]

[0038] The battery diagnostic device described herein can be used to diagnose the condition of a battery installed in a vehicle, among other applications. [Explanation of Symbols]

[0039] 10 vehicles 11 Data Acquisition Unit 12 Data transmission section 20 Battery diagnostic device 21 First Processing Unit 22 Second Processing Unit 23 Third Processing Unit 24. Section 4

Claims

1. A battery diagnostic device for diagnosing the battery installed in a vehicle, A first processing unit acquires parking data from the vehicle indicating the period during which the vehicle was parked, A second processing unit performs frequency analysis of the parking data using multiple specific periods of different lengths as window functions, A third processing unit maps the multiple results obtained from frequency analyses performed for each of the aforementioned multiple window functions to a four-dimensional map represented by elapsed time, frequency, the specified period, and intensity. The system includes a fourth processing unit that utilizes the mapping results to the four-dimensional map for diagnosing the battery. Battery diagnostic device.

2. The second processing unit uses the plurality of specific periods in the parking data where the proportion of driving conditions exceeds a first threshold as the window function. The battery diagnostic device according to claim 1.

3. The second processing unit gradually extends the length of the specified period from an initial value, and if the specified period exceeds a second threshold, it performs a predetermined error processing. The battery diagnostic device according to claim 1 or 2.

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