JP2015113075A - Control apparatus of hybrid vehicle - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a hybrid vehicle which includes an engine and a motor (a motor-generator and the like) and enables selection of a regenerative mode or a free-run mode when an accelerator is not stepped on, and in which the selection of the modes can be appropriately performed, and thus fuel consumption is reduced.SOLUTION: A control apparatus estimates a power consumption amount of a battery (a consumed SOC amount) consumed until reaching a regeneration-predicted section that is predicted for a hybrid vehicle to travel in a regenerative mode on the basis of at least map information (S101). When it is determined that the SOC of the battery is decreased to a lower-limit amount requiring forcible charging before the vehicle reaches the regeneration-predicted section (S103 → S104), the regenerative mode is selected. On the other hand, when it is determined that the vehicle reaches the regeneration-predicted section before the SOC is decreased to the lower-limit amount (S103 → S105), a free-run mode is selected.

Description

  The present invention relates to a control device for a hybrid vehicle including an engine and an electric motor as a driving power source.

  Conventionally, as a control of a hybrid vehicle, regenerative control is known in which an electric motor is operated as a generator during deceleration or the like to regenerate kinetic energy. Further, for example, in the hybrid vehicle described in Patent Document 1, when the accelerator foot release operation (accelerator off) is performed, it is possible to stop both the engine and the electric motor and to travel by inertia (free run). ing. Whether to perform regenerative control or free run is selected by a driver's operation.

  When the above-mentioned free run is performed, the vehicle can be driven with inertia without slowing down so much, so that fuel consumption can be reduced. In other words, as shown in FIG. 4 (a), if the engine is accelerated when the accelerator is on and the engine is free run when the accelerator is off, the engine is only operated for a short time at the end of the acceleration period. As shown in (d), the fuel consumption is very low until time t1.

JP 2007-069787 A

  By the way, as shown in FIG. 3 (a), in the case of general control in which acceleration is performed when the accelerator is on and regenerative control is performed during the deceleration period when the accelerator is off, the battery as shown in FIG. Charging and discharging are repeated alternately. For this reason, as shown in FIG. 4B, the remaining capacity (State Of Charge: SOC) of the battery decreases during the deceleration period, although it decreases during the acceleration period.

  On the other hand, if the free run is performed with the accelerator off as described above, the SOC of the battery does not recover. Therefore, as the accelerator is repeatedly turned on and off, the SOC gradually decreases as shown in FIG. go. When the SOC becomes lower than the lower limit SOCL at time t1, the engine is started to forcibly charge the battery, and the fuel consumption increases rapidly as shown in FIG.

  That is, when regenerative control or free run is selected by the driver's operation as in the conventional example, the SOC of the battery may be excessively lowered, and the engine must be started for forced charging. As a result, there is a risk that the fuel consumption will deteriorate. In view of these points, an object of the present invention is to further reduce the fuel consumption by enabling more appropriate selection between regenerative control and free run when the accelerator of a hybrid vehicle is off.

  In order to achieve the above object, a control apparatus for a hybrid vehicle according to the present invention includes an engine and an electric motor as driving power sources, and regenerates kinetic energy by operating the electric motor as a generator when the accelerator is off. The target is a regenerative mode and a free-run mode in which both the engine and the electric motor are stopped to travel by inertia.

  Based on at least the map information, the power consumption of the battery is estimated until reaching the regeneration prediction section where the hybrid vehicle is predicted to travel in the regeneration mode, and the remaining battery capacity is forced before reaching the regeneration prediction section. If it is determined that the battery will fall to the lower limit required for charging, the regeneration mode is selected. On the other hand, if it is determined that the remaining battery capacity reaches the regeneration prediction section before the remaining capacity of the battery falls to the lower limit, the free run mode is selected Configured to do.

  Due to the above-mentioned specific matters, while the hybrid vehicle is traveling, the power consumption of the battery until reaching the regeneration prediction section on the travel route is estimated, and the battery must be forcibly charged before reaching the regeneration prediction section. It is determined whether or not. If it is determined that forced charging is required, the regeneration mode is selected, and the battery is charged by regenerating kinetic energy when the accelerator is off. In this way, excessive reduction in the remaining battery capacity can be prevented and forced charging can be avoided.

  On the other hand, if it is determined that the forced charging is not required before the regeneration prediction section, the free-run mode is selected, and when the accelerator is off, the engine and the motor are stopped, and the hybrid vehicle is driven with inertia without much deceleration. I will let you. As a result, the distance traveled by inertia can be extended, and fuel consumption can be reduced.

  Here, as the regeneration prediction section, for example, there is a section with a high probability that a relatively large deceleration is required, such as a slope with a relatively steep slope, or a curve, an intersection, a road branch, or a merge. These can be predicted based on at least map information. The battery power consumption can also be estimated based on map information including road gradient and curve curvature.

  Preferably, the prediction of the regeneration prediction section and the estimation of the power consumption of the battery are performed in consideration of the learning result of the past traveling state on the route on which the hybrid vehicle travels. That is, if the driving state and power consumption when actually driving are stored in association with the map information (learning), the prediction of the regeneration prediction section and the power consumption are based on the map information and the learning result. The quantity can be estimated accurately.

  In this case, preferably, the travel route is divided into a plurality of sections having different modes of change in the travel speed of the hybrid vehicle (for example, speed patterns such as acceleration, steady state, and deceleration) based on the map information. Learning may be performed every time. Further, as a learning method, for example, an average value of power consumption may be stored and updated, or in addition to this, statistical processing such as weighted average may be performed.

  More preferably, learning is performed in consideration of the travel pattern of the hybrid vehicle on the travel route. For example, when the hybrid vehicle is traveling in the front of the train line and when following another vehicle, the usage of the engine and the electric motor is different, and the power consumption of the battery is also different. . This traveling pattern can be classified into, for example, a head start, a head steady travel, a head stop, a follow start, a follow steady travel, and a follow stop of the hybrid vehicle.

  Thus, based on the learning result considering the speed pattern and the running pattern of the hybrid vehicle, the power consumption of the battery in each of the plurality of divided sections up to the regeneration prediction section is estimated, and by adding these, the regeneration is performed. It is possible to accurately estimate the power consumption until reaching the prediction interval. And based on this estimation result, it becomes possible to perform regeneration control and free run more appropriately.

  According to the hybrid vehicle control device of the present invention, if it is determined that the battery must be forcibly charged before reaching the regeneration prediction section on the travel route, the regeneration mode is selected with the accelerator off, while the forced charging is required. If it is determined that it will not, the free-run mode can be selected to lengthen the free-run execution section while avoiding forced charging and extend the distance traveled by inertia, further reducing fuel consumption in hybrid vehicles. It is done.

It is a figure which shows the outline of the control apparatus of the hybrid vehicle which concerns on 1st Embodiment. It is explanatory drawing shown typically about the link which divided | segmented the target driving | running route. FIG. 4 is a timing chart showing changes in (a) vehicle speed, (b) SOC, (c) charge / discharge, and (d) fuel consumption when the drive mode and the regeneration mode are repeated. FIG. 4 is a diagram corresponding to FIG. 3 when the drive mode and the free-run mode are repeated. It is a flowchart figure which shows an example of the procedure of mode selection. FIG. 3 is a view corresponding to FIG. 1 according to a second embodiment. It is a schematic diagram which shows an example of a vehicle travel model. It is explanatory drawing about the learning for every driving | running | working pattern. It is explanatory drawing which shows the method of learning of average acceleration. It is explanatory drawing about the determination of a tracking stop or a tracking start. FIG. 3 is a view corresponding to FIG. 2 according to a second embodiment. FIG. 6 is a diagram corresponding to FIG. 5 according to a second embodiment.

Hereinafter, embodiments of the present invention will be described with reference to the drawings. Note that the description of the present embodiment is merely an example, and is not intended to limit the configuration or use of the present invention.
(First embodiment)
FIG. 1 is a block diagram showing an outline of a control apparatus for a hybrid vehicle according to the first embodiment. The hybrid vehicle 1 (see FIG. 2) includes a hybrid system 10 including an engine 11, a motor / generator 12 (electric motor), and a battery 13. The hybrid system 10 is configured to drive the wheels (drive wheels) of the hybrid vehicle 1 by selectively using two driving power sources, that is, an engine 11 and a motor / generator 12.

  The engine 11 is a gasoline engine as an example, and its output is controlled by an electronically controlled throttle or the like. The motor / generator 12 is driven by electric power supplied from the battery 13 or electric power supplied from a generator (not shown). That is, the hybrid system 10 is configured to be able to execute a drive mode in which wheels are driven by at least one of the engine 11 and the motor / generator 12.

  The hybrid system 10 is also configured to execute a regeneration mode in which the motor / generator 12 is operated as a generator when the hybrid vehicle 1 is decelerated and the kinetic energy is converted into electric energy. The battery 13 can be charged with the electric energy thus obtained. That is, the hybrid system 10 has a function of performing drive control and regenerative control of the hybrid vehicle 1 in response to a control signal from the vehicle control ECU 2 as will be described later.

  The vehicle control ECU 2 is a computer device that includes a central processing unit (CPU), a read only memory (ROM), a random access memory (RAM), and an input / output interface. The CPU reads and executes a control program stored in a ROM or the like, and the RAM is used as a working memory, a temporary storage memory, or the like. In addition to the hybrid system 10, a brake actuator 14 and the like are connected to the vehicle control ECU 2.

  The vehicle control ECU 2 is also connected with a GPS receiver 30, various sensors 31, a navigation system 32, and the like. The GPS receiver 30 is an information terminal of a satellite positioning system, and is used to acquire information on the traveling position of the hybrid vehicle 1. The various sensors 31 are for acquiring information related to the traveling state of the hybrid vehicle 1 and the surrounding traveling environment, and examples thereof include a vehicle speed sensor, an acceleration sensor, an accelerator operation amount sensor, and a brake operation amount sensor.

  Signals from these various sensors 31 are transmitted to the vehicle control ECU 2. As various sensors 31, an electromagnetic wave sensor, a millimeter wave radar sensor, a yaw rate sensor, or the like may be connected. In addition, an in-vehicle camera, an infrastructure communication device, and the like may be connected to acquire information related to the traveling environment around the hybrid vehicle 1.

  The navigation system 32 includes a map information DB (database) that stores predetermined map information, and a travel route to a destination input by the driver of the hybrid vehicle 1 (hereinafter referred to as a target travel route) is described above. Is calculated based on the map information and is guided using a display (not shown). Further, the navigation system 32 has a function of displaying the current position information of the hybrid vehicle 1 on a display or the like.

  Specifically, the map information includes information such as the road width, road length, gradient, and legal speed of the travel route. In addition, when there is a curve on the travel route, information on the radius, curvature, etc. is also included in the map information. As will be described in detail later, information (learned value) related to the past consumed SOC amount learned in the vehicle control ECU 2 is stored in the learned value DB of the vehicle control ECU 2 in association with the map information. This learning value may also be included in the map information and stored in the map information DB.

  The vehicle control ECU 2 according to the present embodiment reads and executes a control program stored in the ROM based on signals (information) transmitted from the GPS receiver 30, various sensors 31, the navigation system 32, and the like. Thus, the hybrid system 10 and the like are controlled. Thus, the vehicle control ECU 2 functions as a travel control unit 20, a link division / integration adjustment unit 21, a consumption SOC learning unit 22, and a consumption SOC estimation unit 23 described below.

  Specifically, first, the travel control unit 20 of the vehicle control ECU 2 basically uses an engine drive mode and a motor that drive wheels based on only the output of the engine 11 when the accelerator is on, based on a signal from an accelerator operation amount sensor. A motor drive mode in which the wheel is driven only by the output of the generator 12 and a composite drive mode in which the wheel is driven by the output of both the engine 11 and the motor generator 12 are selected.

  More specifically, on condition that the remaining capacity (state of charge: SOC) of the battery 13 is sufficient, for example, in the acceleration period of the accelerator on, first, the motor drive mode is set, and then the engine drive mode is set at the end of the acceleration period. To do. At this time, the engine 11 is operated in a high load state having the highest thermal efficiency, and the generator 13 is operated by the surplus output to charge the battery 13. Information regarding the SOC of the battery 13 is transmitted from the monitoring unit included in the hybrid system 10 to the vehicle control ECU 2.

  On the other hand, if the accelerator is off, the traveling control unit 20 basically sets the regeneration mode when the driver's deceleration request is large (greater than the set threshold), and sets the free-run mode when the deceleration request is normal or small. . In the free run mode, the engine 11 and the motor / generator 12 are both stopped, and the hybrid vehicle 1 is driven by inertia. However, if the SOC of the battery 13 is equal to or lower than the lower limit SOCL required for forcible charging, the traveling control unit 20 selects the regeneration mode even if the driver's deceleration request is not large.

  The lower limit amount SOCL is set to prevent damage to the battery cell due to deep discharge of the battery 13. For example, the lower limit amount SOCL may be set by adding an amount necessary for starting the engine 11 by operating the motor / generator 12 as a starter with respect to a predetermined amount (for example, 30%) set in advance.

  As described above, the hybrid system 10 is one of the engine drive, the motor drive, and the composite drive during an acceleration period including the start of the hybrid vehicle 1 (not including slow acceleration) or a relatively steep uphill slope. The drive mode is set. On the other hand, the regenerative mode is set in a deceleration period including a stop (not including slow deceleration) or a relatively steep downhill slope. Further, in steady running where the change in speed including a slow acceleration and a slow deceleration is a predetermined value or less, the hybrid system 10 is in one of the drive modes when the accelerator is on, while in the regenerative mode or the free run mode when the accelerator is off. It will be either.

  Then, in association with the change of the operation mode of the hybrid system 10 and the change of the traveling state of the hybrid vehicle 1, the target travel route is divided into a plurality of sections, or the divided sections are temporarily re-established. In order to combine (integrate), a link division / integration adjustment unit 21 is provided. That is, the link division / integration adjustment unit 21 includes all or a part of the target travel route calculated by the navigation system 32 as a plurality of sections (hereinafter referred to as links in FIG. 2). L1 to L5).

  The reason why the target travel route is divided in this manner is to learn or predict the consumed SOC of the battery 13 as described below. That is, first, the consumed SOC learning unit 22 stores the consumed SOC amount of the battery 13 when the hybrid vehicle 1 is actually traveling in the learned value DB, for example, at intervals of travel speed changes such as acceleration, steady state, and deceleration. Let The consumed SOC amount may be a positive value when the SOC decreases due to the discharge of the battery 13, and a negative value when the SOC increases (recovers) due to charging.

  As described above, the hybrid vehicle 1 is basically set to the drive mode during the acceleration period, and is set to the regenerative mode during the deceleration period, and the drive mode and the regenerative mode according to the accelerator operation and the state of the battery 13 in the steady running. Alternatively, the free run mode is set. Therefore, the learning accuracy of the consumed SOC amount can be increased by storing the consumed SOC amount for each section corresponding to the speed pattern such as acceleration, steady state, and deceleration as described above.

  However, the separation of changes in the traveling speed of the hybrid vehicle 1 that actually travels, that is, the separation of the speed pattern, is not necessarily the separation of the links L1 to L5 set based on the map information as shown in FIG. It does not match. Thus, the sections (learning sections) for each speed pattern in which the consumed SOC amount is learned as described above are combined (integrated) by the link division / integration adjustment unit 21 so as to be adapted to the links L1, L2,. Or, it can be further divided.

  In this way, the actual SOC consumption can be stored for each link L1, L2,... (Or for each learning section included in each link L1, L2,...). And if the data of consumed SOC amount are obtained twice or more, they are averaged and stored as a learned value. If new data on consumed SOC is obtained, this is also added and averaged, and the learning value is updated. Thus, the learned value of the consumed SOC amount learned during actual driving in the past is stored in the learned value DB of the consumed SOC learning unit 22.

  Thus, based on the learned value stored in the learned value DB, the consumed SOC estimation unit 23 estimates the consumed SOC amount of the battery 13 in the target travel route on which the hybrid vehicle 1 will travel from now on. That is, the consumption SOC estimation unit 23 first predicts a section (hereinafter referred to as a regeneration prediction section) in which the hybrid system 10 is in the regeneration mode on the target travel route while taking into account the learning value based on the map information. .

  For example, a section with a high probability that a relatively large deceleration is required, such as a relatively steep downhill, a curve, an intersection, a road branch, or a merge, is a regeneration prediction section. In addition, a regeneration prediction section is also obtained when the learning value of the consumed SOC amount in the section is a negative value having a large absolute value and the frequency of the actual regeneration mode during the past travel is considerably high.

  And the consumption SOC estimation part 23 estimates the consumption SOC amount of the battery 13 until it reaches | attains the regeneration prediction area estimated as mentioned above. Specifically, the consumption SOC amount is estimated by reading and adding all the learning values of the consumption SOC amount in the plurality of links L1, L2,... From the learning value DB to the regeneration prediction section.

-Free run prohibition / permission judgment-
Based on the amount of SOC consumed up to the regeneration prediction section estimated as described above, the traveling control unit 20 selects the regeneration mode and the free-run mode as follows. That is, the traveling control unit 20 of the present embodiment performs free run or regenerative control when the accelerator is off based on the current SOC of the battery 13 and the estimated SOC consumption as described above. In other words, it has a function of determining whether free run is prohibited or permitted.

  This point will be described in detail. First, as shown in FIG. 3A, when conventional control is performed in which acceleration is performed when the accelerator is on and regeneration is performed when the accelerator is decelerated, the same figure (c). Thus, since the charging and discharging of the battery 13 are alternately repeated, the SOC decreases during the acceleration period but recovers during the deceleration period as shown in FIG. Further, the engine 11 is operated only at the end of the acceleration period, and as a result, fuel is consumed as shown in FIG.

  On the other hand, when the free run is executed with the accelerator off, the distance traveled by inertia can be increased without slowing down the hybrid vehicle 1 much. That is, when an example is shown in FIG. 4 (a) where acceleration is accelerated when the accelerator is on and free-running is performed when the accelerator is off, the engine 11 is short at the end of the acceleration period as shown in FIG. 4 (d). It is only operated for time, and the fuel consumption until time t1 becomes very small.

  However, since the SOC of the battery 13 does not recover during the free run as shown in FIG. 5B, when the acceleration and the free run deceleration (slow deceleration) are alternately repeated as described above, the SOC gradually increases. It will go down. When the SOC becomes equal to or lower than the lower limit SOCL at time t1 in FIG. 5B, the engine 11 is started for forced charging of the battery 13, and the fuel consumption increases rapidly as shown in FIG. It will be.

  That is, as a result of executing the free run with the accelerator off to improve the fuel consumption, the SOC of the battery 13 is excessively lowered, and the engine 11 has to be started for forced charging. May get worse. From this point of view, in the present embodiment, as described above, the amount of SOC consumed up to the regeneration prediction section on the target travel route of the hybrid vehicle 1 is estimated, and the regeneration is appropriately performed so that the battery 13 is not forcibly charged. The mode and free run mode are selected.

  Hereinafter, the procedure of the free run prohibition / permission determination according to the first embodiment will be described in detail with reference to the flowchart of FIG. This process is repeatedly executed at a predetermined timing after, for example, the ignition is turned on or the start button provided in the hybrid vehicle 1 is turned on.

  In step S101 after the process shown in FIG. 5 is started, the consumed SOC amount is estimated as described above. That is, first, based on the map information or the like output from the navigation system 32, a regeneration prediction section is set on the target travel route by the consumption SOC estimation unit 23. The travel route to this regeneration prediction section is a section where regeneration control is not performed when the free-run mode is selected.

  And the learning value (learned value for each learning section) of the consumed SOC amount in each of the links L1, L2,... Included in the travel route up to the regeneration prediction section is stored in the consumption SOC learning section 22 by the consumption SOC estimation section 23. Read from the learning value DB. The learning value is a positive value when the SOC of the battery 13 decreases and is a negative value when the SOC increases (recovers). Therefore, if the learning values of the links L1, L2,. The consumed SOC amount of the battery 13 until reaching the prediction interval can be calculated.

  Based on the consumed SOC amount (estimated value) calculated by the consumed SOC estimating unit 23 and the current SOC of the battery 13, the SOC of the battery 13 when the regeneration prediction section is reached is calculated in step S102. Then, by comparing the calculated SOC with the lower limit SOCL required for forced charging, it is determined in step S103 whether forced charging is required.

  This determination is performed in the travel control unit 20 in this embodiment, and if the calculated SOC is equal to or less than the lower limit SOCL (affirmative determination: YES), free run is prohibited in step S104 (for example, a free run permission flag). To end the processing (end). On the other hand, if the calculated SOC is larger than the lower limit amount SOCL (negative determination: NO), free run is permitted (for example, a free run permission flag is turned on) in step S105, and the process is terminated (END). .

  As described above, according to the control device for the hybrid vehicle 1 according to the first embodiment, a relatively large downhill such as a case where there is a relatively steep downhill on the target travel route of the hybrid vehicle 1 is relatively large. If there is a section where regeneration control is predicted to be performed in response to the deceleration request, whether or not the SOC of the battery 13 is lowered to the lower limit SOCL before reaching this regeneration prediction section, and whether or not forced charging is required Determine.

  If it is determined that forced charging is required, the hybrid system 10 is set to the regenerative mode when the accelerator is off, and the battery 13 is charged by regenerating kinetic energy, thereby preventing an excessive decrease in the SOC. It is possible to avoid forced charging. Thereby, the deterioration of the fuel consumption resulting from forced charge can be prevented.

  On the other hand, even if the regeneration control is not performed, the regeneration prediction section is reached before the SOC of the battery 13 is excessively lowered. Therefore, if it is determined that the forced charging is not necessary, the hybrid system 10 is free run when the accelerator is off. The mode is set so that the hybrid vehicle 1 travels with inertia without slowing down too much. As a result, the distance traveled by the inertia of the hybrid vehicle 1 becomes longer, and fuel consumption is reduced.

  That is, the amount of SOC consumed up to the regeneration prediction section ahead of the target travel route is estimated, and the regeneration mode and the free-run mode are appropriately selected with the accelerator off so that the battery 13 is not forcibly charged. Thus, the fuel consumption in the hybrid vehicle 1 can be reduced.

  In particular, in the first embodiment described above, the battery 13 when the regeneration prediction section is reached based on the SOC consumption (learned value) during past driving stored (learned) corresponding to the map information. Therefore, it is possible to accurately determine whether or not forced charging is required based on the estimation result, and to appropriately select the operation mode of the hybrid system 10 as described above. it can.

(Second Embodiment)
Next, a second embodiment will be described. In the second embodiment, similarly to the first embodiment described above, the amount of SOC consumed up to the regeneration prediction section on the travel route of the hybrid vehicle 1 is estimated, and free run prohibition / permission is determined. However, when learning the amount of consumed SOC that is the basis of this estimation, changes in the running pattern of the hybrid vehicle 1 are also taken into account as described below.

  That is, in the first embodiment, taking into account that the operation mode of the hybrid system 10 changes depending on the speed pattern of the hybrid vehicle 1 such as acceleration and steady running, for example, for each section (learning section) corresponding to the speed pattern. In addition, the amount of consumed SOC is learned. However, not only such a speed pattern, but also the amount of consumed SOC differs depending on the traveling pattern such as when the hybrid vehicle 1 is traveling at the head of the train or following another vehicle. .

  Therefore, as shown in FIG. 6, in the second embodiment, the vehicle control ECU 2 includes a travel control unit 20, a link division / integration adjustment unit 21, a consumption SOC learning unit 22, and a consumption SOC estimation unit, as in the first embodiment. 23, and further includes a vehicle travel model 24 for simulating different travel patterns as described above. The consumption SOC learning unit 22 learns the consumption SOC amount for each traveling pattern including a change in speed using the vehicle traveling model 24. Except for this point, since the configuration of the second embodiment is the same as that of the first embodiment, the same components are denoted by the same reference numerals, and the description thereof is omitted.

  As schematically shown in FIG. 7 as an example, the vehicle travel model 24 distinguishes between when the hybrid vehicle 1 is traveling in the front of the vehicle train and when following the other vehicle. Considering three speed patterns of start, steady, and stop, six patterns of start start, start steady, start stop, follow start, follow steady, and follow stop are provided. It should be noted that the regular acceleration / deceleration is performed according to the curve of the road in the first steady state, and the irregular acceleration / deceleration is performed according to the distance from the preceding vehicle in the following steady state. Two traveling patterns of acceleration / deceleration and irregular acceleration / deceleration may be added.

  More specifically, for example, the hybrid vehicle 1 that has started at the head of the vehicle train may stop at the head after steady running (or regular acceleration / deceleration) at the head as it is. The started hybrid vehicle 1 may follow the other vehicle as it is and stop after it travels normally (or irregular acceleration / deceleration). On the other hand, the hybrid vehicle 1 that started the vehicle catches up with another vehicle, and thereafter, the vehicle may follow. On the other hand, the preceding vehicle of the hybrid vehicle 1 that has started following the vehicle moves to another travel route, and then the vehicle starts. Sometimes it becomes a run.

  When the traveling pattern of the hybrid vehicle 1 is different, the consumed SOC amount is likely to be different. Therefore, in this embodiment, in this embodiment, the vehicle traveling model 6 including the speed pattern as described above is used. The amount of consumed SOC is learned for each traveling pattern.

-Learning and estimating consumption SOC-
Next, learning of the consumed SOC amount will be described with reference to FIGS. As an example, in the upper part of FIG. 8, in the case where the hybrid vehicle 1 starts, decelerates after steady running, and then accelerates and resumes steady running, and then decelerates and stops, the change in the running speed is time-series. Shown in The solid line graph indicates the case where the hybrid vehicle 1 travels at the head, while the broken line graph indicates the case where the hybrid vehicle 1 travels following another vehicle.

  As shown by the solid line graph in the figure, in the case of the leading run, the acceleration and deceleration tend to be larger and the running speed tends to be higher than the follow running. In the case of the leading run, the start, steady, deceleration, acceleration, steady, and stop are performed as described above. However, in the follow-up running indicated by the broken line graph, the third deceleration and the fourth acceleration are both performed. It is slow acceleration and slow deceleration with a small speed change, and is regarded as steady running as shown in parentheses.

  In this way, the travel speed, acceleration, and the amount of change in SOC (consumption SOC amount) of the battery 13 are stored (learned) at the break where the travel speed pattern of the hybrid vehicle 1 changes, and the hybrid vehicle 1 travels at the head. Or the following traveling, and it is stored which one of the six traveling patterns of the start start, the head steady, the head stop, the follow start, the follow steady, and the follow stop described above. That is, learning is performed for each traveling pattern in consideration of the amount of SOC consumed depending on these traveling patterns.

  More specifically, as shown in the lower part of FIG. 8, for example, the upper and lower average accelerations, upper and lower average speeds, and travel patterns of each of the learning sections δ1 to δ6 (see FIG. 11) are shown. Stores the number of executions. Here, the upper side and the lower side of the average acceleration and average speed are the accelerations (or speeds, and so on) detected in the first and second times in a certain learning section as schematically shown in FIG. The lower one is the lower initial value and the higher one is the upper initial value. The acceleration detected after the third time is averaged and updated with the closer one of the upper and lower average accelerations.

  In addition, it is determined which of the six patterns is the running pattern, and the number of times it is actually performed is stored. For example, in the learning section δ1 at the left end of FIG. 8, the hybrid vehicle 1 often starts as shown in the upper part of the figure, and the start start pattern is 3 times and the follow-up start pattern is 6 as shown in the lower part. It has become times. In addition, the head stationary pattern is twice, and all other patterns are once.

  More specifically, how to determine such a running pattern is, for example, the head start if the acceleration detected in a certain learning section is close to the past upper average acceleration, and the follow start if it is close to the past lower average acceleration. It can be determined that In the case of a start, it can be determined that the start is the start if it is determined that the start is stopped in the previous learning section, and the start is the follow if it is determined that the follow is stopped. Further, as schematically shown in FIG. 10, when the vehicle is stopped before the intersection, it is determined that the start is started when the distance (Xm) to the intersection or the traveling time is short, and the start is followed when the distance is long. May be.

  With respect to the steady running, it can be determined that the head is steady if the speed detected in a certain learning section is close to the past upper average speed, and is the tracking steady if the speed is close to the past lower average speed. It is also possible to determine that the vehicle is in a steady state if the number of times the brake is stepped is greater than a threshold value even if the road is a straight road and a flat road or a gentle uphill, and if it is less, it is determined that the head is steady. Further, the influence of the gradient or the like is removed from the acceleration or deceleration, and the magnitude of the air resistance received by the hybrid vehicle 1 is estimated. If this is smaller than the threshold value, it may be determined that the tracking is steady.

  Furthermore, when the hybrid vehicle 1 is stopped, when the vehicle stops before an intersection as shown in FIG. 10, the distance (Xm) to the intersection is short and no vehicle is present ahead. If it is, it is determined that the vehicle is at the head stop. If the distance to the intersection is long and it is considered that a vehicle is present ahead, it can be determined that the vehicle is following the stop.

  For each running pattern determined in this way, the SOC value is stored in the learning value DB. Then, the consumption SOC estimation unit 23 sets the consumption SOC amount in each of the links L1, L2,... Included in the target travel route of the hybrid vehicle 1 up to the regeneration prediction section to the learned value of the travel pattern with the highest probability. Calculate based on That is, as shown in FIG. 11 as an example, the first link L1 includes three learning sections δ1 to δ3, and the traveling pattern having the highest number of executions in the first learning section δ1 Acceleration (6 times).

  Therefore, for this first learning section δ1, the learning value ΔS1 of the consumed SOC amount in the case of follow-up acceleration is read from the learning value DB. Similarly, for the second learning section δ2, the learning value ΔS2 of the consumed SOC amount in the case of steady following is read. Further, the third learning section δ3 is slow deceleration, and the learning value ΔS3 of the consumed SOC amount in that case is read as it is regarded as a steady following state as shown in parentheses in the upper part of FIG.

  Subsequently, the second link L2 includes two learning sections δ1 and δ2, and the first learning section δ1 has the highest probability of slow acceleration in tracking, that is, tracking steady, in that case. The learning value ΔS4 of the consumed SOC amount is read, and similarly, the learning value ΔS5 in the follow-up steady state is also read for the second learning section δ2. The third link L3 includes one learning section δ6. Here, the probability of follow-up stop is the highest, and it is predicted that the regeneration mode is set, so this is a regeneration prediction section.

  Then, the amount of SOC consumed up to the regeneration prediction section (third link L3) is obtained by adding the learning values ΔS1 to ΔS3 of the first link L1 and the learning values ΔS4 and ΔS5 of the second link L2, ΣΔSi (i = 1 to 5). In other words, the amount of SOC consumed up to the regeneration prediction section is estimated by adding the learning values of the five learning sections δ1 to δ5 included in the two links L1 and L2, respectively.

-Free run prohibition / permission judgment-
FIG. 12 shows the procedure for determining whether to prohibit or permit free run in the second embodiment. In step S201 after the start, the consumption SOC estimation unit 23 performs the consumption SOC up to the regeneration prediction section of the hybrid vehicle 1 as described above. The amount is estimated based on the learned value of the driving pattern having the highest probability stored in the learned value DB.

  That is, for each of the plurality of links L1, L2 included in the travel route up to the regeneration prediction section, among the learning values stored for each learning section δ1,. The amount of SOC consumed by the battery 13 until it reaches the regeneration prediction section is calculated by reading them out and adding them together.

  Based on the consumed SOC amount calculated by the consumed SOC estimation unit 23 and the current SOC of the battery 13, the regeneration prediction section is reached in step S202 as in step 102 of the flow of the first embodiment shown in FIG. The SOC of the battery 13 at this time is calculated, and in step S203, it is determined whether or not forced charging is required as in step 103.

  If the determination in step S203 is affirmative (YES), the process proceeds to step S204, the free run is prohibited as in step S104 of the flow of the first embodiment, and the process ends (end). On the other hand, if a negative determination (NO) is made in step S203, the process proceeds to step S205, in which free run is permitted in the same manner as in step S105, and the process ends (end).

  Therefore, according to the second embodiment, the consumed SOC amount of the battery 13 up to the regeneration prediction section is estimated with higher accuracy based on the learning result that also considers the traveling pattern of the hybrid vehicle 1 as described above. It becomes possible to determine whether or not forced charging is performed with higher accuracy. For this reason, compared with the first embodiment, it becomes possible to more appropriately select the regenerative control and the free run, and the fuel consumption in the hybrid vehicle 1 can be further reduced.

(Other embodiments)
As described above, in the first and second embodiments, the amount of SOC consumed (learned value) learned during actual past driving of the hybrid vehicle 1 is added to the map information to obtain the regeneration prediction section. Although the consumed SOC amount is estimated, when this learning value does not exist, free run may be prohibited. This is to more reliably avoid the forced charging.

  For the same reason, if the learning value exists but has not yet converged, for example, in the second embodiment, there are two or more peaks in the number of executions of the past driving pattern, and the driving pattern with the highest probability. May be adopted, the learning value with the larger consumed SOC amount may be adopted, and based on this, the consumed SOC amount up to the regeneration prediction period may be estimated. In such a case, free run may be prohibited.

  In the first embodiment, the consumption SOC learning unit 22 of the vehicle control ECU 2 learns the consumption SOC amount in the past travel for each speed pattern. In the second embodiment, Although learning is performed for each traveling pattern including a change in speed, the present invention is not limited to this. In order to reduce the storage capacity of the memory of the vehicle control ECU 2, for example, it is only necessary to store information on whether the vehicle is heading or following and the amount of SOC consumed.

  On the contrary, if the memory capacity of the memory is sufficient, information on the time zone in which the hybrid vehicle 1 has traveled in the past, the weather at that time, the season, etc. may also be added to store the SOC consumption, or the hybrid When the driver of the vehicle 1 is different, the consumed SOC amount may be stored for each driver.

  Furthermore, in each of the above embodiments, either the free run is permitted when the accelerator of the hybrid vehicle 1 is off, or the prohibition is prohibited and the regeneration control is performed, but the present invention is not limited to this. . For example, weak regeneration control in which the degree of regeneration of kinetic energy is weaker than normal regeneration control can be performed, and either weak regeneration control or normal regeneration control may be selected. Furthermore, either one of permitting free run, prohibiting the free run, and performing the weak regeneration control or performing the normal regeneration control may be selected.

  INDUSTRIAL APPLICABILITY The present invention is highly effective when applied to an automobile or the like because a hybrid vehicle can perform regeneration control and free run selection more appropriately than before when the accelerator is off and can reduce fuel consumption.

DESCRIPTION OF SYMBOLS 1 Hybrid vehicle 2 Vehicle control ECU (control apparatus)
10 Hybrid system 11 Engine 12 Motor / generator (electric motor)
13 Battery SOC Battery remaining capacity SOCL Lower limit

Claims (1)

An engine and an electric motor are provided as driving power sources, and when the accelerator is off, the electric motor operates as a generator to regenerate kinetic energy, and the engine and the electric motor are both stopped to run inertia. A hybrid vehicle control device capable of selecting a free-run mode,
Based on at least the map information, the power consumption of the battery until the hybrid vehicle reaches the regeneration prediction section predicted to travel in the regeneration mode is estimated, and the remaining capacity of the battery is forcedly charged before reaching the regeneration prediction section. If it is determined that the required lower limit amount is reached, the regeneration mode is selected. On the other hand, if the remaining capacity reaches the regeneration prediction section before the remaining capacity is reduced to the lower limit amount, the free run mode is selected. A control apparatus for a hybrid vehicle configured as described above.

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