JP6232597B2 - Control device for hybrid vehicle - Google Patents

Description

  The present invention relates to a control apparatus for a hybrid vehicle having an engine and a motor as a travel drive source.

  Conventionally, it is mounted on a hybrid vehicle having an engine driven using fuel such as gasoline as a travel drive source and a motor driven by using a battery as an electric power source. In consideration of this, a control device for a hybrid vehicle that performs charge / discharge control of a battery is known (see, for example, Patent Document 1).

Patent No. 3610879

However, in a conventional hybrid vehicle control device, information such as the congestion status and vehicle speed on the planned travel route is determined based on statistical data in the planned travel section.
Therefore, although the vehicle speed and the like at a predetermined point on the planned travel route can be predicted, the ease of occurrence of the predetermined vehicle speed at a future point is not taken into account with respect to the vehicle speed at a certain point. That is, information such as the predicted vehicle speed at a predetermined point is a fixed value determined for each point, and the relationship with the predicted vehicle speed at other points is not considered.
When such statistical data is used to perform hybrid vehicle powertrain control (for example, scheduling of the driving mode, power distribution control of the engine / motor, etc.), the inherent driving situation of the host vehicle is reflected. It was difficult to perform proper powertrain control. That is, there is a problem that it is difficult to perform appropriate powertrain control because the traveling state of the host vehicle in the planned traveling section cannot be predicted with high accuracy.

  The present invention has been made paying attention to the above problem, and provides a hybrid vehicle control device capable of accurately predicting the traveling state of the host vehicle in a scheduled traveling section and performing appropriate powertrain control. Objective.

In order to achieve the above object, a hybrid vehicle control device of the present invention is mounted on a hybrid vehicle including an engine and a motor as a travel drive source, and includes a data storage means, a probability distribution calculation means, a data prediction means, a power Train control means.
The data storage means stores travel data of the host vehicle in each travel section when traveling on a predetermined travel route divided into a plurality of travel sections.
The probability distribution calculating means, traveling from the travel data stored in the data storage means, with respect to the first driving data in the first travel segment of the plurality of travel sections, the next of said first travel segment The relationship of the second traveling data in the second traveling section that is the section is obtained by a probability distribution.
The data predicting means predicts the second travel section using travel data having the highest probability among the probability distributions obtained by the probability distribution calculating means when traveling on the predetermined travel route. Calculate travel data.
The power train control means performs power train control of the host vehicle based on the predicted travel data predicted by the data prediction means.
When the actual driving data of the host vehicle in the first driving section is different from the driving data having the highest probability in the probability distribution, the data predicting unit determines the actual driving data of the host vehicle. And calculating the predicted travel data in the second travel section.

In the control apparatus for a hybrid vehicle of the present invention, the relationship between the second travel data in the second travel section and the first travel data in the first travel section is obtained by a probability distribution. Then, using this probability distribution, predicted travel data in the planned travel section is calculated.
Thereby, prediction driving | running | working data can be calculated | required based on the likelihood of the 2nd driving | running | working data with respect to 1st driving | running | working data. Therefore, for example, compared with the case where the estimated travel data is obtained based on simple statistical data at a predetermined point, the relationship between the travel data of the first travel section and the second travel section is taken into account when calculating the predicted travel data, and the prediction The accuracy can be improved.
As a result, it is possible to accurately predict the traveling state of the host vehicle in the planned traveling section and perform appropriate powertrain control.

1 is an overall system diagram illustrating a hybrid vehicle to which a control device according to a first embodiment is applied. It is a figure which shows an example of the probability distribution map used in Example 1. FIG. It is explanatory drawing which shows the calculation method of the estimated vehicle speed in the scheduled driving | running | working area in Example 1. FIG. It is explanatory drawing which shows the calculation method in the case of considering an actual vehicle speed at the time of calculation of the estimated vehicle speed in the scheduled driving | running | working area in Example 1. FIG. It is a flowchart which shows the flow of the vehicle control process performed in the vehicle control part of Example 1. FIG. It is a figure which shows an example of the relationship between each travel area and statistical vehicle speed in the control apparatus of a comparative example. It is a whole system figure showing the hybrid vehicle to which other examples of the control device of Example 1 were applied.

  EMBODIMENT OF THE INVENTION Hereinafter, the form for implementing the control apparatus of the hybrid vehicle of this invention is demonstrated based on Example 1 shown in drawing.

Example 1
First, the configuration of the hybrid vehicle control device according to the first embodiment will be described by dividing it into “the overall system configuration of the hybrid vehicle”, “the configuration of the vehicle control system”, and “the configuration of the vehicle control process”.

[Overall system configuration of hybrid vehicle]
FIG. 1 is an overall system diagram illustrating a hybrid vehicle to which the hybrid vehicle control device of the first embodiment is applied. The overall system configuration of the hybrid vehicle according to the first embodiment will be described below with reference to FIG.

  The hybrid vehicle S in the first embodiment is a rear-wheel drive FR hybrid vehicle (an example of a hybrid vehicle). As shown in FIG. 1, the drive system of the FR hybrid vehicle S includes an engine Eng, a first clutch CL1, a motor / generator MG, a second clutch CL2, an automatic transmission AT, and a transmission input shaft IN. A propeller shaft PS, a differential DF, a left drive shaft DSL, a right drive shaft DSR, a left rear wheel RL (drive wheel), and a right rear wheel RR (drive wheel). Note that FL is the left front wheel and FR is the right front wheel.

  The engine Eng is a gasoline engine or a diesel engine, and engine start control, engine stop control, throttle valve opening control, fuel cut control, and the like are performed based on an engine control command from the vehicle control unit 1. A first clutch CL1 is connected to the engine output shaft via a flywheel FW.

  The first clutch CL1 is a clutch interposed between the engine Eng and the motor / generator MG, and a first clutch control hydraulic pressure generated by a hydraulic unit (not shown) based on a control command from the vehicle control unit 1. Thus, fastening, slip fastening, and opening are controlled. As the first clutch CL1, for example, a normally closed state in which a complete engagement is controlled from a complete engagement to a slip engagement to a complete release by a stroke control using a hydraulic actuator having a piston is maintained by an urging force of a diaphragm spring. A dry single plate clutch is used. The first clutch CL1 is a mode switching means for switching between an electric vehicle mode using only the motor / generator MG as a travel drive source and a hybrid vehicle mode using both the engine Eng and the motor / generator MG as a travel drive source. It has become.

  The motor / generator MG is a synchronous motor / generator in which a permanent magnet is embedded in a rotor and a stator coil is wound around a stator, and the three-phase generated by the inverter 2 based on a control command from the vehicle control unit 1. It is controlled by applying an alternating current. This motor / generator MG is driven to rotate by receiving power supplied from the battery 3, and can also operate as an electric motor that starts the engine Eng and drives the left and right rear wheels RL and RR (hereinafter, this operation state is referred to as “operating state”). When the rotor receives rotational energy from the engine Eng and the left and right rear wheels RL and RR, it functions as a generator that generates electromotive force at both ends of the stator coil, and may charge the battery 3 (Hereinafter, this operation state is referred to as “regeneration”). Note that the rotor of the motor / generator MG is connected to the transmission input shaft IN of the automatic transmission AT.

  The second clutch CL2 is a clutch interposed between the motor / generator MG and the left and right rear wheels RL, RR, and is generated by a hydraulic unit (not shown) based on a control command from the vehicle control unit 1. Engagement / slip engagement / release is controlled by the two-clutch control hydraulic pressure. As the second clutch CL2, for example, a normally open wet multi-plate clutch or a wet multi-plate brake capable of continuously controlling the oil flow rate and hydraulic pressure with a proportional solenoid is used.

The automatic transmission AT is interposed between the motor / generator MG and the left and right rear wheels RL and RR. For example, the automatic transmission AT can change a stepped gear stage such as forward 7 speed / reverse 1 speed according to the vehicle speed, accelerator opening, etc. It is a stepped transmission that switches automatically. A propeller shaft PS is connected to the transmission output shaft of the automatic transmission AT. The propeller shaft PS is coupled to the left and right rear wheels RL and RR via a differential DF, a left drive shaft DSL, and a right drive shaft DSR.
In the first embodiment, among a plurality of frictional engagement elements that are engaged at each gear stage of the automatic transmission AT, an optimum frictional engagement element (clutch that is disposed on the torque transmission path and that meets a predetermined condition) Or the brake) is selected and the second clutch CL2 is selected. That is, the second clutch CL2 is not newly added as a dedicated clutch independent of the automatic transmission AT.

  The FR hybrid vehicle S has an electric vehicle mode (hereinafter referred to as “EV mode”) and a hybrid vehicle mode (hereinafter referred to as “HEV mode”) as a driving mode depending on a driving mode, that is, a driving drive source. And).

  The “EV mode” is a mode in which the first clutch CL1 is released, the engine Eng is stopped, and the vehicle travels only with the driving force of the motor / generator MG. The “EV mode” has a motor travel mode and a regenerative travel mode. This “EV mode” is selected when the required drive torque is low and the remaining charge of the battery 3 (hereinafter referred to as “battery SOC (abbreviation of State Of Charge)”) is secured.

  The “HEV mode” is a mode in which the first clutch CL1 is engaged and the vehicle travels with the driving forces of both the engine Eng and the motor / generator MG. The “HEV mode” has a motor assist travel mode, a power generation travel mode, and an engine travel mode. The “HEV mode” is selected when the required drive torque is high or when the battery SOC is insufficient.

[Configuration of vehicle control system]
As shown in FIG. 1, the vehicle control system of the FR hybrid vehicle (own vehicle) S according to the first embodiment includes a vehicle control unit 1, an inverter 2, a battery 3, a navigation system 4, and a communication unit 5. It is configured.

  The vehicle control unit 1 includes a microcomputer, its peripheral components, various actuators, etc., and the rotational speed and output torque of the engine Eng, the engagement / slip engagement / release of the first clutch CL1 and the second clutch CL2, and the motor / generator MG. Rotational speed, output torque, and automatic transmission AT gear stage are controlled. Further, the vehicle control unit 1 includes a storage unit 11, a probability distribution calculation unit 12, a prediction unit 13, and a control unit 14.

The storage unit 11 corresponds to a data storage unit that stores vehicle speed data (travel data) of the host vehicle in each travel section when traveling on a predetermined travel route divided into a plurality of travel sections.
That is, the storage unit 11 stores the vehicle speed in the travel section in which the host vehicle has traveled in the past in association with each travel section.

The probability distribution calculation unit 12 is configured to calculate, from the travel data stored in the storage unit 11, the first vehicle speed data in the first travel section of the plurality of travel sections on the predetermined route. This corresponds to a probability distribution calculating means for obtaining the relationship of the second vehicle speed data in the second travel section with a probability distribution.
Here, the “first travel section” is an arbitrary section of a plurality of travel sections, and here, all sections are targeted. Further, the “second travel section” is a travel section that precedes the first travel section, and here is a section next to the section set as the first travel section.

Further, when the probability distribution calculation unit 12 obtains the relationship of the second vehicle speed data to the first vehicle speed data by the probability distribution, a plurality of vehicle speed ranges are set in the first vehicle speed data. Specifically, a vehicle speed range of zero km / h to less than 40 km / h, a vehicle speed range of 40 km / h to less than 60 km / h, a vehicle speed range of 60 km / h to less than 80 km / h, and a speed range of 80 km / h or more. It is a vehicle speed range.
In the second vehicle speed data, the same plurality of vehicle speed ranges as the first vehicle speed data are set. Specifically, a vehicle speed range of zero km / h to less than 40 km / h, a vehicle speed range of 40 km / h to less than 60 km / h, a vehicle speed range of 60 km / h to less than 80 km / h, and a speed range of 80 km / h or more. It is a vehicle speed range.
Then, the probability distribution calculation unit 12 calculates a probability distribution for each vehicle speed range in the “second travel section”, which is the next section, from each vehicle speed range in the “first travel section”. Since the “first traveling section” covers all sections, the probability distribution to the vehicle speed range of the next section is calculated in each section and each vehicle speed range. The calculated probability distribution is shown in the probability distribution map shown in FIG.

The prediction unit 13 corresponds to data prediction means for calculating predicted vehicle speed data (predicted travel data) in a planned travel section using the probability distribution obtained by the probability distribution calculation unit 12 when traveling on a predetermined travel route. To do.
That is, the prediction unit 13 uses the probability distribution map shown in FIG. 2 to obtain vehicle speed data in a travel section scheduled to travel in the future. At this time, first, as shown in FIG. 3, in the traveling section in which the current FR hybrid vehicle S is traveling (hereinafter referred to as “current traveling section n”), the current traveling section is based on the vehicle speed information from the vehicle speed sensor 6. A traveling vehicle speed range at n (here, a vehicle speed range Pn of zero km / h to less than 40 km / h) is set.
In the next section of the current travel section n (hereinafter referred to as “next section n + 1”), the probability distribution from the travel vehicle speed range Pn set in the current travel section n to each vehicle speed range of the next section n + 1 is the most. A vehicle speed range that is highly probable (here, a vehicle speed range Pn + 1 of 60 km / h to less than 80 km / h) is set as a predicted vehicle speed range in the next section n + 1.
Next, in the next section of the next section n + 1 (hereinafter referred to as “next section n + 2”), the probability distribution from the predicted vehicle speed range Pn + 1 set in the next section n + 1 to each vehicle speed range of the next section n + 2 The vehicle speed range with the highest probability (here, the vehicle speed range Pn + 2 between 60 km / h and less than 80 km / h) is set as the predicted vehicle speed range in the next section n + 2.
Further, in the next section of the next section n + 2 (hereinafter referred to as “third section n + 3”), the probability distribution from the predicted vehicle speed range Pn + 2 set in the next section n + 2 to each vehicle speed range of the third section n + 3 Among these, the vehicle speed range with the highest probability (here, the vehicle speed range Pn + 3 of 40 km / h to less than 60 km / h) is set as the predicted vehicle speed range in the third section n + 3.
Hereinafter, this is repeated to obtain predicted vehicle speed data (in this case, predicted vehicle speed range) in the scheduled travel section n + α.

Further, in the prediction unit 13, when the current vehicle speed data of the FR hybrid vehicle S is different from the travel data having the highest probability in the probability distribution in the travel section, the current vehicle speed data of the FR hybrid vehicle S is obtained. Use to calculate the predicted vehicle speed range in the scheduled travel section.
In other words, in the case where the predicted vehicle speed range in the planned travel section n + α is obtained only from the probability distribution map, as described above, the predicted vehicle speed range is set to the vehicle speed range Pn + 2 in the next section n + 2 between 60 km / h and less than 80 km / h doing. However, it is assumed that when the FR hybrid vehicle S actually travels in the next section n + 2, it travels at 100 km / h. Therefore, when the predicted vehicle speed range (predicted vehicle speed data) in the planned travel section n + α is obtained, the travel vehicle speed range in the next section n + 2 is changed to a vehicle speed range Pn + 2 ′ of 80 km / h or more as shown in FIG. . The actual vehicle speed is detected by the vehicle speed sensor 6.
In the next section of the next section n + 2 (hereinafter referred to as “third section n + 3”), the probability distribution from the vehicle speed range Pn + 2 ′ set in the next section n + 2 to each vehicle speed range of the third section n + 3 Among these, the vehicle speed range with the highest probability (here, the vehicle speed range Pn + 3 ′ less than 80 km / h) is set as the predicted vehicle speed range in the third section n + 3.
Hereinafter, as described above, the predicted vehicle speed data (predicted vehicle speed range) in the scheduled travel section n + α is obtained using the probability distribution.

  The control unit 14 executes a vehicle control process to be described later, and based on the predicted vehicle speed data (predicted vehicle speed range) in the scheduled travel section n + α predicted by the prediction unit 13, the power train control (here, the FR hybrid vehicle S) This corresponds to power train control means for performing scheduling in the driving mode.

  Further, vehicle speed information from the vehicle speed sensor 6 and battery SOC information from the SOC monitoring unit 7 that constantly monitors the battery SOC are input to the vehicle control unit 1.

The navigation system 4 includes a storage unit 4a, a calculation unit 4b, and a display (not shown).
The storage unit 4a stores map information including road environment information such as road curvature radius, gradient, intersection, signal, railroad crossing, pedestrian crossing, speed limit, tollgate.
The arithmetic unit 4b receives a signal from a satellite and detects the absolute position of the FR hybrid vehicle S on the earth. Then, referring to the map stored in the storage unit 4a, the position (current location) where the current FR hybrid vehicle S exists is specified, and the planned travel route from the current location to the destination, and the planned travel route Set the travel section at. The scheduled travel route and the travel section on the route are input to the vehicle control unit 1. In addition, a display (not shown) is provided in the passenger compartment and is visible from the driver.

The communication unit 5 is connected to the vehicle control unit 1 and wirelessly communicates with a data center 8 having traffic information and statistical traffic data (telematics communication) via a communication network such as a wireless base station and the Internet (not shown). I do. This “communication” is bidirectional, and information is transmitted from the control unit 14 of the vehicle control unit 1 to the data center 8 via the communication unit 5, and information is transmitted from the data center 8 via the communication unit 5. Can be received and input to the probability distribution calculation unit 12 of the vehicle control unit 1.
As the communication unit 5, various devices such as a mobile phone, DSRC, and wireless LAN can be adopted.

[Configuration of vehicle control processing]
FIG. 5 is a flowchart illustrating a flow of a vehicle control process executed by the vehicle control unit according to the first embodiment. Hereinafter, each step of the flowchart of FIG. 5 showing the contents of the vehicle control process will be described.

In step S1, a planned travel route from the current location to the destination is set by the navigation system 4, and the process proceeds to step S2.
Here, in setting the planned travel route, first, the driver inputs a destination to the navigation system 4 by manual operation. The navigation system 4 searches for a plurality of travel routes based on the input destination information, the current location information detected based on the signal from the satellite, and the map information stored in the storage unit 4a. Is displayed on the display. Then, the driver selects and sets a planned travel route from the retrieved travel routes. The set scheduled travel route is transmitted to the data center 8 via the vehicle control unit 1 and the communication unit 5.

In step S2, statistical traffic data on the planned travel route is acquired from the data center 8 via the communication unit 5 following the setting of the planned travel route in step S1, and the process proceeds to step S3.
Here, the “statistical traffic data” is vehicle speed data determined for each reference position interval on the road called a node set in the data center 8. Further, the statistical traffic data to be acquired is limited to data of a vehicle type that is the same as or similar to the vehicle set in advance.

In step S3, following the acquisition of statistical traffic data in step S2, the set scheduled travel route is divided into a plurality of sections. Further, the probability distribution to the vehicle speed range of the next section in each of the divided sections and the plurality of vehicle speed ranges is calculated based on the acquired statistical traffic data and the vehicle speed data of each section stored in the storage unit 11, and step The process proceeds to S4.
That is, in step S3, the planned travel route is divided into a plurality of travel sections, and a probability distribution map as shown in FIG. 2 is obtained.
The travel section is divided by dividing the travel route according to the segment reference position set based on various LINK information necessary for the route segment that can be acquired by the FR hybrid vehicle S.

In step S4, following the calculation of the planned travel route classification and probability distribution in step S3, a predicted vehicle speed range (predicted vehicle speed data) in the planned travel section n + α is estimated using this probability distribution, and the process proceeds to step S5. .
Here, the estimation of the predicted vehicle speed range is executed by the prediction unit 13. At this time, the predicted vehicle speed range in the scheduled travel section n + α is estimated by using the vehicle speed range having the highest probability as the predicted vehicle speed range in the travel section in the probability distribution obtained in step S3. However, when the actual vehicle speed data when traveling in the travel section is different from the predicted vehicle speed range obtained from the probability distribution (when NO is determined in step S7 described later), the actual vehicle speed data corresponds to the corresponding vehicle speed. The vehicle is corrected to the vehicle speed range in the travel section, and the predicted vehicle speed range in the planned travel section n + α is calculated.

In step S5, following the estimation of the predicted vehicle speed range in step S4, a powertrain control plan in the scheduled travel section n + α is calculated, and the process proceeds to step S6.
Here, the “powertrain control plan” is scheduling of the travel mode of the FR hybrid vehicle S, and sets the travel mode in the planned travel section n + α.

  In step S6, following the calculation of the powertrain control plan in step S5, the vehicle currently travels based on section information (vehicle speed information, SOC information, etc.) when the FR hybrid vehicle S actually travels in a predetermined travel section. The power train control in the running section is changed, and the process proceeds to step S7. This change in powertrain control is first set so that the motor output maximum value “EV mode” at that time can be realized when the travel mode is set to “EV mode” in the plan. Then, the battery SOC at the time of actual traveling is compared with the set maximum motor output value, and the actual traveling mode is set (changed or continued with respect to the plan).

  In step S7, following the change of the powertrain control in step S6, the vehicle speed range corresponding to the actual vehicle speed in the travel section (currently traveled section) in which the powertrain control is changed is set using the probability distribution. It is determined whether or not the predicted vehicle speed range is different. If YES (predicted value = actual value), the process proceeds to step S8 on the assumption that there is no change in the predicted vehicle speed range in the scheduled travel section n + α. If NO (predicted value ≠ actual value), it is determined that the predicted vehicle speed range in the scheduled travel section n + α has been changed, and the process returns to step S4.

In step S8, following the determination that there is no difference between the planned value and the actual value in the travel section in step S7, it is determined whether or not the FR hybrid vehicle S has reached the destination. In the case of YES (arriving at the purpose), the process proceeds to the end, and this vehicle control process is terminated. If NO (no arrival at the destination), the process returns to step S4.
Here, whether or not the destination has been reached is determined from the current location information detected by the navigation system 4 based on the signal from the satellite.

Next, the operation will be described.
First, a description will be given of a vehicle speed estimation configuration and a problem of a hybrid vehicle control device of a comparative example, and subsequently a vehicle control operation in the hybrid vehicle control device of the first embodiment will be described.

[Vehicle Speed Estimation Configuration and Problem of Control Device for Hybrid Vehicle of Comparative Example]
FIG. 6 is a predicted vehicle speed map showing a predicted vehicle speed range in each travel section in the control device of the comparative example. Hereinafter, based on FIG. 6, the vehicle speed estimation structure and its subject in the control apparatus of the hybrid vehicle of a comparative example are demonstrated.

  In the control device of the comparative example, when estimating the predicted vehicle speed range in the planned travel section n + α, first, the planned travel path is set, and the set planned travel path is divided into a plurality of travel sections. Then, statistical traffic data (vehicle speed data) in each divided travel section is acquired from the data center 8. Moreover, the vehicle speed data memorize | stored in the own vehicle in each divided driving | running | working area are read. And the average value of vehicle speed data is calculated | required for every driving | running | working area, and it is set as the estimated vehicle speed area in the said driving | running | working area.

  That is, in the control device of the comparative example, the predicted vehicle speed range estimated for each travel section is a fixed value, is calculated for each travel section individually, and is not affected by the vehicle speed data in the adjacent travel sections.

Therefore, even when the actual vehicle speed is different from the predicted vehicle speed range calculated from statistical traffic data, etc., when actually traveling in a predetermined travel section, the predicted vehicle speed range and the actual value The deviation cannot be reflected in the estimation of the vehicle speed range of the planned travel section that will travel in the future.
In other words, the relationship between the travel data in a certain travel section and the travel data in another travel section ahead of that is not taken into account, and the prediction accuracy of the travel data in the planned travel section becomes low. It was happening.

[Vehicle control action]
In the FR hybrid vehicle S according to the first embodiment, vehicle speed data (travel data) on a route that the host vehicle has already traveled (traveled route) is stored in the storage unit 11 of the vehicle control unit 1. Further, the vehicle speed data on the route on which the other vehicle has already traveled (route that has traveled) is stored in the data center 8.
Then, when it is set to travel on a certain route (scheduled travel route), in the flowchart shown in FIG. 5, the process proceeds from step S1 to step S2 to step S3, and a plurality of travel routes set by dividing the planned travel route. The relationship of the vehicle speed data in each of the travel sections is obtained by a probability distribution (a probability distribution map shown in FIG. 2 is set).

Here, when the relationship of the vehicle speed data in each of the plurality of travel sections is obtained by the probability distribution, not only the vehicle speed data of the host vehicle stored in the storage unit 11 of the FR hybrid vehicle S as the host vehicle but also the communication unit 5 The vehicle speed data of other vehicles stored in the data center 8 acquired via the vehicle is also used.
Therefore, the accuracy of the probability distribution can be improved as the number of samples increases.

  And it progresses to step S4 and calculates | requires the estimated vehicle speed area in the scheduled driving | running | working area n + (alpha) using the probability distribution (probability distribution map shown in FIG. 2) of the following area with respect to each driving | running | working area and each vehicle speed area. That is, as shown in FIG. 3, a plurality of vehicle speed ranges in the next travel section (the next section n + 2) of the travel section with reference to a predetermined vehicle speed range (Pn + 1) in a certain travel section (for example, the next section n + 1). Among these, the vehicle speed range (Pn + 2) with the highest probability of occurrence is set as the predicted vehicle speed range in the next travel section (next section n + 2). This is repeated to obtain the predicted vehicle speed range in the scheduled travel section n + α.

  As a result, in consideration of the relationship between the vehicle speed range in the relevant section and the vehicle speed range in the next travel section (ease of occurrence of a predetermined vehicle speed range in the next section relative to the vehicle speed range in the relevant section), the planned travel section n + α The predicted vehicle speed range at can be estimated. Therefore, the prediction accuracy of the predicted vehicle speed range in the planned travel section n + α can be improved as compared with the prediction accuracy in the control device of the comparative example that obtains the planned vehicle speed range based on simple statistical data at a predetermined point.

And it progresses to step S5 and calculates the powertrain control plan in this scheduled driving | running | working area n + (alpha) based on the estimated vehicle speed area in the scheduled driving | running | working area n + (alpha) calculated | required using probability distribution.
At this time, since the prediction accuracy of the predicted vehicle speed region in the scheduled travel section n + α is high, the powertrain control plan can also be made suitable for the host vehicle, reducing fuel consumption, reducing electricity consumption, and driving performance. Improvements and reductions in the amount of exhaust can be achieved.

Moreover, in the first embodiment, the process proceeds to step S6, and the power train control is changed based on the section information in the traveling section in the traveling section in which the vehicle is actually traveling. Then, the process proceeds to step S7, and if there is a difference between the actual vehicle speed and the predicted vehicle speed range in the travel section, the process returns to step S4 to change the vehicle speed range corresponding to the actual vehicle speed to the vehicle speed range in the travel section. Then, based on the newly set vehicle speed range, the predicted vehicle speed range in the scheduled travel section n + α is calculated again.
That is, as shown in FIG. 4, the predicted vehicle speed range in a predetermined travel section (here, the next section n + 2) was Pn + 2 when obtained from the probability distribution, but the actual travel section (the next section n + 2 ) Is changed to Pn + 2 ′ based on the vehicle speed at the time of traveling). Then, based on the changed vehicle speed range Pn + 2 ′, a predicted vehicle speed range in the next section (third section n + 3) is further determined, and finally a predicted vehicle speed range in the scheduled travel section n + α is determined.

  As a result, even if the predicted vehicle speed range obtained from the probability distribution and the actual vehicle speed data deviate from the actual traveling speed, the future predicted vehicle speed range is determined according to the vehicle speed data at that time. It can be corrected. For this reason, it is possible to further improve the prediction accuracy of the predicted vehicle speed range in the scheduled travel section n + α.

  Further, in the first embodiment, as described above, not only the vehicle speed data of the own vehicle but also the vehicle speed data of other vehicles is used when obtaining the probability distribution. For this reason, the number of vehicle speed data increases and the precision of probability distribution can be improved. By using this highly accurate probability distribution, it is possible to further improve the prediction accuracy of the predicted vehicle speed range of the scheduled travel section n + α.

  In the first embodiment, power train control of the FR hybrid vehicle S is performed based on the vehicle speed data of the FR hybrid vehicle S. That is, traveling data used for powertrain control is vehicle speed data. Therefore, data acquisition and prediction can be performed easily with high accuracy, and powertrain control can be made more suitable for the host vehicle.

Next, the effect will be described.
In the hybrid vehicle control device of the first embodiment, the following effects can be obtained.

(1) It is mounted on an FR hybrid vehicle S equipped with an engine Eng and a motor / generator (motor) MG as a travel drive source,
Data storage means (storage unit) 11 that stores travel data (vehicle speed data) of the host vehicle in each travel section when traveling on a predetermined travel route divided into a plurality of travel sections (n to n + α);
From the travel data (vehicle speed data) stored in the data storage means (storage unit) 11, the first travel in the first travel section (current travel section n) of the plurality of travel sections (n to n + α). Relevance of second travel data in a second travel section (next section n + 1) ahead of the first travel section (current travel section n) of the plurality of travel sections (n to n + α) with respect to data A probability distribution computing means (probability distribution computing unit) 12 for obtaining the above by a probability distribution (probability distribution map FIG. 2);
When traveling on the predetermined travel route, the host vehicle (FR hybrid vehicle) S is used in the future by using the probability distribution (probability distribution map FIG. 2) obtained by the probability distribution calculating means (probability distribution calculating unit) 12. Data predicting means (predicting unit) 13 for calculating predicted traveling data in the planned traveling section n + α to travel;
Power train control means (control section) 14 for performing power train control of the host vehicle (FR hybrid vehicle) S based on the predicted travel data predicted by the data prediction means (prediction section) 13;
It was set as the structure provided with.
As a result, it is possible to accurately predict the traveling state of the host vehicle in the scheduled traveling section and perform appropriate powertrain control.

(2) The probability distribution calculation means (probability distribution calculation unit) 12 sets the next section in each of a plurality of travel data (vehicle speed range) set in each section of the plurality of travel sections (n to n + α). Find the probability distribution for multiple set driving data (vehicle speed range)
The data predicting means (predicting unit) 13 is configured to calculate the predicted traveling data using traveling data having the highest probability in the probability distribution.
Thereby, in addition to the effect of (1), the traveling state of the host vehicle in the scheduled traveling section can be easily predicted.

(3) The data predicting means (predicting unit) 13 is configured such that when the current traveling data (vehicle speed data) of the host vehicle (FR hybrid vehicle) S is different from traveling data having the highest probability in the probability distribution, The current travel data (vehicle speed data) of the host vehicle (FR hybrid vehicle) S is used to calculate the predicted travel data.
Thereby, in addition to the effect of (2), the prediction result of the traveling state of the host vehicle in the scheduled traveling section can be corrected according to the actual traveling data.

(4) A data center 8 for storing travel data of vehicles other than the host vehicle (FR hybrid vehicle) S in each travel section when traveling on a predetermined travel route divided into the plurality of travel sections (n to n + α). From communication means (communication unit) 5 for obtaining the travel data (vehicle speed data),
The data predicting unit (predicting unit) 13 includes the host vehicle (communication unit) 5 acquired via the communication unit (communication unit) 5 in addition to the travel data (vehicle speed data) stored in the data storing unit (storage unit) 11. FR hybrid vehicle) From the travel data (vehicle speed data) of vehicles other than S, the first travel data is related to the second travel data by a probability distribution (probability distribution map FIG. 2).
As a result, in addition to any one of the effects (1) to (3), the number of travel data used for obtaining the probability distribution increases, and the accuracy of the probability distribution can be improved.

(5) The travel data is configured as vehicle speed data.
As a result, in addition to any of the effects (1) to (4), data acquisition and prediction can be easily performed with high accuracy, and the powertrain control can be made more suitable for the host vehicle.

  Although the hybrid vehicle control device of the present invention has been described based on the first embodiment, the specific configuration is not limited to this embodiment, and the invention according to each claim of the claims is not limited thereto. Design changes and additions are allowed without departing from the gist.

In Example 1, although the example which acquires the vehicle speed data of another vehicle from the data center 8 via the communication unit 5 was shown, it is not restricted to this. As illustrated in FIG. 7, the probability distribution may be calculated based on the vehicle speed data of the host vehicle stored in the storage unit 11 without the communication unit.
In this case, since it is not necessary to acquire statistical traffic data with communication information from the outside, the communication unit 5 is not required and an inexpensive configuration can be achieved.

  Further, in the first embodiment, an example in which “vehicle speed data” is used as the travel data is shown. However, for example, acceleration, energy consumption, fuel consumption, accelerator operation, driver operation amount such as brake operation, etc. Any data indicating the state of the changing vehicle may be used.

And although the 1st driving | running | working area and the 2nd driving | running | working area set when calculating probability distribution were made into adjacent areas in Example 1, it is not restricted to this. For example, a predetermined section n may be set as the first travel section, and a travel section n + 2 that is several sections ahead of the predetermined section n may be set as the second travel section.
Furthermore, the relationship between the traveling data of the section n + 1 and the section n + 3 may be obtained for the section n.

The calculation accuracy of the probability distribution has a characteristic that the accuracy increases as the number of data increases. Therefore, for example, when traveling on the same road every day, such as a commuting route, the effect of improving the prediction accuracy of the predicted travel data is increased.
Also, by sharing not only the driving data but also the result of probability distribution with other vehicles, it is possible to predict the predicted driving data rather than calculating the predicted driving data on the planned driving route using only conventional statistical data. The accuracy can be further improved.

  Further, in the first embodiment, an example is shown in which the driving mode scheduling of the FR hybrid vehicle S is performed as the powertrain control. However, this power train control only needs to control the power train from the drive source to the drive wheels of the FR hybrid vehicle S. For example, the motor / motor based on the prediction of the deceleration regeneration amount in the FR hybrid vehicle S It may be output control of the generator MG and output control (power distribution control) of the engine Eng and the motor / generator MG for thermal protection of various parts in the vehicle.

  In the first embodiment, when the planned travel route is set in the navigation system 4, the driver finally selects and sets the planned travel route. However, the present invention is not limited to this. For example, even if the driver does not select and set the planned travel route, the planned travel route may be set with reference to the travel history information when starting the travel. In that case, a plurality of travel routes may not be displayed on the display provided in the vehicle interior.

  In the first embodiment, the vehicle control process is terminated when it is determined that the vehicle has arrived at the destination. However, the vehicle control process may be terminated by a driver's manual operation, and the destination is not set. Even when the vehicle arrives at the registered home, the vehicle control process may be terminated.

1 vehicle control unit 11 storage unit (running data storage means)
12 Probability distribution calculation unit (probability distribution calculation means)
13 Prediction unit (data prediction means)
14 Control unit (powertrain control means)
2 Inverter 3 Battery 4 Navigation system 4a Storage unit 4b Calculation unit 5 Communication unit 6 Vehicle speed sensor 7 SOC monitoring unit 8 Data center
Eng engine
MG motor / generator
CL1 1st clutch
CL2 2nd clutch
AT automatic transmission
RL, RR Left and right rear wheels (drive wheels)
FL, FR Front left and right wheels

Claims (3)

It is mounted on a hybrid vehicle equipped with an engine and a motor as a travel drive source,
Data storage means for storing travel data of the host vehicle in each travel section when traveling on a predetermined travel route divided into a plurality of travel sections;
The second travel that is the next travel section of the first travel section with respect to the first travel data in the first travel section of the plurality of travel sections from the travel data stored in the data storage means. A probability distribution calculating means for obtaining a relation of the second running data in the section by a probability distribution;
Data for calculating predicted travel data in the second travel section using travel data having the highest probability among the probability distributions obtained by the probability distribution calculation means when traveling on the predetermined travel route. Prediction means,
Power train control means for performing power train control of the host vehicle based on the predicted travel data predicted by the data prediction means;
Equipped with a,
When the actual travel data of the host vehicle in the first travel section is different from the travel data having the highest probability in the probability distribution, the data prediction unit uses the actual travel data of the host vehicle. A control apparatus for a hybrid vehicle, wherein the predicted travel data in the second travel section is calculated . In the hybrid vehicle control device according to claim 1 ,
Communication means for acquiring the travel data from a data center that stores travel data of vehicles other than the host vehicle in each travel section when traveling on a predetermined travel route divided into the plurality of travel sections;
The data prediction means includes the second data for the first travel data from travel data of vehicles other than the host vehicle acquired via the communication means in addition to the travel data stored in the data storage means. A control apparatus for a hybrid vehicle, characterized in that a relation of travel data is obtained by a probability distribution. In the hybrid vehicle control device according to claim 1 or 2 ,
The hybrid vehicle control device, wherein the travel data is vehicle speed data.