JP4200863B2 - Traveling speed pattern estimation device and hybrid vehicle drive control device - Google Patents

Description

  The present invention relates to a travel speed pattern estimation device and a hybrid vehicle drive control device, for example, estimation of a travel speed pattern for a frequently traveled route, and setting of an engine and motor operation schedule based on the estimated travel speed pattern About.

Hybrid vehicles using a combination of an engine such as an internal combustion engine and a motor such as an AC motor or a brushless DC motor rotated by electric power supplied from a power storage means such as a battery (secondary battery such as a storage battery) as a power source of the vehicle are widely used. It has been put into practical use.
Patent Document 1 proposes a technique for reducing engine combustion consumption in this hybrid vehicle.

JP 2000-333305 A

  In the technique described in Patent Document 1, a route to a destination is divided into a plurality of sections at points where start and stop are predicted, road data and a travel history are acquired from a navigation device, and travel for each section is performed. Based on the estimated traveling speed pattern and the fuel consumption characteristics of the engine, the engine and motor operation schedules are set so that the fuel consumption to the destination is minimized. .

In the technique described in Patent Document 1, if the start and stop prediction points for dividing a travel route are intersections, junctions, etc., the division points are clear and can be easily determined in connection with road information data. .
However, if the travel route is divided at the predicted start and stop points, there are the following problems from the viewpoint of travel prediction.
(1) Starting and stopping predicted points such as intersections are restraints and restrictive elements from the viewpoint of vehicle driving control, and are points where the driving state varies variously.
That is, an intersection or the like is a point where the vehicle passes as it is when it is a blue light, stops before it is red, and passes during the start. In addition, even if the vehicle starts, if there is a crossing pedestrian when turning left or right, the vehicle may stop and then restart. If there is a traffic jam, the stop position may leave the intersection. Furthermore, there are cases where the vehicle stops several times before the intersection and passes after starting.
(2) When predicting the traveling speed pattern, it is necessary to analyze the traveling data by dividing each section, but the traveling state at both ends of each section varies variously, and the analysis processing becomes complicated. For example, since passing, stopping, and starting are considered for each of the start point and end point of the divided section, it is necessary to consider the combination.
(3) Further, in the case of predicting traveling assuming that the analysis / classification has been completed, it is necessary to associate the predictions with the adjacent sections before and after, and it is not possible to analyze each section independently. For this reason, if a plurality of sections are considered in the travel route, the number of combinations becomes enormous.

Therefore, a first object of the present invention is to provide a travel speed pattern estimation device capable of easily estimating a travel speed pattern of a travel route.
It is a second object of the present invention to provide a drive control device for a hybrid vehicle that can easily set the operation schedule of the engine and the motor.

In the first aspect of the present invention, a travel information storage means for storing driving data for the travel route, the travel route specifying means for specifying a run line path, on the basis of the traveling data, running stability stable running is assumed Section dividing means for dividing the identified travel route into small sections at a point; travel speed pattern candidate generating means for generating a traveling speed pattern candidate for each of the divided small sections based on the travel data; for each small section, by extracting the running pattern candidates 1 from the running speed pattern candidates said generating comprises the estimated traveling speed pattern outputting means for outputting an estimated traveling speed pattern of the travel route which is the specified running now, the The section dividing means includes: “a point in a section where a predetermined vehicle speed or more continues for a predetermined time or more”, “a point in a section where a predetermined vehicle speed or more continues for a predetermined distance”, Travel where the speed fluctuation range is within a predetermined range and where the speed is stable for a predetermined period of time or a "point within a section where the speed fluctuation range is within the predetermined range and continues for a predetermined distance or more" There is provided a travel speed pattern estimation device that divides the identified travel route into small sections as stable points .
According to a second aspect of the present invention, in the travel speed pattern estimating device according to the first aspect, the travel route specifying means specifies a route on which the vehicle frequently travels as a frequent route based on the travel data. provide.
According to a third aspect of the present invention, the travel information storage means stores travel data and travel environment data for a travel route in association with each other, and the estimated travel speed pattern output means matches the current travel environment data. The travel speed pattern according to claim 1 or 2, wherein a travel speed pattern candidate is extracted for each of the small sections, and an estimated travel speed pattern of the identified travel route to be traveled is output. An estimation device is provided.
According to a fourth aspect of the present invention, an engine in which part or all of the driving force is used for driving or generating electric power of the vehicle and a motor for generating the driving force of the vehicle are provided, and the driving force of at least one of the engine and motor A power storage means for supplying electric power to the motor charged by power generation and regeneration by driving the engine, a power storage amount detection means for detecting a power storage amount of the power storage means, The travel speed pattern estimation device according to any one of claims 1 to 3 , the estimated travel speed pattern of the specified route output by the travel speed pattern estimation device, and the power storage of the power storage means Operation for setting the operation schedule of the engine and the motor based on the amount so that the fuel consumption in the specified route is minimized. To provide a drive control apparatus for a hybrid vehicle, characterized by comprising: a schedule setting means.

According to the first to third aspects of the present invention, the vehicle is not divided at the intersection where the traveling state is indefinite, and the traveling stable point where the traveling stability is expected , that is, “a predetermined vehicle speed or more continues for a predetermined time or more. ”Points in a section where the vehicle speed exceeds a predetermined distance” or “Points in a section where the speed fluctuation range is within a predetermined range and continues for a predetermined time” or “Speed fluctuation range is Since the identified travel route is divided at a “point in a section that continues for a predetermined distance or more within a predetermined range” , the travel speed pattern of the travel route can be easily estimated.
Further, according to the present invention described in claim 4 , since the estimated traveling speed pattern for the identified traveling route divided at the traveling stable point where the traveling stability is expected is used, the operation schedule of the engine and the motor is easy. Can be set to

DESCRIPTION OF EMBODIMENTS Hereinafter, preferred embodiments of a travel speed pattern estimation apparatus and a hybrid vehicle drive control apparatus according to the present invention will be described in detail with reference to FIGS. 1 to 17.
(1) Outline of Embodiment In this embodiment, travel data and travel environment data for a travel route are stored in association with each other, and a route that has traveled a predetermined number of times or more is specified as a frequent route. A “frequent route” is a route that is often used by the driver, such as a commuting route. Thus, by automatically identifying the “frequent route” from past driving data, the user can perform any operation. “Frequently used routes”, that is, routes that are frequently used are identified, and the motor and engine operation schedules are set according to the characteristics of the identified routes, as described below, to achieve driving with high fuel efficiency. The
The identified frequent route identifies a point where the traveling is assumed to be stable from the traveling data, divides it into a plurality of small sections, and creates a traveling speed pattern candidate for each small section. Then, by extracting a traveling speed pattern candidate that matches the current traveling environment data for each small section, an estimated traveling speed pattern of a frequent route to be traveled is output.
Here, “running stability” means that a predetermined vehicle speed or more continues for a predetermined time or a predetermined distance, or a speed fluctuation range continues for a predetermined time or a predetermined distance within a predetermined range.
In the present embodiment, an intermediate point between intersections including a section in which traveling at a predetermined vehicle speed or higher continues for a predetermined time or longer is adopted as a point where the driving stability is assumed, and the frequent route is divided at the driving stable point.
Thus, since it divides | segments in the driving | running | working stable point, without dividing | segmenting in the intersection where driving state is indefinite, the driving state of the both ends of each divided | segmented small area is stabilized. As a result, the conditions at both ends of the small section can be considered, and there is no need to consider the influence of the traveling state of the preceding and following sections, and only each small section can be analyzed independently.
A change in the driving state in the vicinity of the intermediate intersection in the small section can be grasped as a pattern, and classification becomes easy.
In addition, it is possible to independently analyze and predict independently in a plurality of continuous sections along the travel route, and connect the results without restriction.

(2) Details of Embodiment FIG. 1 is a conceptual diagram showing a configuration of a drive control system for a hybrid vehicle to which a travel speed pattern estimation device and a drive control device for a hybrid vehicle according to an embodiment of the present invention are applied.
In FIG. 1, 10 is a drive control system for a hybrid vehicle as a travel speed pattern estimation device in the present embodiment, and 20 is a drive device. Here, reference numeral 21 denotes an engine such as an internal combustion engine driven by fuel such as gasoline or light oil, which includes an engine control device such as an ECU (not shown) and is used as a power source for vehicles such as passenger cars, buses and trucks. . The driving force of the engine 21 is transmitted to a driving force transmission device 25 including a transmission (multi-stage transmission or continuously variable transmission) (not shown), a driving shaft, driving wheels, and the like, and the vehicle is rotated by rotating the driving wheels. Driven. The driving force transmission device 25 may be provided with a braking device such as a drum brake or a disc brake.
In the hybrid vehicle in the present embodiment, a part of the driving force of the engine 21 may be output for driving, and the remaining driving force may be used for driving the generator 22 to generate power. As a configuration for that purpose, for example, a planetary gear is used and the shafts of the engine 21, the generator 22, and the motor 24 are connected.

Here, the vehicle is a hybrid vehicle, has a motor 24 (AC motor, DC brushless motor, etc.) that is rotated by electric power, and uses the engine 21 and the motor 24 together as a power source for the vehicle. The motor 24 generates a driving force by the electric power supplied from the battery 23 as the power storage means, and the driving force is transmitted to the driving wheels of the driving force transmission device 25.
In addition, a generator 22 such as an AC generator is connected to the driving force transmission device 25 so as to generate a regenerative current when the vehicle is decelerated. The regenerative current generated by the generator 22 is supplied to the battery 23, and the battery 23 is charged. In addition, a current can be generated in the generator 22 by the driving force of the engine 21. The motor 24 is preferably an AC motor, and in this case, includes an inverter (not shown). Similarly, the generator 22 is preferably an AC generator, and in this case, includes an inverter (not shown). Further, the battery 23 includes a capacity detection sensor (not shown) for detecting SOC (State Of Charge) that is the amount of stored electricity. This capacity detection sensor functions as a storage amount detection means.

The motor 24 may be configured integrally with the generator 22. In this case, the motor 24 generates a driving force when electric power is supplied from the battery 23 and functions as a power source. When the motor 24 is rotated by the driving force transmission device 25 such as during braking of the vehicle, the regenerative current is generated. It functions as the generator 22 which generate | occur | produces.
Further, the generator 22 may generate power using the driving force of the engine 21, and the motor 24 may generate regenerative power during the deceleration operation of the vehicle.

  The battery 23 is a secondary battery as a power storage means that can be repeatedly charged and discharged, and is generally a lead storage battery, a nickel cadmium battery, a nickel metal hydride battery, or the like, but is a high performance lead used in an electric vehicle or the like. A storage battery, a lithium ion battery, a sodium sulfur battery, etc. may be sufficient. Note that the power storage means does not necessarily have to be the battery 23, and electrically stores and discharges energy, such as a capacitor (capacitor) such as an electric double layer capacitor, a flywheel, a superconducting coil, a pressure accumulator, or the like. Any form may be used as long as it has a function. Furthermore, any of these may be used alone, or a plurality of them may be used in combination. For example, the battery 23 and the electric double layer capacitor can be combined and used as a power storage means.

  Reference numeral 26 denotes a main control device, which is a kind of computer including a calculation means (not shown) such as a CPU and an MPU, a storage means such as a semiconductor memory and a magnetic disk, a communication interface, and the like. And based on the signal from the various sensors of the driving | running | working data acquisition part 13, operation | movement of the engine 21, the engine control apparatus, the motor 24, the generator 22, and an inverter is controlled. Here, the sensor is an accelerator sensor, a brake sensor, or the like, and detects information related to the operation of the driver of the vehicle and transmits it to the main control device 26.

  The main control device 26 normally controls the usage ratio of the engine 21 and the motor 24 according to the traveling state of the vehicle and the storage state of the battery. For example, when the engine efficiency is low, such as when starting or running at a low speed, the motor travels. When the storage state of the battery is low, power may be generated in an efficient engine state and stored in the battery. When decelerating or downhill, regenerative power is stored in the battery. In the present embodiment, the estimated traveling speed pattern is determined by dividing the frequent route into small sections based on the traveling stable point, and the fuel consumption of the engine 21 is determined for each small section based on the determined estimated traveling speed pattern. Set the minimum driving schedule. When traveling on a frequent route, the engine 21 and the motor 24 are driven according to the set operation schedule.

In FIG. 1, 12 is a navigation database in which navigation information is stored as data used for navigation processing in a normal navigation device such as map data, road data, and search data, and 14 is time, date and time, and traffic congestion. It is a driving environment data acquisition part which acquires the data regarding the driving environment of vehicles, such as information and weather information. The travel data acquisition unit 13 includes various sensors, and acquires data related to the vehicle travel state such as the vehicle speed, the brake operation state, and the accelerator opening.
The travel pattern prediction unit 11 is a kind of computer that includes a calculation unit such as a CPU and MPU (not shown), a semiconductor memory (ROM, RAM, etc.), a storage unit such as a magnetic disk, a communication interface, and the like. Data is acquired from the travel data acquisition unit 13 and the travel environment data acquisition unit 14, and navigation processing such as display of the current position of the vehicle and route search to the destination is executed.
The travel pattern prediction unit 11 also executes a travel speed pattern estimation process that estimates a travel speed pattern that reflects the driving characteristics of the driver, including a division point setting process based on a stable travel point and a frequent route segmentation process. .
The travel pattern predicting unit 11 includes an input unit (not shown) including an operation key, a push button, a jog dial, a cross key, a remote controller, a CRT display, a liquid crystal display, an LED (Light Emitting Diode) display, a plasma display, and a windshield. Display unit including hologram device for projecting hologram, voice input unit including microphone, voice synthesizer, voice output unit including speaker, FM transmitter, telephone line network, Internet, mobile phone network, etc. It is desirable to have a communication unit that transmits / receives various data to / from.

  The navigation database 12 includes a database composed of various data files. In addition to search data for searching for a route, the navigation database 12 displays a guide map along the searched route on the screen of the display unit, Map data, facilities to display characteristic photographs, frame maps, etc. on the route, the distance to the next intersection, the direction of travel at the next intersection, etc., and other guidance information Records various data such as data. The navigation database 12 also records various data for outputting predetermined information by the audio output unit.

  Here, the search data includes intersection data, road data, traffic regulation data, and route display data. In the intersection data, in addition to the number of intersections in which the data is stored, data relating to each intersection is assigned with a number for identification as intersection data. Further, each intersection data is stored with a number for identifying each connection road in addition to the number of roads connected to the corresponding intersection, that is, the number of connection roads. The intersection data may include the type of the intersection, that is, the distinction between the intersection where the traffic signal lamp is installed or the intersection where the traffic signal lamp is not installed.

  In addition to the number of roads in which the data is stored, the road data is stored with data relating to each road assigned with a number for identification as road data. Each road data stores a road type, a distance as a length of each road, a construction time as a time required for traveling on each road, and the like. Furthermore, the road type includes administrative road attributes such as national roads, prefectural roads, main local roads, general roads, and highways.

  In the road data, the width, gradient, cant, altitude, bank, road surface condition, whether there is a median, the number of road lanes, the point where the number of lanes decreases, the width of the road data is narrow. It is desirable to include data such as points. In the case of an expressway or a main road, each of the lanes in the opposite direction is stored as separate road data and processed as a double road. For example, in the case of a trunk road with two or more lanes on one side, it is processed as a two-way road, and the upward lane and the downward lane are stored in the road data as independent roads. Further, for corners, it is desirable to include data such as radii of curvature, intersections, T-junctions, and corner entrances. Road attributes such as railroad crossings, expressway entrance / exit rampways, expressway toll gates, downhill roads, and uphill roads may also be included.

  Data stored in the navigation database 12 is stored in storage means such as a semiconductor memory or a magnetic disk. The storage means includes all forms of storage media such as magnetic tape, magnetic disk, magnetic drum, flash memory, CD-ROM, MD, DVD-ROM, optical disk, MO, IC card, optical card, memory card, etc. It is also possible to use a removable external storage medium.

  The travel data acquisition unit 13 includes a GPS sensor that receives GPS information from GPS (Global Positioning system) hygiene, an azimuth sensor that detects the azimuth of the vehicle, an accelerator opening sensor that detects the accelerator opening, and driving Brake switch for detecting the movement of the brake pedal operated by the driver, steering sensor for detecting the steering angle of the steering operated by the driver, the winker sensor for detecting the movement of the winker switch operated by the driver, and the shift operated by the driver A shift lever sensor that detects the movement of the machine's shift lever, a vehicle speed sensor that detects the vehicle speed, that is, a vehicle speed sensor, an acceleration sensor that detects the vehicle acceleration, and a yaw rate that indicates a change in the orientation of the vehicle. It has a yaw rate sensor. The travel data includes the current position of the vehicle, the accelerator opening, the movement of the brake pedal operated by the driver, the steering angle of the steering operated by the driver, the movement of the blinker switch operated by the driver, and the driver operating It includes the movement of the shift lever of the transmission, the vehicle speed, the acceleration of the vehicle, the yaw rate indicating the change in the direction the vehicle is facing, and the like.

  Here, the travel data acquisition unit 13 acquires travel data such as the current position and travel speed of the vehicle at predetermined intervals from when the vehicle starts at the departure point to when it stops at the destination point. That is, travel data at a predetermined time interval (for example, every predetermined time such as 100 [msec], 1 [sec]) or a predetermined distance interval (for example, every predetermined distance such as 100 [m], 500 [m]). To get. Or although it is an indefinite interval, you may acquire driving | running | working data corresponding to the point which can specify positions, such as every node point (delimitation point on the road database) of a road, or an intersection. In this way, by obtaining travel data at predetermined intervals, it is possible to obtain a trajectory of the travel of the vehicle and a change in travel speed at that time, that is, a travel speed pattern. Can be used to generate and estimate

  The travel environment data acquisition unit 14 includes a clock, a calendar, and the like, and acquires and stores date and time information such as the current time, date, day of the week, and date and time the vehicle departs. In addition, the travel environment data acquisition unit 14 collects information on traffic control systems such as the police and the Japan Highway Public Corporation in a road traffic information communication system called VICS (R) (Vehicle Information & Communication System), for example. Road traffic information such as information on traffic jams, traffic regulation information, construction information on road construction, etc. is acquired and stored. Furthermore, the driving environment data acquisition unit 14 specifies event information such as scheduled locations and scheduled dates of events such as festivals, parades, fireworks displays, etc., for example, roads around stations and large commercial facilities every day except weekends. It is desirable to acquire and store statistical traffic information such as traffic jams occurring at the time of day, traffic jams occurring during summer holidays, and weather information such as weather forecasts created by the Japan Meteorological Agency. . The travel environment information as information on the environment in which the vehicle travels acquired and stored by the travel environment data acquisition unit 14 includes the current time, date, day of the week, date and time of departure of the vehicle, weather, traffic congestion information, and traffic regulation information. , Road construction information, event information, etc.

  The travel environment data acquisition unit 14 also acquires the date and time, day of the week, wiper, headlight, air conditioner, operation status of vehicle-mounted devices such as a defroster, and sensing data of vehicle-mounted sensors such as a raindrop sensor and a temperature sensor. To do. The operation status data and sensing data of the on-vehicle equipment can be used by the travel pattern prediction unit 11 to estimate the weather at that time.

The travel data storage unit 15 stores travel data acquired by the travel data acquisition unit 13 and travel environment data acquired by the travel environment data acquisition unit 14. In this case, the travel data and the travel environment data in one travel of the vehicle are stored in association with each other.
The traveling speed pattern candidate machine part 16 stores a traveling speed pattern generated from past traveling data described later.

  Further, the division point storage unit 17 stores the data of each division point set in the division point setting process for each travel route. This division point data is stored for each travel route specifying data for specifying a travel route (a data string composed of departure point, intersection existing on the travel route, and each point data specifying the arrival point).

  In the hybrid vehicle drive control system 10 according to the present embodiment, when the travel pattern prediction unit 11 executes the travel speed pattern estimation process to estimate the travel speed pattern, the main control device 26 is based on the travel speed pattern. A schedule of the usage ratio between the engine 21 and the motor 24 is set, and the operating states of the engine 21 and the motor 24 and the SOC of the battery 23 are controlled according to the schedule.

And the drive control system 10 of the hybrid vehicle as the travel speed pattern estimation device has a travel information storage means, a frequent route identification means, a section division means, a travel speed pattern candidate generation means, and an estimated travel from the functional viewpoint. It has a speed pattern output means and an operation schedule setting means.
In the present embodiment, the travel data storage unit 15 functions as a travel information storage unit, and stores travel data for the travel route. The travel data storage unit 15 stores travel environment data in association with travel data.
The travel pattern prediction unit 11 functions as a frequent route identification unit, a section division unit, a travel speed pattern candidate generation unit, and an estimated travel speed pattern output unit.
When the traveling pattern predicting unit 11 functions as a frequent route specifying unit, the traveling pattern predicting unit 11 identifies a frequent route based on the traveling data, and when functioning as a section dividing unit, the traveling stable point where traveling stability is assumed based on the traveling data. The frequent route is divided into small sections. Further, when functioning as a travel speed pattern candidate generation means, a travel speed pattern candidate is generated based on travel data, and when functioning as an estimated travel speed pattern output means, a travel speed pattern candidate that matches the current travel environment data is selected. Extract and output an estimated travel speed pattern of the route to be traveled.
The main control unit 26 functions as an operation schedule setting unit, and the engine and motor are operated so that the fuel consumption in the frequent route is minimized based on the estimated traveling speed pattern of the frequent route and the amount of power stored in the power storage unit. Set the schedule.

A dividing process for dividing a route from a predetermined departure place to a destination into small sections based on stable running points by the running speed pattern estimation device of the present embodiment configured as described above will be described.
FIG. 2 illustrates a travel route for explaining the division process.
Note that the travel route shown in FIG. 2 is a simplified representation of the travel state on a certain day, assuming a situation that a typical commuter travels from home to work.
As shown in FIG. 2, the home of the departure point is P0, the intersections existing on the travel route are P1 to P7, and the office of the arrival point (destination) is P8.
It is assumed that the following traveling has been performed.
(A) Between P0 and P3 Depart from home P0, run through a residential area, and exit to the main road at intersection P3. From P0 to P3, in most cases, it is a narrow, unforeseen alley, with children going to school and cars going to work coming out of the garage.
(B) Between P3 and P5 Run relatively smoothly on the suburban main road. On this day, the P4 signal connection was good and I was able to run through. However, the traffic jam was the same as before the intersection P5, and I stopped 4 times waiting for the signal.
(C) Between P5 and P6, the car runs smoothly on the way, but the signal at the intersection P6 is always stopped without change.
(D) P6-P8
If you pass through intersection P6, the signal at the next intersection P7 is linked to the previous signal, so in most cases you will arrive at work P8 without stopping. To do.

FIG. 3 shows a traveling pattern of a certain day in the traveling route illustrated in FIG.
With reference to this FIG. 3, the setting of the driving | running | working stable area and area division point in this embodiment is demonstrated.
In the present embodiment, it is important to set a segment division point at a traveling stable point where the traveling state is relatively stable, and to perform subsequent traveling analysis based on this divided point. Therefore, in the present embodiment, it is necessary not to change the analysis even when the travel pattern slightly changes in the subsequent travel on the same travel route, in order not to complicate the analysis. Travel analysis is performed on such a premise, and the degree of influence on the travel pattern can be analyzed for factors such as date, day of the week, departure time, and weather.

(1) Selection of travel stable section In this embodiment, since it divides | segments in a travel stable area, the travel stable area in a travel route is selected.
The travel stability condition, which is a condition for selecting the travel stable section, is “when the vehicle travels continuously for a predetermined time Tb (or a predetermined distance Lb) at a predetermined vehicle speed Vb or higher”.
In addition, although the predetermined vehicle speed Vb in this embodiment is 40 km / h, other speeds, such as 50 km, may be used, and the user may arbitrarily set the speed. Further, the vehicle speed Vb may be changed in accordance with a preset travel area (residential area, suburb, etc.) and road type (general road, highway, etc.).
In addition, the travel stable section can be a section in which the speed fluctuation range continues for a predetermined time or more within a predetermined range or for a predetermined distance or more.

In the case of the travel pattern illustrated in FIG. 3, a section exceeding the predetermined speed Vb corresponds to t12 (the travel time of this section is also represented by t12, the same applies hereinafter), t23, t35, t56, and t68.
Among these, sections t23, t35, t56, and t68 that continuously exceed Vb for a predetermined time tb or more correspond to the traveling stable section.

(2) Setting of segment division points FIG. 3 is an example of traveling on a certain day. When traveling repeatedly on the same daily route for a long time in almost the same time zone as in commuting, If you run every day, there is a slight difference, and it is natural that it varies. For example, in the example of FIG. 3, the vehicle passes through the intersection P4 at Vb or more, but may stop depending on the day, and the intersection P3 can pass without being caught by a signal depending on the day. On the other hand, since the intersections P1 and P2 are once stopped intersections, the driving patterns are almost the same every day.
In consideration of the above points, in the present embodiment, when there is an intersection in the selected traveling stable section, the traveling stable section is displayed by being divided by the intersection.

First, the starting point P0 and the destination P8 are both ends of the travel route, and are unconditionally set as division points PD0 and PD8, respectively.
Between the intersections P0 and P1, the vehicle speed condition Vb or higher is not satisfied, and between P1 and P2, the vehicle speed condition is temporarily satisfied. However, the time point Tb or lower does not satisfy the dividing point setting condition. That is, between P0 and P1 and between P1 and P2 is not a traveling stable section, no dividing point is set between them.
On the other hand, traveling stability conditions (vehicle speed, time conditions) are satisfied between the intersections P2 and P3. Therefore, an intermediate point between the intersection P2 and the intersection P3 including the travel stable section t23 is set as a section division point (travel stable point) PD23.

A relatively long traveling stable section t35 exists between the intersections P3-P5.
However, as described above, depending on the day, there may be a stop at the signal at P4, or sometimes there is a traffic jam and the stop / start is repeated.
Therefore, a new threshold value Tbb (Tbb> Tb) larger than Tb is provided, and when the time t34 to the intersection P4 in the traveling stable section t35 is equal to or longer than Tbb, even if the traveling stable time is Tb, Set it so that it does not fall much less than the same time.
For example, in the travel pattern of FIG. 3, when the travel stable section t35 is divided by the intersection P4, the section t34 is t34 ≧ Tbb, so the section division point PD34 is set between the intersection P3 and the intersection P4.
Similarly, when the travel stable section t35 is divided by the intersection P4, the section t45 is t45 ≧ Tbb, so the section division point PD45 is provided between the intersection P4 and the intersection P5.

Between the intersections P5 and P6, there is a traveling stable section t56 that satisfies the traveling stability condition independently only in this section, and the PD 56 is provided between the intersection P5 and the intersection P6.
A travel stable section t68 exists between the intersections P6-P8, and an intersection P7 exists in the travel stable section t68. Therefore, similarly to the traveling stable section t35, the traveling stable section t68 is divided at the intersection P7. In this case, even if the section T67 between the intersections P6-P7 passes through the intersection P7 without stopping, since t67 <Tb (t67 <Tbb), no division point is set.
On the other hand, since the section t78 divided by the intersection P7 is t78 ≧ Tbb, the section division point PD68 is set in the middle between the intersections P7 and P8.

Next, a specific processing operation of the travel route dividing point setting process according to the present embodiment will be described.
In the present embodiment, the segment division point is set after the initial travel for the travel route.
FIG. 4 is a flowchart showing the processing operation of the dividing point setting process.
First, the running pattern prediction unit 11 performs initial settings such as securing the RAM area necessary for the division (step 50).
Then, the travel pattern prediction unit 11 determines whether or not the travel route traveled is the first travel (step 51), and if it is the second or later (; N), the process is terminated.
On the other hand, in the case of the first travel (step 51; Y), the travel pattern prediction unit 11 acquires the travel data acquisition unit 13 during travel and stores it in the RAM for storage in the travel data storage unit 15 after the travel is completed. The stored travel data is acquired (step 52). That is, the travel pattern predicting unit 11 stores travel data including the current vehicle position detected at predetermined intervals (distance, time, etc.) and the travel speed at the position from the departure point to the destination stored in the RAM. get. In the present embodiment, traveling data is stored in the RAM in order to perform the dividing point setting process for the first traveling route. Therefore, the travel data is acquired from the RAM, but may be acquired from the travel data storage unit 15.

Then, the running pattern prediction unit 11 sets the value of n secured in the RAM to n = 0 (step 53).
In addition, the travel pattern prediction unit 11 secures in RAM a division point storage area for storing data for specifying a travel route and a division point for the travel route, and divides the destination into a division point PD0. The point PDN is set and stored in the RAM (step 54). Here, the value of N is a value obtained by adding 2 to the number of the departure point and the destination (arrival point) to P when the number of intersections existing between the departure point and the destination (arrival point) is P. N = P + 2. Further, as the data for specifying the travel route, the departure point, the intersection data existing on the travel route (in this embodiment, the intersection number, but may be a coordinate value), and the arrival point data string. By comparing the point data strings, it can be determined whether the traveled route is the first route or the previously traveled route. In addition, as the point data of the departure point and the arrival point, when registered in the navigation DB 12 as a destination or departure point (including point data when a user personally registers a point like a home or a company) Uses the point data, and if not registered, the coordinate value is used.

Next, the traveling pattern prediction unit 11 determines whether or not a section traveling at a predetermined vehicle speed Vb or higher, which is one of the traveling stability conditions, exists in the section of the intersection Pn−Pn + 1 from the acquired traveling data (step S11). 55).
When there is no travel section at the vehicle speed Vb or higher (step 55; N), 1 is added to n (step 58), the process returns to step 55, and the next intersection section is determined. In the example of FIG. 3, the intersection P0-P1 corresponds.

On the other hand, when there is a travel section at the vehicle speed Vb or higher (step 55; Y), the time tn (n + 1) continuously traveled at the vehicle speed Vb or higher is another travel stability condition, the predetermined time Tb. It is judged whether it is above (step 57).
When the continuous running time at the vehicle speed Vb or higher is less than the predetermined time Tb (step 57; N), for example, the section t12 between the intersections P1-P2 and the section t67 between the intersections P6-P7 in FIG. Since the running stability condition is not satisfied, the process proceeds to step 56 to determine the next section.

When the continuous travel time tn (n + 1) at the vehicle speed Vb or higher is equal to or longer than the predetermined time Tb (step 57; Y), the travel pattern predicting unit 11 determines that the vehicle speed v at the intersection P (n + 1) is v ≧ Vb. It is determined whether or not there is (step 58). Thereby, it is determined whether or not the intersection P (n + 1) is an intersection in the traveling stable section.
If the vehicle speed v at the intersection P (n + 1) is not equal to or higher than Vb (step 58; N), the intersection P (n + 1) is not an intersection in the travel stable section, so an intermediate point between the intersection Pn and the intersection P (n + 1) is determined. The section division point PDn (n + 1) is set, and the coordinates (latitude, longitude) of the section division point PDn (n + 1) are stored in the division point storage area secured in the RAM (step 59). In the illustration of FIG. 3, the division point PD23 set at an intermediate point between the intersections P2 and P3 (the travel stable section t23 exists) and the division point PD45 set between the intersections P4 and P5 correspond.

When the vehicle speed v at the intersection P (n + 1) is equal to or higher than Vb (step 58; Y), the intersection P (n + 1) is an intersection within the traveling stable section, so the traveling pattern predicting unit 11 determines the intersection P (n + 1). ) To determine whether or not the traveling time tn (n + 1) to the intersection P (n + 1) in the traveling stable section is equal to or longer than Tbb (step 60).
When tn (n + 1) ≧ Tbb (step 60; Y), even if the vehicle stops at the intersection Pn (n + 1), the traveling stable time does not fall below Tb substantially the same time, so the traveling pattern predicting unit 11 Then, the process proceeds to step 59. That is, the traveling pattern predicting unit 11 sets an intermediate point between the intersections Pn and P (n + 1) as the segment division point PDn (n + 1), and stores the coordinates (latitude, longitude) of the segment division point PDn (n + 1) in the RAM. Store in the reserved dividing point storage area. In the illustration of FIG. 3, the division point PD34 set at an intermediate point between the intersections P3 and P4 (the section t34 ≧ Tbb of the traveling stable section t35 exists) corresponds.

  On the other hand, when tn (n + 1) <Tbb (step 60; N), there is a possibility that the traveling stable section may not exist when stopping at the intersection P (n + 1). Without setting a dividing point between the intersections Pn-P (n + 1), the process proceeds to step 56, and then it is determined whether or not a dividing point can be set for the next intersection.

In step 59, after the coordinates of the set dividing point are stored in the dividing point storage area of the RAM, the traveling pattern prediction unit 11 compares n with (N-1) (step 61), and n is (N-1). ) (N = P (number of intersections) +2) (; N), the process proceeds to step 56 to determine whether or not division points can be set for the next intersection.
On the other hand, if n ≧ (N−1) (step 61; Y), since the possibility of setting the dividing points between the intersections up to the arrival point PN has ended, the traveling pattern prediction unit 11 performs the RAM division. The division point data stored in the point storage area is associated with the travel route specifying data of the divided travel route (data sequence consisting of departure point, intersection existing on the travel route, and each point data specifying the arrival point). Then, it is stored in the dividing point storage unit 17 (step 62), and the dividing point setting process is terminated.

As described above, according to the present embodiment, the vehicle speed and the acceleration / deceleration state vary every time the vehicle travels even on the same travel route, but the travel state does not vary greatly like an intersection. A point where the traveling is stable every time is set as a dividing point, and the dividing point is substantially in the same traveling state (vehicle speed or the like), that is, within a normal speed fluctuation range. This makes it possible to satisfy the condition that the traveling state at both ends (division points) of each small section is stable when the travel route is divided at the division points.
As a result, if the conditions at both ends of the divided small section are considered, it is not necessary to consider the influence of the traveling state of the preceding and following sections, and only each small section can be analyzed independently.
In addition, it is possible to grasp the running state change in the vicinity of the intermediate intersection in the booth divided at the dividing point as a pattern, and it is easy to classify.
Furthermore, it is possible to independently analyze and predict a traveling speed pattern or the like for each of a plurality of continuous small sections along the traveling route, and connect the results without restriction.

Next, the overall flow of the operation for estimating the traveling speed pattern using the dividing points set based on the traveling stable section will be described.
FIG. 5 is a flowchart showing the overall operation of the traveling speed pattern estimation apparatus according to the embodiment of the present invention.

First, the traveling pattern prediction unit 11 transmits the traveling data acquired by the traveling data acquisition unit 13 to the traveling data storage unit 15 and stores and accumulates the same as when a normal navigation device executes navigation processing ( Step S1). In addition, the travel pattern prediction unit 11 transmits the travel environment data acquired by the travel environment data acquisition unit 14 to the travel data storage unit 15 for storage.
In this case, the travel pattern predicting unit 11 stores travel data and travel environment data in one travel of the vehicle in association with each other in a predetermined area of the RAM, and at the time when one travel of the vehicle ends. The travel data storage unit 15 stores the travel data and travel environment data stored in the RAM. Note that the one-time traveling of the vehicle refers to the traveling from when the vehicle is once started until it is stopped, that is, until the driving device 20 is started and then stopped.

  Subsequently, the travel pattern predicting unit 11 executes a frequent route specifying process for specifying a frequently traveled route as a frequent route based on the travel data stored and accumulated in the travel data storage unit 15 (step S1). S2). In this case, a route that the vehicle has traveled over a predetermined number of times is specified and registered as a frequent route.

Subsequently, the travel pattern prediction unit 11 executes a travel speed pattern candidate generation process for generating a travel speed pattern candidate (step S3). In this case, the traveling pattern predicting unit 11 identifies the frequent route from each point data (starting point data, intermediate intersection data, and arrival point data) of the frequent route, and the frequent route (traveling route). Is read from the dividing point storage unit 17.
Then, the traveling pattern prediction unit 11 divides the frequent route into a plurality of small sections based on the read division point data, and classifies past traveling data in each small section into a plurality of normal classifications. Based on the data, a representative travel speed pattern is generated for each classification, and is set as a travel speed pattern candidate. Therefore, a plurality of traveling speed pattern candidates are usually generated for each small section. There may be a small section in which only a single classification is applicable and only a single traveling speed pattern candidate is generated.

  Subsequently, the travel pattern prediction unit 11 executes a travel speed pattern estimation process for estimating a travel speed pattern on a route estimated to travel from now on (step S4). In this case, the travel pattern prediction unit 11 extracts travel data that matches the current travel environment data from the travel data stored in the travel data storage unit 15, and extracts the past travel extracted for each small section. The travel speed pattern candidate to which the most data belongs is selected as the estimated travel speed pattern. Then, the estimated traveling speed patterns selected for each small section are connected, and output as a traveling speed pattern in a route estimated to travel from now on.

Next, the frequent route specifying process will be described in detail.
FIG. 6 is a flowchart showing the operation of the frequent route specifying process in the embodiment of the present invention.

  First, the travel pattern prediction unit 11 collates the acquired current travel data with the past travel data stored in the travel data storage unit 15 when the acquisition of travel data in one travel of the vehicle is completed. Then, it is confirmed how many times the vehicle has traveled the same route as the route traveled this time (step S2-1). In this case, rather than directly comparing the current travel data with past travel data, the current travel data is collated with the data stored in the navigation database 12 by the map matching function used in the navigation process. It is more efficient to specify the route traveled and check how many times the route has traveled in the past.

Subsequently, the traveling pattern predicting unit 11 determines whether or not the number of times of traveling on the same route as the currently traveling route is a predetermined number of times (for example, 10 times) or more (Step S2-2). And when it is more than predetermined times, the driving | running | working pattern estimation part 11 specifies and registers the path | route which drove this time as a frequent path | route, and complete | finishes a process (step S2-3). If the number of times is not greater than or equal to the predetermined number of times, the process is terminated without setting the route traveled this time as a frequent route.
In this embodiment, it is registered in the frequent route after 10 runs. However, the number of other times such as 15 times and 20 times may be used as the registration condition for corresponding to the frequent route, and the user can change it. Any n times (the default value is, for example, 10 times) may be used.

Next, traveling speed pattern candidate generation processing will be described.
FIG. 7A is a diagram illustrating an example of dividing a frequent route in the present embodiment into a plurality of small sections, FIG. 7B is a diagram illustrating an example of travel data of the small sections in the present embodiment, and FIG. FIG. 9 is a diagram showing an example of travel data of each classified subsection in the embodiment, FIG. 9 is a diagram showing an example of a representative travel speed pattern of the subsection classified in the present embodiment, and FIG. 10 is an implementation of the present invention. FIG. 11 is a flowchart showing an example of travel speed pattern candidate generation processing according to the embodiment of the present invention. FIG. 11 is a diagram showing an example of classification of travel data for all small sections of a frequent route in the form of FIG.

First, in the travel speed pattern candidate generation process (FIG. 11), the travel pattern prediction unit 11 acquires travel data stored in the registered frequent route from the travel data storage unit 15 (step S3-1).
Further, the traveling pattern prediction unit 11 acquires the dividing point data set in the above-described dividing point setting process (see FIG. 4) for the frequent route (traveling route) from the dividing point storage unit 17, and for each dividing point. The frequent route is divided into a plurality of small sections (step S3-2).

In the present embodiment, the traveling pattern predicting unit 11 divides each small section without considering other small sections by dividing into small sections for each dividing point set in the traveling stable section where the traveling is stable. The travel speed pattern candidates can be generated and selected, and the estimated travel speed pattern can be determined simply by connecting the selected travel speed pattern candidates.
Therefore, the traveling pattern prediction unit 11 divides the frequent route into a plurality of, for example, four small sections A to D at each division point, as illustrated in FIG.
In the figure, S is the start point of the frequent route, and G is the end point of the frequent route. Further, the divided travel data corresponding to the small section B is as shown in FIG. 7B, for example. In FIG. 7B, the horizontal axis represents the distance from the starting point of the small section B, the vertical axis represents the speed (vehicle speed) at each distance, and each curve represents a traveling speed pattern corresponding to each traveling data. .

  Subsequently, the travel pattern prediction unit 11 calculates the overall average travel speed in each small section of the frequent route based on the travel data accumulated for the frequent route (step S3-3). In this case, the average travel speed for the entire section is an average travel speed for the entire small section, and is calculated for each travel data. Since the travel data is acquired and accumulated every time the frequent route is traveled, the average travel speed of the entire section in each small section is calculated for each of the number of travel data equal to the number of travels on the frequent route.

  Then, the travel pattern prediction unit 11 extracts the travel data having a section overall average travel speed having a close value from the travel data, and sets the travel data as a set based on the section overall average travel speed. Classification is performed (step S3-4). The traveling data is classified for each small section of the frequent route. In this case, the classification of the travel data is performed by using, for example, a clustering method called a k-average method.

For example, travel data corresponding to the small section B as shown in FIG. 7B is classified into three sets as shown in FIGS. 8A to 8C. In FIG. 8, the horizontal axis represents the distance from the starting point of the small section B, the vertical axis represents the speed at each distance, and each curve represents a traveling speed pattern corresponding to each traveling data.
In this case, for each curve shown in FIG. 7 (B), the overall average travel speed as the average travel speed in the entire small section B is calculated, and based on the overall average travel speed, a clustering called k-average method is performed. When the classification is performed using the technique, it is divided into a classification B-1, a classification B-2, and a classification B-3 as shown in FIGS. 8A to 8C, it can be seen that the traveling data in each set classified based on the overall average traveling speed of the section has a generally similar traveling speed pattern.

  Subsequently, the travel pattern prediction unit 11 calculates the point average travel speed at each point in the range from the start point to the end point of each small section using the travel data belonging to the set for each classified set. The point average traveling speed is, for example, an average of traveling speeds corresponding to respective traveling data at each point determined every predetermined distance from the starting point. Subsequently, the traveling pattern predicting unit 11 generates a transition of the spot average traveling speed by connecting the spot average traveling speed at each point so as to be continuous from the start point to the end point of the small section. The transition of the traveling speed is set as a representative traveling speed pattern of the set (step S3-5).

For example, based on traveling data belonging to a set of classification B-1, classification B-2, and classification B-3 as shown in FIGS. 8A to 8C, respectively, FIGS. A representative traveling speed pattern as shown in FIG.
In FIG. 9, the horizontal axis represents the distance from the starting point of the small section B, the vertical axis represents the speed at each distance, and each line represents a representative travel speed pattern. In the representative travel speed pattern, a travel average corresponding to travel data at each point is simply averaged to calculate a spot average travel speed, and the spot average travel speed is continuously connected from the start point to the end point of the small section B. Is.

  In addition, when calculating the spot average traveling speed at each point, the traveling speed corresponding to each traveling data at each point may be simply averaged, or the new traveling data may have a larger influence. Weighting can also be performed by adding a weighting coefficient. By the way, the representative travel speed pattern is treated as a candidate when the travel speed pattern is estimated for each small section in the travel speed pattern estimation process. Therefore, in the present embodiment, the representative travel speed pattern is hereinafter referred to as the travel speed pattern. This is called a pattern candidate. The generated travel speed pattern candidates are transmitted to and stored in the travel speed pattern candidate machine part 16.

Further, as a method for classifying the travel data into sets, in addition to the method based on the overall average travel speed as described above, a method based on the similarity of travel speed patterns can be used. Here, “similarity of travel speed pattern” refers to travel data for each small section, as shown in FIG. 7B, the horizontal axis is the distance from the start point of the small section, and the vertical axis is each distance. Is a degree of similarity of the shapes of the curves when drawn as a curve indicating a traveling speed pattern in a two-dimensional space. And the driving | running | working data with which the curve which shows a driving | running | working speed pattern is mutually similar is extracted, and it classify | categorizes into one set. In this case, the traveling pattern prediction unit 11 performs the following operations (1) and (2).
(1) First, some traveling data is arbitrarily selected.
(2) Next, for each selected driving data,
(2-1) The square (that is, the square error) of the difference in speed between the selected travel data and other travel data at each point in the range from the start point to the end point of the small section is calculated.
(2-2) If the square error at each point is within a predetermined range, it is determined that the other travel data belongs to the same set as the selected travel data.
Thereafter, the operations (1) and (2) are repeated for each small section.

  The method based on the overall average travel speed as described above represents each travel data by a scalar amount called the overall average travel speed and applies the k-average method to the scalar amount to obtain the travel data. In contrast to the classification method, the method based on the similarity of the traveling speed pattern represents the traveling data as a vector quantity called a speed train at each point, and the k average method is applied to the vector quantity. This is a classification method. Therefore, according to the method based on the similarity of travel speed patterns, the amount of calculation increases, but travel data having similar travel speed patterns can be classified more appropriately.

  FIG. 10 exemplifies the result of classifying all the small sections of the frequent route in this way. In this case, the travel data is divided into one set (A-1) in the small section A, three sets (B-1, B-2, B-3) in the small section B, and two sets in the small section C. In (C-1, C-2), the small section D is classified into two sets (D-1, D-2). In the figure, each set in each small section is indicated by an ellipse. Although not shown, traveling speed pattern candidates corresponding to each set are generated.

  In addition, the relationship between the set in the small section on the upstream side (left side in the figure, departure side) adjacent to each other and the set in the small section on the downstream side (right side in the figure, on the arrival point side) connects the sets. The number represented by a line segment and circled on the line segment indicates the number of travel data belonging to the set in the downstream small section among the travel data belonging to the set in the upstream small section. .

  In the example shown in FIG. 12, the total number of travel data is 50, and it can be seen that in the small section A, all the travel data belong to one set A-1. Then, from the line segment connecting the sets and the numbers circled on the line segment, in the next small section B, 10 travel data out of the 50 travel data belonging to the set A-1 are collected in the set B. It can be seen that 15 traveling data belong to the set B-2 and 25 traveling data belong to the set B-3. Similarly, in the small section C, 8 of the 10 travel data belonging to the set B-1 belong to the set C-1, and 2 travel data belong to the set C-2. Of the 15 travel data belonging to the set B-2, 8 travel data belong to the set C-1, and 7 travel data belong to the set C-2. Note that all 25 travel data belonging to the set B-3 belong to the set C-2. Further, in the small section D, 3 of the 16 travel data belonging to the set C-1 belong to the set D-1, and 13 travel data belong to the set D-2. Of the 34 travel data belonging to the set C-2, 24 travel data belong to the set D-1, and 10 travel data belong to the set D-2.

  In the travel speed pattern candidate generation process in the present embodiment, travel environment data is not used. That is, the travel pattern prediction unit 11 acquires travel data from the travel data storage unit 15 without acquiring travel environment data, and generates a travel speed pattern candidate based on the travel data. Therefore, for example, traveling data on a rainy day and traveling data on a sunny day may be mixed in the traveling data belonging to one set.

  In this way, the generation of the travel speed pattern candidates based on only the travel data, not based on the travel environment data, may occur depending on the road conditions even if the travel environment such as day of the week or weather is different. This is because the same traveling speed pattern may be obtained without depending on the environment. Under such circumstances, the travel speed pattern candidates can be generated based on a large amount of travel data by generating the travel speed pattern candidates based only on the travel data without considering the travel environment. The reliability of the generated traveling speed pattern candidates can be improved. For example, when the travel environment is rain, that is, when the number of travel data on a rainy day is one, the travel speed pattern on the next rainy day is estimated based on the one travel data. Will be less reliable. On the other hand, along with the rainy day driving data, when there are nine driving data that are not rainy day driving data but have a similar driving speed pattern, based on a total of ten driving data, The present embodiment that estimates the traveling speed pattern of the next rainy day improves the reliability.

Next, the traveling speed pattern estimation process will be described.
FIG. 12 is a diagram illustrating an example of travel data estimation for all small sections of a frequent route according to the embodiment of the present invention, and FIG. 13 is a flowchart illustrating an operation of travel speed pattern estimation processing according to the embodiment of the present invention. It is.

  First, the traveling pattern prediction unit 11 determines whether or not the drive device 20 has been started (step S4-1), and if it has been started, acquires the current position and the current time of the vehicle. If the drive device 20 has not been started, the process is terminated. Then, the travel pattern prediction unit 11 refers to the travel data stored in the travel data storage unit 15 based on the acquired current position and current time of the vehicle, and is predicted to travel on a frequent route from now on. (Step S4-2). For example, in the case of commuting, if the current position is at home and the time is in the morning commuting time zone, the user may be trying to travel on a commuting route registered as a frequent route by referring to accumulated traveling data. I can judge. In addition, in the case of frequent routes, if the destination is set, or if a travel route to the destination is searched, the assumed travel from the current vehicle location (departure point) to the destination It is possible to determine whether the route is a frequent route or the searched traveling route.

  The travel pattern predicting unit 11 ends the process when it is not expected to travel on the frequent route from now on, but if it is predicted, the travel pattern prediction unit 11 uses the current travel environment data such as the day of the week and the wiper operation status as the travel environment. Obtained from the data obtaining unit 14 (step S4-3). Subsequently, the travel pattern prediction unit 11 extracts past travel data that matches the current travel environment data acquired from the travel environment data acquisition unit 14 from the travel data storage unit 15 (step S4-4). In this case, since the travel data and the travel environment data in one travel of the vehicle are stored in association with each other, the current travel environment data matches with the search by using the travel environment data as a condition. The travel data associated with the travel environment data can be extracted as travel data that matches the current travel environment data.

  Subsequently, as shown in the example shown in FIG. 10, the travel pattern prediction unit 11 selects travel data that matches current travel environment data from a set of travel data classified for all small sections of the frequent route. The set to which the largest number belongs is specified (step S4-5).

  If it demonstrates based on the example shown by FIG. 10, the data that the wiper is operating will be acquired as the present driving environment data, for example. Then, it is assumed that there are three pieces of driving data associated with the driving environment data indicating that the wiper is operating in the total of 50 driving data. Further, the three traveling data belong to the set A-1 in the small section A, belong to the set B-1 in the small section B, belong to the set B-2, and belong to the set B-2 in the small section C. It is assumed that one belongs to C-1 and two belong to the set C-2. In the small section D, two belong to the set D-1 and one belongs to the set D-2. In this case, the set to which the most travel data matching the current travel environment data belongs is set A-1, set B-1, set C-2, and set D-1. Therefore, it can be estimated that the traveling speed pattern of the frequent route to be traveled from now on is most likely to belong to the set connected by the line segments indicated by bold lines in FIG.

  Subsequently, the travel pattern prediction unit 11 extracts a set of travel speed pattern candidates specified in each small section from the travel speed pattern candidate storage unit 16 (step S4-6). Then, the traveling pattern prediction unit 11 outputs the extracted traveling speed pattern candidate as an estimated traveling speed pattern (step S4-7), and ends the process.

In this way, in the traveling speed pattern estimation process, traveling data that matches the current traveling environment data is extracted, and a traveling speed pattern candidate of a set to which the traveling data belongs most is extracted to be an estimated traveling speed pattern. It has become.
Therefore, an appropriate estimated traveling speed pattern that matches the current traveling environment data can be output. Here, the estimated travel speed pattern that matches the current travel environment data is a set of travel speed pattern candidates to which the travel data that matches the current travel environment data belongs most.

  Further, as described above, the traveling speed pattern candidates are generated based only on traveling data, not based on traveling environment data, and are generated based on a large amount of traveling data. Therefore, for example, even when the number of travel data matching a specific travel environment data such as a rainy day is small, a highly accurate estimated travel speed pattern can be output.

  Note that the driving environment that affects the driving speed pattern usually includes the weather, time zone, day of the week, settlement date, end of period, and the like. In the case of the weather, generally, rainy weather has the effect of slowing the flow of traffic and lowering the traveling speed even on the same route. Further, in the case of the time zone, the morning and evening commuting time zone has a traffic jam and the running speed becomes low, and the traffic speed is low at night and the like so that the running speed becomes high. In the case of a day of the week, the traffic speed is high on Sundays, so the traveling speed increases. In the example of the morning commute, if you depart 5 minutes earlier than usual, you can arrive 10 minutes earlier. . The settlement date is a day such as fifty days or a leap day that is generally set as an accounting deadline for transactions in general, and the traffic speed increases, so the traveling speed decreases. Moreover, the term end is a period such as the end of March or the end of the year, which is generally set as a settlement period, and similarly, the traffic speed increases and the traveling speed decreases. Furthermore, sudden traffic accidents, unexplained traffic jams, festivals, demonstrations, temporary closures due to firefighting activities, etc., road closures, etc. for a certain period of time and traffic restrictions also affect the traveling speed pattern. It can be considered as a driving environment.

Next, the operation of the drive device 20 based on the estimated traveling speed pattern will be described.
FIG. 14 is a first diagram showing an example of a schedule set in the present embodiment, FIG. 15 is a second diagram showing an example of a schedule set in the present embodiment, and FIG. 16 is an embodiment of the present invention. It is a flowchart which shows operation | movement of a scheduling process.

  First, the main control device 26 acquires an estimated traveling speed pattern from the traveling pattern prediction unit 11 (step S11), and based on the estimated traveling speed pattern, determines the operating states of the engine 21 and the motor 24 and the SOC of the battery 23. A scheduling process for setting an operation schedule for control is executed. When the operation schedule is set, the main control device 26 controls the operations of the engine 21, the engine control device, the motor 24, the generator 22, and the inverter according to the operation schedule, and executes a traveling process for causing the vehicle to travel.

  Then, after acquiring the estimated traveling speed pattern, the main control device 26 acquires the current SOC detected by the capacity detection sensor of the battery 23 (step S12). In this case, since the scheduling process is performed immediately before traveling on the frequent route, the current SOC is the starting point of the frequent route, that is, the SOC at the departure point.

  Subsequently, the main control device 26 sets the end point of the frequent route, that is, the SOC at the destination (step S13). In this case, the SOC at the destination is, for example, the same value as the SOC at the departure point of the frequent route, but can be arbitrarily set within the SOC management range.

  By the way, also in the drive control system 10 of the hybrid vehicle of this Embodiment, the management width | variety of SOC which is the electrical storage amount of the battery 23 is preset, and SOC falls within the management width | variety similarly to a normal hybrid vehicle. In this way, the operation schedule is set. The battery 23 has a voltage-current characteristic that varies depending on the SOC as in a normal battery, and the life of the battery 23 is shortened if the SOC is too large or too small. For example, when overcharged, the battery 23 may be destroyed. Therefore, for example, the management width set in advance is set so that the maximum value is about 60% and the minimum value is about 40%, and the SOC of the battery 23 is controlled not to exceed the management width. The

  However, if the management width is fixed, if there are many opportunities for the generator 22 to generate a regenerative current as in a long downhill, the regenerative current cannot be sufficiently collected in the battery 23 and is wasted. Therefore, although the generator 22 has many opportunities to generate a regenerative current, the fuel consumption cannot be sufficiently reduced.

On the other hand, in the present embodiment, when traveling on a frequent route, since the traveling speed pattern on the frequent route to be traveled is estimated, an operation schedule is set so that the battery 23 is not overdischarged or overcharged. can do.
Therefore, the main control device 26 adjusts the upper limit value or the lower limit value of the SOC management width, and widens the management width as necessary, so that the SOC does not become overdischarged and overcharged, and the regenerative current is sufficiently increased. An efficient operation schedule is set so that the fuel consumption can be sufficiently reduced by collecting in the battery 23 (step S14). That is, based on the estimated traveling speed pattern, an operation schedule that minimizes the fuel consumption of the engine 21 is set.

  Subsequently, the main control device 26 sets an operation schedule for controlling the operating states of the engine 21 and the motor 24 and the SOC of the battery 23 according to the acquired estimated traveling speed pattern. Then, it is determined whether or not there is an abnormality in the set operation schedule (step S15). Here, the abnormality is that the SOC value of the destination included in the set operation schedule is different from the initially set value, or the SOC included in the set operation schedule exceeds the management range. It is. If there is an abnormality, the main control device 26 sets the operation schedule again. The fuel consumption amount and the vehicle system information may be included in the driving schedule, and it may be determined whether or not the driving schedule is abnormal based on the fuel consumption amount and the vehicle system information.

  For example, it is desirable to travel by the motor 24 because the traveling efficiency by the engine 21 is poor in a traffic jam section. Therefore, as shown in FIG. 14A, when the estimated travel speed pattern output by the travel pattern prediction unit 11 includes a traffic jam section, that is, when the occurrence of traffic jam is predicted in advance, the main control device 26 Sets the driving method so that the battery 23 is sufficiently charged before the traffic jam section.

Here, in the conventional control in which the upper limit value or the lower limit value of the SOC management range is not adjusted, the SOC changes as indicated by S10 in FIG. In this case, the SOC has reached the upper limit due to the power generation when the SOC is running stably, and there is no remaining power for deceleration at the subsequent section E, and the regenerative energy at the corner is wasted. In this embodiment, regeneration of the E section can be predicted, so that power generation during stable running is kept to the minimum necessary, wasteful power generation is stopped, SOC is set to S11, and remaining energy in the E section is left to recover deceleration energy. . Thereafter, in the F section, S20 in the case of the conventional control can be set in advance to S21 in the present embodiment.
Further, when the upper limit value or the lower limit value of the SOC management width of the conventional control is not adjusted, the SOC changes as shown in FIG. That is, since the travel distance by the motor 24 in the traffic jam section is long and the current consumption is large, the engine 21 is operated as indicated by A to prevent the SOC from interrupting the lower limit value, and the generator 22 It is necessary to carry out power generation running that causes power generation to occur. Therefore, the fuel consumption cannot be reduced sufficiently. Further, since the vehicle arrives at the destination immediately after passing the traffic jam section, it cannot sufficiently generate power, and the SOC at the destination cannot be made equal to the SOC at the departure point.

On the other hand, when it is predicted in advance that the battery consumption is high due to a traffic jam section or the like, the SOC management width of the section in front of the section where the battery consumption is expected to be high (such as a traffic jam section) The upper limit value can be adjusted to increase to an appropriate value, for example, from the conventional value indicated by su0 in FIG. 14 (b) to the value indicated by su1 in FIG. 14 (c).
For example, the SOC can be changed as shown in FIG. In this case, there is a stable travel section in the section before the traffic congestion section, and there is a regeneration section in which large deceleration is estimated and a regenerative current is generated, such as section E and section F in FIG. When detected from the location or the gradient of the road data in the navigation DB 12, the battery 23 can be sufficiently charged in the travel stable section and the regeneration section. As a result, even if the travel distance of the motor 24 in the traffic jam section is long and the current consumption is large, the SOC is set to an appropriate value without operating the engine 21 as shown by B in FIG. Can keep. Therefore, fuel consumption can be reduced sufficiently. The SOC at the destination can be set equal to the SOC at the departure point. Further, after expanding the SOC management range as shown in FIG. 14 (c), the SOC is set to S12, S12, in the preceding section so that the regenerative energy can be accumulated in the sections E and F in which the regeneration of energy can be expected by deceleration in the same manner as described above. Energy efficiency can be improved more by setting like S22.

  On the other hand, if there is no regenerative section in front of the traffic jam section, it is known that there is a lot of battery consumption in the traffic jam section and that subsequent charging is not sufficient, so stable driving in front of the traffic jam section In such a section, the engine 21 is driven in a region where the combustion efficiency is good (rotation speed and torque) to generate power by the generator 22, and is charged within a range where the COC of the battery 23 is not overcharged. As a result, the engine 21 can be efficiently driven to generate power and charge in a section where traveling is stable, without driving the engine 21 in a traffic jam section.

  Further, for example, since the efficiency of traveling by the engine 21 is poor even in a section where acceleration / deceleration and start / stop are frequent, it is desirable to travel by the motor 24. Therefore, as shown in FIG. 15A, the estimated travel speed pattern output by the travel pattern predicting unit 11 includes a section where acceleration / deceleration and start / stop are frequently performed, and the vehicle travels stably immediately after the section. When a possible section is included, the main control device 26 sets a driving method so that the battery 23 is charged after passing through a section where acceleration / deceleration and start / stop are frequent.

  Here, when the upper limit value or the lower limit value of the SOC management width is not adjusted, the SOC changes as shown in FIG. That is, since the travel distance by the motor 24 is long and the current consumption is large in the section where acceleration / deceleration and start / stop are frequent, the engine 21 is set as indicated by C in order to prevent the SOC from interrupting the lower limit value. It is necessary to perform power generation running that causes the power generator 22 to generate power. Therefore, the fuel consumption cannot be reduced sufficiently.

On the other hand, when the lower limit value of the SOC management width is adjusted and lowered to an appropriate value, the SOC changes as shown in FIG. In this case, when a regenerative section exists immediately after a section where acceleration / deceleration or start / stop is frequently performed, the regenerative current can be collected in the battery 23 by the start point of the regenerative section. In addition, when there is no regenerative section, the engine 21 is driven in a region where the combustion efficiency is good (rotation speed, torque) in a section where the subsequent travel is stable to generate power. As a result, the battery 23 can be sufficiently charged in the regenerative section or the section where the traveling is stable, so that the traveling distance by the motor 24 in the section where acceleration / deceleration or start / stop is large is long and the current consumption is large. However, as indicated by D in FIG. 15C, the SOC can be kept within the management range without operating the engine 21. Therefore, the fuel consumption can be sufficiently reduced. Then, in a section immediately after a section where acceleration / deceleration and start / stop are frequent, the battery 23 can be sufficiently charged to recover the SOC.
In the example shown in FIG. 15 (c), the upper limit value of the SOC management width is also increased. However, as in the example shown in FIG. 14 (c), there is a congestion section on the frequent route. Because it is included. If the frequent route does not include a traffic jam section, only the lower limit value of the SOC management width needs to be adjusted.

  As described above, the main control device 26 detects the regeneration section based on the estimated traveling speed pattern, and sets the operation schedule so that the regenerative current can be collected in the battery 23 by the start point of the regeneration section. Therefore, the regenerative current is not wasted. In addition, since the operation schedule is set so that all the regenerative current generated in the regenerative section can be recovered by the battery 23, the fuel consumption can be sufficiently reduced.

Next, the operation of the traveling process will be described.
FIG. 17 is a flowchart showing the operation of the traveling process in the embodiment of the present invention.

  When the vehicle starts traveling on the frequent route, the main control device 26 controls the operations of the engine 21, the engine control device, the motor 24, the generator 22, and the inverter according to the set operation schedule. In this case, the main control device 26 acquires the SOC detected by the capacity detection sensor of the battery 23, that is, the actual SOC in real time, compares it with the SOC included in the operation schedule (step S21), and determines whether there is an abnormality. Is determined (step S22).

  Since the traveling speed pattern when actually traveling on the route is not completely the same as the estimated traveling speed pattern, it is considered that the actual SOC change is different from the SOC change included in the driving schedule. Therefore, when the state where the difference between the actual SOC and the SOC included in the operation schedule exceeds a preset threshold value is continued for a while, the main controller 26 determines that there is an abnormality, and at that time The driving schedule from the current position of the vehicle to the destination is set again (step S25). If there is no abnormality, the main control device 26 continues the control according to the operation schedule (step S23). Further, even when the actual SOC exceeds the upper limit value or the lower limit value of the management width, it may be determined that there is an abnormality and the operation schedule from the current position of the vehicle to the destination may be set again. Further, when the actual SOC exceeds the upper limit value or the lower limit value of the management width, the operations of the engine 21, the engine control device, the motor 24, the generator 22, and the inverter are controlled so that the SOC returns within the management width. Then, the battery 23 may be charged or discharged from the battery 23.

  If the state where the current position of the vehicle deviates from the frequent route continues for a while, the main control device 26 determines that the vehicle is not traveling on the frequent route and acquires navigation information from the navigation database 12. The driving schedule from the current position of the vehicle to the destination is set again. Furthermore, even when the current position of the vehicle deviates from the frequent route due to a temporary detour or the like, the driving schedule from the current position of the vehicle to the destination is set again. When the current position of the vehicle is not greatly deviated from the frequent route, the main controller 26 determines whether the difference between the actual SOC and the SOC included in the driving schedule is equal to or less than a preset threshold value. Continue control according to the set operation schedule.

  Subsequently, the main controller 26 determines whether or not the vehicle has arrived at the destination (step S24). If the vehicle has not arrived, the operation described above is repeated.

  As described above, in the present embodiment, the travel pattern prediction unit 11 of the hybrid vehicle drive control system 10 analyzes travel data when traveling on a frequent route that travels frequently for commuting, attending school, shopping, and the like. Thus, when a travel speed pattern candidate is generated and travels on a frequent route, an appropriate estimated travel speed pattern corresponding to the current travel environment data is output. Then, the main control device 26 sets an operation schedule based on the estimated traveling speed pattern, and controls operations of the engine 21, the engine control device, the motor 24, the generator 22, and the inverter according to the operation schedule. Therefore, the SOC can be maintained appropriately, and the fuel consumption of the engine 21 can be sufficiently reduced.

  In this case, the travel pattern predicting unit 11 divides the frequent route into small sections, and based on the travel data only, the travel speed pattern candidates for each small section are not based on the travel environment data such as date, day of the week, and weather. Generate. Therefore, since a traveling speed pattern candidate can be generated based on a large amount of traveling data, the accuracy of the traveling speed pattern candidate can be increased.

  In addition, the travel pattern prediction unit 11 extracts travel data that matches the current travel environment data, extracts travel speed pattern candidates of a set to which the travel data belongs most, and sets it as an estimated travel speed pattern. Therefore, it is possible to output an appropriate estimated traveling speed pattern corresponding to the current traveling environment data.

  That is, when generating a travel speed pattern candidate, the travel speed pattern candidate is generated based only on the travel data when traveling on the same frequent route without being based on the travel environment data, and the frequent route to be traveled from now on is generated. When outputting the estimated traveling speed pattern, the traveling speed pattern candidate of the set to which the past traveling data matching the current traveling environment data belongs most is selected. Therefore, more travel data can be used to generate travel speed pattern candidates, and travel data can be appropriately narrowed down based on travel environment data to output an estimated travel speed pattern.

  Furthermore, since the traveling pattern prediction unit 11 stores past traveling data in the traveling speed pattern candidate storage unit 16 as several traveling speed pattern candidates for each small section, it is possible to consolidate data and reduce the storage capacity. can do.

  Furthermore, since the traveling pattern prediction unit 11 outputs the estimated traveling speed pattern by extracting the traveling speed pattern candidate that matches the traveling environment data when the traveling is about to be performed, the traveling pattern predicting unit 11 estimates based on a large amount of past traveling data. Compared to outputting the traveling speed pattern, the calculation processing load is reduced, and the estimated traveling speed pattern can be output quickly.

  Further, when setting a schedule for efficiently traveling on the route, the main control device 26 adjusts the upper limit value or the lower limit value of the SOC management width, widens the management width as necessary, and drives the driving method. Set. Therefore, an efficient driving method can be set so that the regenerative current can be sufficiently collected in the battery 23 and the fuel consumption can be sufficiently reduced while the SOC does not exceed the management range. .

  In the embodiment described above, the estimated travel speed pattern output means extracts travel data that matches the current travel environment data for each small section, and travel speed pattern candidates that represent the set to which the travel data belongs most. May be extracted and an estimated traveling speed pattern may be output.

  As described above in detail, according to the present embodiment, in the traveling speed pattern estimation device, traveling information storage means for storing traveling data and traveling environment data in association with each other, and traveling speed based on the traveling data. Travel speed pattern candidate generating means for generating a pattern candidate, and estimated travel speed pattern output means for extracting a travel speed pattern candidate that matches the current travel environment data and outputting an estimated travel speed pattern of a route to be traveled from now on Have.

  In this case, when generating the travel speed pattern candidate, when generating the travel speed pattern candidate based only on the travel data, not based on the travel environment data, and outputting the estimated travel speed pattern of the route to be traveled from now on Since a traveling speed pattern candidate that matches the current traveling environment data is selected, a highly accurate estimated traveling speed pattern can be quickly output with a light calculation processing load.

  The other travel speed pattern estimation device further includes a frequent route identifying means for identifying a frequent route based on the travel data, and a section dividing means for dividing the frequent route into small sections, and generates travel speed pattern candidates. The means generates a traveling speed pattern candidate for each small section, and the estimated traveling speed pattern output means extracts the traveling speed pattern candidate for each small section and outputs an estimated traveling speed pattern of a frequent route to be traveled from now on.

  In this case, since the estimated traveling speed pattern of the frequent route that travels frequently is output, a highly accurate estimated traveling speed pattern can be output quickly. Further, since the frequent route is divided into small sections and the traveling speed pattern candidates are extracted for each small section, it is possible to output an estimated traveling speed pattern with higher accuracy quickly.

  In still another travel speed pattern estimation device, the travel speed pattern candidate generation unit further calculates travel data for each small section based on the average travel speed for each small section or the similarity of the travel speed pattern for each small section. Classification is performed, and a traveling speed pattern that represents a set of traveling data for each classified small section is generated as a traveling speed pattern candidate.

Although one embodiment of the present invention has been described above, the present invention is not limited to the described embodiment, and various modifications can be made within the scope described in each claim.
For example, in the embodiment described above, in the dividing point setting process, as a travel stable section in which travel is stable, a “section in which a predetermined vehicle speed Vb or higher continues for a predetermined time Tb or longer” is a travel stable section in which travel is stable. However, in this invention, it is not limited to this, You may select another area as a driving | running | working stable area. For example, “a section where a predetermined vehicle speed Vb or more continues for a predetermined distance or more”, “a section where the speed fluctuation range continues within a predetermined range for a predetermined time”, “a section where the speed fluctuation range continues for a predetermined distance or more”, etc. May be selected as the travel stable section.

Further, in the present embodiment, the intermediate point between the intersections Pn-P (n + 1) including the traveling stable section is set as the dividing point. However, including the case of the above embodiment and the modified example, the dividing point is as follows. It is also possible to set.
(1) Intermediate point of travel stable section (2) Intermediate time point of time required to travel in the travel stable section. In this case, the intermediate time point is determined by averaging one time or multiple times. (3) In the traveling stable section, the intermediate point or intermediate time point in the constant vehicle speed continuation range. (4) In the traveling stable section, during acceleration and deceleration. A point that passes the specified vehicle speed (5) A maximum speed point in the stable travel zone (6) A middle point of the intersection (regardless of whether there is a stable travel zone)
(7) Intermediate point excluding the traffic jam range between intersections (8) Intermediate time point of travel time between intersections (9) Maximum speed point between intersections (10) Vehicle point after a predetermined time from the intersection The “point” does not have to be exactly a half point in the time or section, and may be an arbitrary point in the range of the time or section.

In the embodiment described and the modifications described above, when the travel stable section in the dividing point setting process is selected based on each intersection, the following processing may be performed on the intersection.
(1) If the distance between the intersections is short, the influence of a subsequent intersection may appear, so the previous intersection is selected as the next intersection.
(2) When going out to a highway from a residential area or the like, the intersection interval is short and there are many places where it is necessary to stop temporarily, and stopping and starting are often performed regardless of the subsequent intersection. The intersection in such a case is also included in the subsequent intersection and selected.
(3) Even if the distance between the intersections is long, if it is included in the traffic congestion range of the next intersection, it is selected as the next intersection.
The above determinations (1) and (2) are made based on the road data in the navigation DB 12, and the determination (3) can be made from each piece of travel data that has traveled a plurality of times.

In the present embodiment, the section division point is set by the division point setting process after the first run on the travel route, but it is possible to do other than the first time.
For example, the dividing point setting process may be performed after travel in which the travel route is recognized as a frequent route (travel that satisfies the frequent route recognition condition). As described above, in the travel recognized as the frequent route, since the vehicle travels a plurality of times on the travel route, the user is used to traveling more than the initial travel, and is stable and average travel. For this reason, a more stable point can be set as a division point by performing the division point setting process using the travel data at the time when driving is accustomed to the travel route.

In the present embodiment, in the traveling speed pattern candidate generation process (see FIG. 11), the frequent route is divided using the division point data set in the division point setting process, and the division point data to be used is fixed. However, the dividing point data may be changed as appropriate.
That is, the stable running section on the frequent route is almost constant in the short term, but the traffic flow usually changes in the long term, and the following situations exist.
(A) The flow becomes smooth by improving intersections and adjusting new traffic lights.
(B) If the interlocking method of the traffic light is changed even between the intersections, the above traveling sections need to be combined or separated.
(C) Bypass can be made and dispersed and smooth.
(D) The flow of vehicles will change greatly due to the establishment, expansion, and abolition of factories, and the establishment and abolition of schools and facilities.
(E) The driver himself will develop a new route.

In such a case, it is necessary to review the dividing points, and changes are made by the following method.
(A) When the route is changed In this case, the dividing point setting process is newly performed only for the changed portion, and the dividing point data is changed. The driver can change the route by switching the new route or comparing the location information of the vehicle and the road information while driving.
In addition, about the change of a route, you may make it judge that it is a change, when driving | running the route after a change in multiple times.
(B) For example, when the traffic light at the intersection Pn, which has been stopped at all times, is interlocked with the traffic light at the previous intersection P (n-1), when the traffic stops almost at the intersection Pn, division between the two intersections It is simpler to combine the points PD (n−1) n and PDn (n + 1). In this case, the division point is changed by the user inputting a division point to be merged and an instruction to merge, or by the travel pattern prediction unit 11 determining from the road information. In addition, the traveling pattern prediction unit 11 monitors the traveling state at the intersection Pn. For example, when the traveling pattern prediction unit 11 has not stopped at the intersection Pn more than D times, the division points PD (n−1) n and PDn (n + 1) are detected. ) May be combined.

  (C) In addition, the traveling pattern prediction unit 11 may periodically perform division point setting processing every time the frequent route is traveled F times and update the division point data in the division point storage unit 17. Good. In this case, the travel pattern predicting unit 11 counts the number of travels for each frequent route even after the frequent route registration, and after performing the dividing point setting process every time the travel number becomes F times, The count value for the path is set to zero.

  In the above-described embodiment, the “frequent route” is specified when the travel data is accumulated a plurality of times for the travel route. However, the route having the travel data even once is specified as the “frequent route”. You may do it. Although it is undeniable that the reliability of the driving pattern estimation results will be low if the past data is only once, the inventors have obtained knowledge from field surveys that the commuting route and the like will have almost the same driving pattern every day. Therefore, even if the past travel data is 1, it is considered that there is an effect of estimating the travel pattern. Furthermore, in the above-described embodiment, the device automatically specifies the “frequent route” from past travel data, but the user directly inputs and registers a specific route such as a commute route or once. The traveled route may be registered as a frequent route by the user, and the registered route may be set as the “specified route” to estimate the travel pattern when traveling on the specified route. In this case, the process of causing the user to input and register the “specific route” and recording it corresponds to means for specifying the travel route.

It is a conceptual diagram which shows the structure of the drive control system of a hybrid vehicle to which the driving speed pattern estimation apparatus in one Embodiment of this invention and the drive control apparatus of a hybrid vehicle are applied. It is the figure which illustrated the driving | running route for demonstrating the division | segmentation process in embodiment same as the above. It is a figure showing the driving | running | working pattern on the day with a driving | running route in embodiment same as the above. It is a flowchart showing the processing operation | movement of the dividing point setting process in embodiment same as the above. It is a flowchart which shows the whole operation | movement of the traveling speed pattern estimation apparatus in embodiment same as the above. It is a flowchart which shows the operation | movement of the frequent path | route identification process in embodiment same as the above. It is a figure which shows the example which divides the frequent route in embodiment same as the above into a plurality of small sections, and the example of the travel data of a small section. It is a figure which shows the example of the driving | running | working data of the classified small section in embodiment of this invention. It is a figure which shows the example of the representative travel speed pattern of the classified small section in embodiment same as the above. It is a figure which shows the example of classification | category classification of the driving | running | working data about all the small sections of the frequent route in embodiment same as the above. It is a flowchart which shows operation | movement of the driving speed pattern candidate production | generation process in embodiment same as the above. It is a figure which shows the example of the driving | running | working data estimation about all the small sections of the frequent route in embodiment same as the above. It is a flowchart which shows operation | movement of the traveling speed pattern estimation process in embodiment same as the above. It is a 1st figure which shows the example of the set schedule in embodiment same as the above. It is a 2nd figure which shows the example of the set schedule in embodiment same as the above. It is a flowchart which shows the operation | movement of the scheduling process in embodiment same as the above. It is a flowchart which shows the operation | movement of the travel process in embodiment same as the above.

Explanation of symbols

DESCRIPTION OF SYMBOLS 10 Drive control system of hybrid vehicle 11 Travel pattern prediction part 13 Travel data acquisition part 14 Travel environment data acquisition part 15 Travel data memory | storage part 17 Division | segmentation point memory | storage part 21 Engine 22 Generator 23 Battery 24 Motor 26 Main control apparatus

Claims (4)

Travel information storage means for storing travel data for the travel route;
A travel route specifying means for specifying traveling line path,
Section dividing means for dividing the identified travel route into small sections based on the travel data, at a travel stable point where travel stability is assumed,
Based on the travel data, travel speed pattern candidate generating means for generating a travel speed pattern candidate for each of the divided small sections;
Estimated travel speed pattern output means for extracting one travel pattern candidate from the generated travel speed pattern candidates and outputting an estimated travel speed pattern of the identified travel route to be traveled for each small section; Prepared,
The section dividing means includes “a point in a section where a predetermined vehicle speed or higher continues for a predetermined time or longer”, “a point in a section where a predetermined vehicle speed or higher continues for a predetermined distance”, “a speed fluctuation range within a predetermined range, and a predetermined time The above specified travel route is set as a travel stable point where the stability of the travel is assumed to be “a point in the continued section” or “a point in the section where the speed fluctuation range is within a predetermined range and continues for a predetermined distance or more”. Dividing into sections,
A traveling speed pattern estimation device characterized by the above. The travel speed pattern estimation apparatus according to claim 1, wherein the travel route specifying means specifies a route on which the vehicle travels frequently as a frequent route based on the travel data. The travel information storage means stores travel data and travel environment data for the travel route in association with each other,
The estimated travel speed pattern output means extracts a travel speed pattern candidate that matches the current travel environment data for each of the small sections, and outputs an estimated travel speed pattern of the identified travel route to be traveled from now on.
The travel speed pattern estimation apparatus according to claim 1 or 2, characterized by the above. A hybrid vehicle that includes an engine in which a part or all of the driving force is used for driving or generating electric power of the vehicle and a motor that generates the driving force of the vehicle, and is driven by at least one of the driving force of the engine and the motor. ,
Power storage means that is charged by power generation and regeneration by driving the engine and supplies power to the motor;
A storage amount detection means for detecting a storage amount of the storage means;
The travel speed pattern estimation device according to any one of claims 1 to 3 ,
Based on the estimated travel speed pattern of the identified route output by the travel speed pattern estimation device and the amount of power stored in the power storage means, the engine consumes a minimum amount of fuel on the identified route. And an operation schedule setting means for setting an operation schedule of the motor,
A drive control apparatus for a hybrid vehicle, comprising:

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