Deterioration diagnosis device and deterioration diagnosis method for on-vehicle rotary electric machine

Abstract

A deterioration diagnosis device and a deterioration diagnosis method for an on-vehicle rotary electric machine according to one aspect of the present invention acquire a signal indicating a temperature of a coil of the rotary electric machine detected by a temperature detector, monitor and store a history of temperature distribution of the coil and a history of a difference in temperature change of the coil, and determine a degree of deterioration of the rotary electric machine from the history of temperature distribution and the history of the difference in temperature change. This enables the degree of deterioration of the rotary electric machine to be determined with high accuracy by deterioration diagnosis taking into account temperature changes along with operating time at high temperatures.

Description

In-vehicle rotating electrical machine deterioration diagnosis device and deterioration diagnosis method

[0001] The present invention relates to an in-vehicle rotating electrical machine deterioration diagnosis device and a deterioration diagnosis method.

[0002] The in-vehicle rotating electrical machine deterioration diagnosis device and deterioration diagnosis method disclosed in Patent Document 1 store the estimated life at a predetermined temperature of the rotating electrical machine measured in advance, acquire data on the coil temperature detected by the temperature detection means accompanying the use of the rotating electrical machine, integrate the usage time corresponding to the predetermined temperature of the estimated life based on the Arrhenius law, and determine the deterioration state with respect to the estimated life.

[0003] Japanese Patent Application Laid-Open No. 2014-025753

[0004] In conventional deterioration diagnosis, the deterioration of the coil insulation layer, which is a cause of deterioration of the rotating electrical machine, was determined only from the usage time at high temperature of the rotating electrical machine. However, in reality, as the temperature of the rotating electrical machine changes (in other words, the heat cycle), the coil insulation layer expands and contracts, causing wear on the coil insulation layer, and such wear also contributed to the deterioration of the coil insulation layer. Therefore, in conventional deterioration diagnosis that does not consider temperature changes, there is a possibility of underestimating the degree of deterioration.

[0005] Therefore, an object of the present invention is to provide an in-vehicle rotating electrical machine deterioration diagnosis device and a deterioration diagnosis method that can accurately determine the degree of deterioration of the rotating electrical machine by deterioration diagnosis considering temperature changes together with the usage time at high temperature.

[0006] Therefore, the in-vehicle rotating electrical machine deterioration diagnosis device and deterioration diagnosis method according to the present invention, in one aspect, acquire a signal of the temperature of the coil of the rotating electrical machine detected by a temperature detector, monitor and store the history of the temperature distribution of the coil and the history of the difference in temperature change of the coil, and determine the degree of deterioration of the rotating electrical machine from the history of the temperature distribution and the history of the difference in temperature change.

[0007] According to the present invention, the degree of deterioration of the rotating electrical machine can be accurately determined by deterioration diagnosis considering temperature changes together with the usage time at high temperature.

[0008] This is a block diagram of the drive system of an electric vehicle. This is a diagram showing a histogram representing the history of the temperature distribution of a coil. This is a diagram showing a map representing the history of the difference in temperature changes of a coil. This is a time chart illustrating the sampling of the difference ΔTe. This is a flowchart showing the process flow for monitoring and storing the history of coil temperature. This is a diagram showing a map representing the history of the difference in temperature changes of a coil. This is a time chart illustrating the sampling of the difference ΔTe'. This is a flowchart showing the process flow for deterioration diagnosis of a rotating electric machine. This is a time chart showing the time of change in coil temperature.

[0009] Hereinafter, embodiments of the vehicle-mounted rotating electric machine deterioration diagnosis device and deterioration diagnosis method according to the present invention will be described with reference to the drawings. Figure 1 is a block diagram showing the drive system of an electric vehicle 100 as one embodiment of a vehicle on which a rotating electric machine is mounted. Note that the rotating electric machine may be either a generator or an electric motor.

[0010] The drive system of the electric vehicle 100 includes a 12V battery 101, a DC / DC converter 102, a distribution unit (in other words, a junction box) 103 for consolidating wiring, an on-board charger 104 for voltage conversion, a drive battery 105, a standard charging connector 106, a fast charging connector 107, a driving motor 108 which is a rotating electric machine, an inverter 109 for changing the rotational speed of the driving motor 108, a transmission 110 for transmitting the output torque of the driving motor 108 to the drive wheels, and a power controller 111 for controlling the driving motor 108.

[0011] Here, the power controller 111 is an electronic control device that includes an MPU (Micro Processor Unit) and non-volatile memory, and has the function of controlling the drive motor 108 for the operation of the electric vehicle 100, as well as the function of a degradation diagnostic device that determines the degree of degradation of the drive motor 108, which is a rotating electric machine. In other words, in this embodiment, the power controller 111 corresponds to a degradation diagnostic device.

[0012] The power controller 111 acquires a signal from a temperature sensor 112, which acts as a temperature detector to detect the coil temperature (or a temperature correlated with the coil temperature) of the traction motor 108, in order to diagnose the deterioration of the traction motor 108. Based on the history of the coil temperature of the traction motor 108, the power controller 111 determines the degree of deterioration of the traction motor 108. The results of the deterioration diagnosis of the traction motor 108 by the power controller 111 are stored in the non-volatile memory of the power controller 111 and used for providing deterioration information to maintenance shops, determining whether the traction motor 108 can be reused, and deciding whether to provide inspection guidance to users.

[0013] The following describes in detail the degradation diagnosis (degradation diagnosis method) of the traction motor 108 performed by the power controller 111. In the degradation diagnosis of the traction motor 108, the power controller 111 monitors and stores the history of the temperature distribution of the traction motor 108's coils and the history of the difference in temperature changes of the traction motor 108's coils (in other words, the history of the range of fluctuations in coil temperature), and determines the degree of degradation of the traction motor 108 based on this history.

[0014] Figure 2 is an example of a histogram showing the history of the coil's temperature distribution. In the histogram in Figure 2, the horizontal axis represents the coil temperature, and the vertical axis represents the cumulative usage time for each interval of the coil temperature. In rotating electric machines such as the traction motor 108, if the usage time at high coil temperatures is prolonged, chemical changes can occur, causing cracks in the coil insulation layer and worsening the degree of deterioration. Therefore, if the history of the coil's temperature distribution shown in the histogram in Figure 2 indicates that the usage time at high temperatures above a predetermined temperature (hereinafter referred to as high-temperature usage time) was long, it can be estimated that the degree of deterioration is worse than when the high-temperature usage time was relatively short.

[0015] Furthermore, Figure 3 is an example of a map representing the history of the difference in the temperature change of the coil. In the map of Figure 3, the coil temperature is taken as both axes, and the number of history occurrences n for each difference, in other words, the number of experiences, is accumulated and stored in each grid. Specifically, the horizontal axis of the map of Figure 3 is the temperature of the coil when the power of the electric vehicle 100 is turned on (in other words, when the power switch such as the ignition switch is switched from off to on), and the vertical axis of the map of Figure 3 is the maximum value of the coil temperature while the power of the electric vehicle 100 is turned on (in other words, while the power switch is on).

[0016] In other words, the difference between the coil temperature when the power is turned on and the maximum value of the coil temperature while the power switch is on is defined as the difference in the coil temperature change (range of fluctuation), and the number of history occurrences n is accumulated for each difference. The larger the difference in the coil temperature change and the greater the number of history occurrences n for that difference, the more it is estimated that the degree of deterioration is worsening. Note that in the map in Figure 3, the lower the coil temperature when the power is turned on and the higher the maximum value of the coil temperature while the power switch is on, the larger the difference in the coil temperature change.

[0017] Figure 4 is a time chart illustrating the sampling of the difference ΔTe, where ΔTe is defined as the difference in coil temperature change, between the coil temperature when the power is turned on and the maximum value of the coil temperature while the power switch is on. At time t1 in Figure 4, when the power switch is switched from off to on, the coil temperature at that time is stored as the lowest coil temperature Temin (in other words, the initial temperature) during that trip.

[0018] Then, at time t2 in Figure 4, when the coil temperature reaches its maximum value Temax1, the number of history counts n corresponding to the combination of the minimum coil temperature Temin and the maximum value Temax1 in Figure 3 is incremented. In other words, the number of history counts n corresponding to the difference ΔTe1 between the minimum coil temperature Temin and the maximum value Temax1 is incremented.

[0019] Figure 5 is a flowchart showing the process flow of monitoring and storing the coil temperature history performed by the power controller 111. In step S1, the power controller 111 determines whether or not the power is on, in other words, whether or not the power switch has been switched from off to on.

[0020] If the power is on, the power controller 111 proceeds to step S2, acquires and saves the coil temperature detected at that timing, and then proceeds to step S3. On the other hand, if the power is not on but is in the process of being on, the power controller 111 bypasses step S2 and proceeds to step S3.

[0021] In step S3, the power controller 111 acquires detected coil temperature values ​​at regular sampling intervals. Then, from the coil temperatures acquired at regular sampling intervals in step S3, the power controller 111 calculates the coil temperature distribution history (see Figure 2).

[0022] Furthermore, in the next step S4, the power controller 111 determines whether the coil temperature has reached a maximum value based on whether the sign of the time derivative of the coil temperature has reversed from positive (increase) to negative (decrease). If the power controller 111 detects a new maximum value, it saves that maximum value in step S5. If it does not detect a new maximum value, it retains the previous maximum value in step S6.

[0023] In step S7, the power controller 111 determines whether the power switch has switched from on to off, that is, whether the power has been turned off. If the power switch remains on (in other words, if the power is on), the power controller 111 returns to step S3 and repeats the sampling process of coil temperature and maximum value. On the other hand, if the power switch switches from on to off, the power controller 111 proceeds to step S8 and stores the coil temperature history information sampled during this trip in the non-volatile memory.

[0024] Incidentally, the difference in coil temperature change is not limited to the difference between the coil temperature when the power is turned on and the maximum value of the coil temperature while the power is on, but can also be the difference between adjacent maximum and minimum values ​​of the coil temperature while the power is on. Figure 6 is an example of a map showing the history of the difference in coil temperature change when the difference between adjacent maximum and minimum values ​​is used as the difference in coil temperature change.

[0025] The horizontal axis of the map in Figure 6 represents the minimum value of the coil temperature while the electric vehicle 100 is powered on, and the vertical axis of the map in Figure 6 represents the maximum value of the coil temperature while the electric vehicle 100 is powered on. The difference between adjacent maximum and minimum values ​​is defined as the difference in the temperature change of the coil, and the number of history cycles n is accumulated for each difference.

[0026] Figure 7 is a time chart illustrating the sampling of the difference ΔTe' when the difference between adjacent maximum and minimum values ​​of the coil temperature while the power is on is defined as the difference in the change in the coil temperature ΔTe'. At time t11 in Figure 7, when the power switch is turned from off to on, the coil temperature at that time is sampled as the minimum value Te'min1. Then, at time t12, when the coil temperature reaches the maximum value Te'max1, the history count n corresponding to this combination of adjacent minimum value Te'min1 and maximum value Te'max1 is incremented. In other words, the history count n corresponding to the difference ΔTe'1 between adjacent minimum value Te'min1 and maximum value Te'max1 is incremented.

[0027] Figure 8 is a flowchart illustrating the process of diagnosing the deterioration of the drive motor 108 (rotating electric machine) based on the history of the coil's temperature distribution and the history of the difference in the coil's temperature change. In step S21, the power controller 111 acquires the history of the coil's temperature distribution (see Figure 2), and in the next step S22, it acquires the history of the difference in the coil's temperature change (see Figure 3 or Figure 6).

[0028] Then, in step S23, the power controller 111 calculates an index value ΔTE from the history of the difference in the temperature change of the coil, which indicates the degree of coil temperature fluctuation, or in other words, the degree of deterioration due to wear of the coil insulating layer. For example, the power controller 111 multiplies the difference ΔTe in each grid in Figure 3 or Figure 6 by the number of history occurrences n, and takes the sum of the multiplied values ​​for all grids as the index value ΔTE. Here, the larger the index value ΔTE, the more times the coil has experienced large temperature fluctuations, and therefore it can be estimated that the degree of deterioration due to wear of the coil insulating layer is worsening.

[0029] Furthermore, as shown in Figure 9, the time Δt required for the coil temperature to change from a minimum to a maximum (or from a maximum to a minimum), that is, the time of change in the coil temperature, can be stored, and the sum of "difference ΔTe / Δt × number of history cycles" for each grid can be used as the index value ΔTE. Here, the shorter the time Δt, in other words, the faster the change in coil temperature, the larger the index value ΔTE calculated will be. This is because the faster the change in coil temperature, the faster the wear of the coil insulation layer is expected to progress, so the faster the change in coil temperature, the more it is judged that the degree of deterioration is worse.

[0030] Furthermore, by summarizing one trip from power-on to power-off, the average value of the difference ΔTe during one trip and the average number of history occurrences during one trip can be calculated, and the index value ΔTE can be defined as "average difference ΔTe × average number of history occurrences". In this case as well, the larger the index value ΔTE, the more it can be estimated that the degree of deterioration due to wear of the coil insulation layer is worse.

[0031] Furthermore, the average time Δt required for the coil temperature to change from a minimum to a maximum (or from a maximum to a minimum) over one trip can be calculated, and the index value ΔTE can be defined as "average difference ΔTe / average change time Δt × average number of history cycles". In this case as well, the faster the coil temperature changes, the larger the index value ΔTE calculated will be, and it will be judged that the degree of deterioration due to wear of the coil insulation layer is worse. Note that when summarizing the coil temperature history for each trip, the rate of deterioration (degree of deterioration progression) over one trip will be judged.

[0032] The power controller 111 calculates the index value ΔTE, and then, in step S24, calculates the degree of deterioration of the traction motor 108 (in other words, the lifespan of the traction motor 108) from the history of the coil temperature distribution (see Figure 2) and the index value ΔTE (in other words, the history of the difference in the change of the coil temperature). Here, the power controller 111 determines that the larger the sum of "coil temperature × cumulative usage time" obtained from the history of the coil temperature distribution, the worse the degree of deterioration (in other words, the shorter the lifespan of the traction motor 108), and further determines that the larger the index value ΔTE, the worse the degree of deterioration.

[0033] Next, the power controller 111 proceeds to step S25 and corrects the degree of deterioration of the traction motor 108, which was determined in step S24, based on the change time Δt of the coil temperature. This correction process for the degree of deterioration in step S25 is the process that would occur if the change time Δt was not used in the calculation of the index value ΔTE. In step S25, the power controller 111 determines that the shorter the change time Δt, the worse the degree of deterioration of the traction motor 108 is and the shorter its lifespan.

[0034] According to this deterioration diagnosis, the degree of deterioration of the traction motor 108 (rotating electric machine) can be determined with high accuracy by considering both the usage time at high temperatures and temperature changes (heat cycles). As a result, it becomes possible to appropriately decide whether to reuse the traction motor 108 (rotating electric machine) and to provide inspection guidance, thereby preventing malfunctions in the electric vehicle 100.

[0035] The technical ideas described in the above embodiments can be used in appropriate combinations, provided that no contradictions arise. Furthermore, although the content of the present invention has been specifically described with reference to preferred embodiments, it will be obvious to those skilled in the art that various modifications can be taken based on the basic technical ideas and teachings of the present invention.

[0036] For example, when the power controller 111 calculates the sum of "coil temperature × cumulative usage time," it can limit the calculation to coil temperatures above a threshold. Furthermore, the power controller 111 can convert the cumulative usage time at a given coil temperature into cumulative usage time at a reference temperature based on the difference between the reference temperature and the coil temperature, and then calculate the degree of deterioration of the drive motor 108 based on the sum of these converted cumulative usage times.

[0037] Furthermore, the power controller 111 corrects the sum of "coil temperature × cumulative usage time" or the cumulative usage time at the reference temperature to a larger value as the index value ΔTE increases, and can determine the degree of deterioration of the drive motor 108 based on the corrected value. In addition, the rotating electric machine that can be subject to deterioration diagnosis is not limited to the drive motor 108 (electric motor) of the electric vehicle 100, but for example, in a vehicle equipped with an internal combustion engine for power generation and driven by a motor, the generator driven by the internal combustion engine can be the subject of diagnosis.

[0038] Furthermore, in the above embodiment, the power controller 111 that drives and controls the travel motor 108 also functions as a deterioration diagnostic device, but a computer dedicated to deterioration diagnosis can be provided, or another control device can be equipped with the function of a deterioration diagnostic device. In addition, the deterioration diagnostic device can predict when replacement of the rotating electric machine will be necessary based on the progress of deterioration of the rotating electric machine (in other words, the rate of deterioration), and can provide the user with advance notice of the inspection time. Furthermore, the history of the coil temperature distribution and the history of the difference in coil temperature changes acquired by the deterioration diagnostic device can be transmitted to the cloud and stored.

[0039] 100...Electric vehicle, 108...Traction motor (rotating electric machine), 111...Power controller (degradation diagnostic device), 112...Temperature sensor (temperature detector)

Claims

1. A vehicle-mounted rotating electric machine deterioration diagnostic device for determining the degree of deterioration of a rotating electric machine, which is mounted on a vehicle having a rotating electric machine and a temperature detector for detecting the temperature of the coils of the rotating electric machine, the device acquires the temperature signal of the coils detected by the temperature detector, monitors and stores the history of the temperature distribution of the coils and the history of the difference in temperature changes of the coils, and determines the degree of deterioration of the rotating electric machine from the history of the temperature distribution and the history of the difference in temperature changes.

2. A vehicle-mounted rotating electric machine deterioration diagnostic device according to claim 1, wherein the difference in the temperature change of the coil is the difference between the temperature of the coil when the vehicle is powered on and the maximum value of the temperature of the coil while the vehicle is powered on.

3. A vehicle-mounted rotating electric machine deterioration diagnostic device according to claim 1, wherein the difference in the temperature change of the coil is the difference between adjacent maximum and minimum values ​​of the coil temperature while the vehicle is powered on.

4. A vehicle-mounted rotating electric machine deterioration diagnostic device according to claim 1, wherein the device determines that the degree of deterioration of the rotating electric machine is worse the larger the difference in temperature change and the greater the number of historical occurrences of the difference.

5. A vehicle-mounted rotating electric machine deterioration diagnostic device according to claim 1, wherein the shorter the time it takes for the temperature of the coil to change, the more it is determined that the degree of deterioration of the rotating electric machine is worse.

6. A method for diagnosing the deterioration of an on-board rotating electric machine, which is performed by a control device mounted on a vehicle having a rotating electric machine and a temperature detector for detecting the temperature of the coils of the rotating electric machine, the method comprising: acquiring a temperature signal of the coils detected by the temperature detector; monitoring and storing the history of the temperature distribution of the coils and the history of the difference in temperature changes of the coils; and determining the degree of deterioration of the rotating electric machine from the history of the temperature distribution and the history of the difference in temperature changes.

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