Driving force control method of vehicle and driving force control device of vehicle

Abstract

To balance suppression of energy consumption with acceleration characteristic a driver requests.SOLUTION: A driving force control method of a vehicle is given in which a controller (1) creates characteristic of acceleration or deceleration request of a vehicle owner per traveling route based on travel history of the vehicle owner, creates a request torque characteristic model which models a relation between accelerator opening degree and torque based on the characteristic of acceleration or deceleration request of the vehicle owner, and controls driving force based on the request torque characteristic model.SELECTED DRAWING: Figure 6

Description

[Technical field]

[0001] The present invention relates to a driving force control method and a driving force control device for a vehicle. [Background technology]

[0002] In order to extend the cruising distance of a vehicle, it is necessary to reduce the energy required for traveling. For example, an electric vehicle has a characteristic that the driving force is highly responsive to the driver's accelerator pedal depression operation due to the characteristics of the electric motor, but because of this excellent responsiveness, the vehicle may accelerate more than the driver intends when starting off, which may result in the need to decelerate, resulting in excessive energy consumption. Patent Document 1 discloses a technology for suppressing the above-mentioned excessive energy consumption by detecting the amount of depression of the accelerator pedal by the driver per predetermined time when starting off, and notifying the driver if the amount of depression is equal to or greater than a predetermined value. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2018-107935 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, the technology described in the above document only informs the driver that the accelerator pedal is depressed too much, and does not directly affect the driving force generated according to the accelerator pedal depression. Therefore, there is room for improvement in the effect of suppressing energy consumption. In addition, the technology described in the above document has a fixed predetermined value that is used as a criterion for determining whether the accelerator pedal depression is excessive, so although it can suppress the accelerator pedal depression by the driver, it may not be possible to obtain the acceleration feeling that the driver desires.

[0005] SUMMARY OF THE PRESENT EMBODIMENT An object of the present invention is to achieve both reduction in energy consumption and acceleration characteristics required by the driver. [Means for solving the problem]

[0006] According to one aspect of the present invention, a method for controlling driving force of a vehicle is provided, in which a controller generates characteristics of the acceleration and deceleration requirements of the vehicle owner for each driving route based on the driving history of the vehicle owner, generates a required torque characteristics model that models the relationship between accelerator opening and torque based on the characteristics of the owner's acceleration and deceleration requirements, and controls the driving force using the generated required torque characteristics model. Effect of the Invention

[0007] According to each of the above aspects, it is possible to achieve both the suppression of energy consumption and the acceleration characteristics required by the driver (owner). [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a schematic diagram of a control system according to an embodiment of the present invention. [Diagram 2] FIG. 2 is a diagram showing a configuration of a main part of the controller. [Diagram 3] FIG. 3 is a flowchart showing a control routine executed by the controller to generate the optimum required torque characteristic model. [Figure 4] FIG. 4 is a flowchart showing a control routine executed by the controller after the vehicle is started and after the generation of the optimum required torque characteristic model. [Diagram 5] FIG. 5 is a flowchart showing a control routine executed by the controller upon arrival at the destination. [Figure 6] FIG. 6 is an example of a time chart of the required torque. [Figure 7] FIG. 7 is an example of a time chart of the vehicle speed and acceleration when the torque control is performed based on the optimum required torque characteristic model. [Figure 8]FIG. 8 is a diagram showing the effect of improving fuel economy according to the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0010] Fig. 1 is a schematic diagram of a control system according to an embodiment of the present invention. The controlled object in this embodiment is an electric vehicle (BEV: Battery Electric Vehicle) that uses an electric motor as a drive source. However, the present invention can also be applied to a series-parallel hybrid vehicle.

[0011] The control system includes a controller 1 that performs control described below, a motor controller 2 that controls an electric motor (not shown) in response to commands from the controller 1, a navigation system 3, and an advanced driver assistance system (ADAS) 4. The controller 1 is composed of one or more microcomputers that include a central processing unit (CPU), a read-only memory (ROM), a random access memory (RAM), and an input / output interface (I / O interface).

[0012] 2 is a diagram showing the configuration of the main parts of the controller 1. Note that this configuration refers to a functional configuration, not a physical configuration. In addition, this configuration is not limited to being configured in a computing device mounted on a vehicle, and may be configured on a cloud, for example.

[0013] The main components of the controller 1 include a learning section 10 , a virtual VCM, an energy prediction model 12 , and a required torque map 13 .

[0014] The learning unit 10 reads the driving history of the vehicle owner. The driving history includes at least information on the driving environment (hereinafter also referred to as driving environment information) and a vehicle speed change pattern. In addition, each change pattern of the required torque and the accelerator opening degree (also referred to as APO opening rate) is included. The driving environment information includes at least the width, gradient, and speed limit of the driving route, and the distance from the preceding vehicle.

[0015] If the vehicle speed change pattern is known, not only the transition of the vehicle speed but also the magnitude of acceleration / deceleration and average vehicle speed can be known. In other words, the driving characteristics of the owner can be understood from the vehicle speed change pattern.

[0016] The width, gradient, and speed limit of the travel route are obtained from the navigation system 3. The following distance is obtained from the ADAS 4. Data on the vehicle speed, required torque, and APO opening rate are stored in a memory (not shown) of the controller 1.

[0017] The virtual VCM 11 is a virtual device provided in the controller 1 that performs the same function as an actual vehicle controller (VCM: Vehicle Control Module) (not shown). While the actual VCM controls the electric motor in response to the driver's accelerator operation, the virtual VCM 11 executes calculations to further improve the fuel economy performance behind the scenes of the control of the actual VCM. The calculations will be described later.

[0018] The energy prediction model 12 models the energy (electricity in the case of an electric vehicle) used or recovered when the vehicle runs, such as the energy required for acceleration, the energy that can be regenerated during deceleration, the energy required to maintain the vehicle speed, etc. The required torque map 13 is a map for outputting the required torque using the vehicle speed and the APO aperture rate as input values.

[0019] In the above configuration, the learning unit 10 generates characteristics of the acceleration / deceleration request of the owner along a driving route to a place that the owner frequently visits, and based on this generates a required torque characteristic model (a model of the relationship between accelerator opening and torque) suitable for the owner to travel that driving route. Hereinafter, this required characteristic model is also referred to as an adapted required torque characteristic model. The places that the owner frequently visits are input by the owner. Note that by linking with a smartphone or the like, it may be possible to determine whether or not the owner is the owner, and to read in the destinations that the owner frequently visits.

[0020] Then, by performing reinforcement learning using the characteristics of the owner's acceleration / deceleration requirements, the adapted required torque characteristics model, and the energy prediction model 12, a required torque characteristics model (hereinafter also referred to as an optimized required torque characteristics model) is generated that can reduce energy consumption while satisfying the owner's acceleration / deceleration requirements when the owner travels along the driving route.

[0021] By controlling the driving force based on the optimized torque requirement characteristic model thus generated, it is possible to improve the fuel economy without causing the owner dissatisfaction with insufficient acceleration force or the like.

[0022] Next, generation of the appropriate requested torque and control related thereto will be described.

[0023] Fig. 3 is a flowchart showing a control routine executed by the controller 1 to generate an optimum torque requirement characteristic model. Fig. 4 is a flowchart showing a control routine executed by the controller 1 after the vehicle is started after the optimum torque requirement characteristic model is generated. Fig. 5 is a flowchart showing a control routine executed by the controller 1 when the vehicle arrives at the destination. The steps of the flowchart will be described below.

[0024] In step S100, the controller 1 sets a destination that the owner regularly visits. Specifically, the controller 1 reads a destination input by the owner and sets it as the destination. A regularly visited destination is, for example, a workplace, a station used for commuting, etc.

[0025] In step S110, the controller 1 reads the driving history for the past few days.

[0026] In step S120, the controller 1 determines, from the driving history, a driving route that is habitually used as a driving route to the destination set in step S100 (hereinafter also referred to as a frequent driving route).The controller 1 then determines the width, gradient, speed limit, etc. of the frequent driving route, the change patterns of the vehicle speed, required torque, and APO opening rate when traveling on the frequent driving route, and the distance from the preceding vehicle.Note that since the required torque and the APO opening rate have a one-to-one correspondence, it may seem that only one of them needs to be determined, but since the characteristics of the torque relative to the APO opening rate are changed by learning for adaptation and optimization, which will be described later, both are determined.

[0027] In step S130, the controller 1 generates an adapted required torque characteristic. Specifically, the required torque characteristic is generated by machine learning using the data determined in step S120, and the map constants of the previously stored required torque characteristic (hereinafter also referred to as initial required torque characteristic) are corrected based on the generated required torque characteristic. In the machine learning, the width, gradient, speed limit, and following distance of the frequently used driving route are set as first input data, and the vehicle speed change pattern (vehicle speed and acceleration / deceleration) when the frequently used driving route is driven is set as second input data. Then, the change patterns of the required torque and the APO opening rate when the frequently used driving route are set as teacher data, and repeated calculations are performed.

[0028] In step S140, the controller 1 judges whether the generation of the adapted required torque characteristic model has been completed. If it has been completed, the process proceeds to step S150. If it has not been completed, the process returns to step S130. Here, the above-mentioned calculation is performed a predetermined number of times, and if the deviation between the learning result and the initial required torque characteristic becomes equal to or smaller than a predetermined value, the generation is judged to be completed. The predetermined number of calculations is not particularly limited, and can be set arbitrarily from the viewpoint of performing more accurate learning. In this embodiment, it is set to, for example, 20 to 30 times. The predetermined value for the deviation is also not particularly limited. The deviation amount is used as the judgment criterion in order to eliminate obviously erroneous learning results, and is set arbitrarily based on this viewpoint.

[0029] The processes in steps S110 to S140 above are the machine learning carried out by the learning unit 10.

[0030] In step S150, the controller 1 switches to an optimization mode in which the adapted required torque characteristic model is optimized. The optimization is a mode in which an optimized required torque characteristic model is generated by performing reinforcement learning using the adapted required torque characteristic model and the energy prediction model 12, which can suppress energy consumption when traveling a frequently used traveling route, and more preferably minimizes energy consumption.

[0031] In step S160, the controller 1 performs learning for optimization. Specifically, the width, gradient, and speed limit of the frequently used driving route, and the average vehicle speed when the frequently used driving route was driven in the past are set as the first input data, and the vehicle speed change pattern (vehicle speed and acceleration / deceleration) and the vehicle distance when the frequently used driving route was driven are set as the second input data. Then, the optimized torque requirement characteristic model is generated by performing reinforcement learning using these input data and the energy prediction model. Note that, when generating the optimized torque requirement characteristic model, the acceleration may not be suppressed until the first peak of acceleration, and emphasis may be placed on reducing the energy consumption after the peak. This is because if the initial acceleration is suppressed, the owner may feel that the acceleration is insufficient and may step on the accelerator more, resulting in the energy consumption not being suppressed.

[0032] In step S170, the controller 1 determines whether the optimization is complete, and if so, ends this routine, and if not, returns to the process of step S160. In this step, it is determined that the optimization is complete when the reinforcement learning calculation has been performed a predetermined number of times. The predetermined number here is not particularly limited, as in step S140.

[0033] The above-mentioned processes in steps S150 to S170 are the reinforcement learning carried out in the main part of the controller 1 shown in FIG.

[0034] By the above control routine, an optimum torque requirement characteristic model for the travel route to the set destination is generated before the vehicle is started. If multiple destinations are set, an optimum torque requirement characteristic model for each of the frequently used travel routes to each destination is generated.

[0035] After the optimum required torque characteristic model is generated, when the vehicle is started, the controller 1 executes the control routine of FIG.

[0036] In step S200, the controller 1 determines whether or not re-learning is necessary, and if not necessary, proceeds to the process of step S210, and if necessary, proceeds to step S260, where the control routine of Fig. 3 is executed again. In this step, it is determined that re-learning is unnecessary if a predetermined period has not passed since the previous optimization, or if the number of vehicle starts since the previous optimization has not reached a predetermined number. This is based on the idea that using newer data allows optimization that is more suitable for the current owner, and the predetermined period and the predetermined number are not particularly limited and can be set arbitrarily.

[0037] In step S210, the controller 1 proposes the optimized torque characteristic model to the owner as a new characteristic model. For example, the proposal is displayed as text information on a display unit such as an instrument panel. Together with this proposal, an icon for deciding whether or not to adopt the new characteristic model or an operation method for making the decision is also displayed.

[0038] In step S220, the controller 1 determines whether or not the owner has adopted the new characteristic model. If the owner has adopted the new characteristic model, the process proceeds to step S230, and if the owner has not adopted the new characteristic model, the process proceeds to step S270.

[0039] In step S230, the controller 1 switches the pre-stored characteristic model to the optimum required torque characteristic model, that is, changes the map constants of the required torque map.

[0040] In step S240, the controller 1 determines whether or not there is a mode switching request, and if there is, the process proceeds to step S250, otherwise this routine ends. A mode switching request means that, in a vehicle having multiple driving modes with different output characteristics such as a sports mode, a standard mode, an eco mode, etc., the owner performs an operation to switch from the currently selected mode to another mode.

[0041] In step S250, the controller 1 switches to the specified mode.

[0042] In step S270, the controller 1 maintains the characteristic model before optimization.

[0043] In step S280, the controller 1 determines whether or not there is a mode switching request, and if there is, the process proceeds to step S250, and if there is no mode switching request, the present routine is terminated.

[0044] After this routine is completed, the vehicle continues to travel in the characteristic model and travel mode resulting from the processing of steps S220 to S250, and the control routine shown in FIG. 5 is executed.

[0045] In step S300 in FIG. 5, the controller 1 determines whether or not the destination has been reached. If the destination has been reached, the process proceeds to step S310. If the destination has not been reached, the process of this step is repeated until the destination is reached.

[0046] In step S310, the controller 1 proposes changing the map constants of the characteristic model to those of the optimized required torque characteristic model in the same manner as in step S210.

[0047] In step S320, the controller 1 determines whether or not the owner has decided to modify the memory, and if so, proceeds to the process of step S330, and if not, proceeds to the process of step S340.

[0048] When changing the memory, the controller 1 updates the characteristic model stored in the memory to the optimized required torque characteristic model in step S330. When not changing the memory, the controller 1 holds the characteristic model currently stored in the memory in step S340, and performs re-learning by executing the control routine of FIG. 3 again in step S350.

[0049] Next, the effects obtained by carrying out the above-mentioned control will be described with reference to FIGS.

[0050] A required torque characteristic model (adapted required torque characteristic model) suited to the acceleration / deceleration characteristics of the owner when traveling a frequently used driving route is generated by the processing of steps S100 to S130 in Fig. 2. The effect obtained by this is shown in Fig. 6.

[0051] Fig. 6 is an example of a time chart of the required torque when driving based on the initial required torque characteristic (top), and when driving based on the adapted required torque characteristic model (bottom). The dashed line in the bottom represents the waveform when driving based on the initial required torque characteristic, i.e., the waveform in the top.

[0052] In the driving scenario shown in Figure 6, after the owner starts moving, it is determined that the owner has accelerated too much, so the APO opening rate is reduced, and then it is determined that the APO opening rate has been reduced too much, so the APO opening rate is adjusted in the increasing direction and the vehicle transitions to constant speed driving. If torque control is performed based on the initial required torque characteristic model in this driving scenario, the required torque increases or decreases according to the change in the APO opening rate described above. As a result, energy is wasted in the part where there is excessive acceleration (area A in the figure) and in the part where there is excessive deceleration and then re-acceleration (area B in the figure).

[0053] In contrast, if torque control is performed based on an adapted required torque characteristic model adapted to the driving characteristics of the owner, excessive acceleration will not occur even if the APO opening rate at start is the same, so the required torque will not increase once and then decrease in area A. Also, when transitioning to low-speed driving, excessive deceleration will not occur in response to the accelerator operation of the owner, so there will be no re-acceleration in area B. In other words, unnecessary energy consumption in areas A and B can be suppressed.

[0054] Furthermore, in this embodiment, the adapted required torque characteristic model is optimized from the viewpoint of reducing energy consumption by the processes of steps S150 to S170 in Fig. 2. The effect of this is shown in Fig. 7.

[0055] 7 is an example of a time chart of vehicle speed and acceleration when torque control is performed based on the appropriate torque requirement model. The solid line in the figure shows the case of the appropriate torque requirement model, and the dashed line shows the case of the initial torque requirement model.

[0056] As described above, excessive acceleration against the owner's intention is suppressed, so that overshooting of the vehicle speed when transitioning to constant speed driving is suppressed (area C in the figure, etc.). Furthermore, until the first peak after acceleration starts, acceleration is equivalent to that in the case of the initial required torque characteristic model (areas D and E in the figure). In other words, by suppressing overshooting of the vehicle speed, wasteful energy consumption can be suppressed without causing dissatisfaction to the owner due to insufficient acceleration force, etc. Furthermore, when decelerating, for example, before an intersection or a corner, a strong deceleration G is continuously maintained (area F in the figure). This makes it possible to increase the amount of energy regeneration.

[0057] As described above, torque control based on the optimized torque requirement model can suppress unnecessary energy consumption and increase the amount of energy regeneration, thereby improving fuel economy. According to a simulation assuming a typical driving route used for commuting, etc., the result was that the fuel economy improved by about 3% to 6% compared to the case where torque control based on the initial torque requirement model was performed (see Figure 8).

[0058] When updating the memory with the optimized required torque characteristic model, the amount of memory required increases as the number of frequently used driving routes increases, for example, for commuting, shopping, picking up and dropping off family, etc. For this reason, it is desirable to use cloud-based memory with ample capacity as the memory for storing the required torque characteristics, rather than on-board memory.

[0059] As described above, in this embodiment, the controller 1 generates the characteristics of the acceleration / deceleration request of the vehicle owner for each driving route based on the driving history of the vehicle owner, generates an adapted required torque characteristic model (required torque characteristic model) that models the relationship between the APO opening rate (accelerator opening) and torque based on the characteristics of the acceleration / deceleration request of the owner, and controls the driving force using the generated required torque characteristic model. This enables control that is more in line with the acceleration / deceleration request of the owner than controlling the driving force based on the initial required torque characteristic generated by assuming the acceleration / deceleration request characteristic of a general driver, thereby reducing energy consumption.

[0060] In this embodiment, the above-mentioned driving route is a driving route that the owner uses on a daily basis, and the above-mentioned driving history is information on at least a change pattern of the vehicle speed and a driving environment. That is, according to this embodiment, it is possible to control the driving route that the owner uses on a daily basis based on a required torque characteristic model dedicated to the owner, so that the effect of suppressing energy consumption is greater.

[0061] In this embodiment, the controller 1 generates an optimized required torque characteristic model capable of suppressing energy consumption for each driving route by performing reinforcement learning using the adapted required torque characteristic model and an energy management model that predicts the energy consumption of the vehicle, and controls the driving force using the optimized required torque characteristic model. This not only satisfies the acceleration / deceleration request of the owner, but also makes it possible to further suppress the energy consumption.

[0062] In this embodiment, the controller 1 notifies the owner that the optimized request torque characteristic model has been generated, and controls the driving force using the optimized request torque characteristic model only when the owner permits the use of the optimized request torque characteristic model, thereby enabling the driving force control to reflect the owner's intention.

[0063] In this embodiment, the driving route that the owner uses daily is the driving route to the destination input by the owner, so that it is possible to reduce energy consumption for the driving route that the owner desires.

[0064] In this embodiment, when the vehicle owner ends a trip, the owner can select whether or not to store the optimized torque characteristic model and use it the next time the vehicle is driven. This allows the owner to keep only the models that the owner actually drives and likes.

[0065] Although the embodiments of the present invention have been described above, the above-mentioned embodiments merely show some of the application examples of the present invention, and it is not intended that the technical scope of the present invention be limited to the specific configurations of the above-mentioned embodiments. [Explanation of symbols]

[0066] 1 Controller, 2 Motor controller, 3 Navigation system, 4 Driving assistance system, 10 Learning unit, 11 Virtual VCM, 12 Energy prediction model, 13 Torque requirement map

Claims

1. 1. A method for controlling driving force of a vehicle, comprising: The controller generating characteristics of an acceleration / deceleration request of a vehicle owner for each driving route based on the driving history of the vehicle owner; generating a required torque characteristic model that models a relationship between an accelerator opening and torque based on the characteristics of the acceleration / deceleration request of the owner; The driving force is controlled by using the required torque characteristic model. A method for controlling driving force of a vehicle.

2. 2. The vehicle driving force control method according to claim 1, the driving route is a driving route that the owner uses on a daily basis, The driving history is information about at least a change pattern of a vehicle speed and a driving environment. A method for controlling driving force of a vehicle.

3. 2. The vehicle driving force control method according to claim 1, The controller: generating an optimized required torque characteristic model capable of suppressing the energy consumption for each of the travel routes by performing reinforcement learning using the required torque characteristic model and an energy management model that predicts the energy consumption of the vehicle; controlling the driving force using the optimized torque requirement characteristic model; A method for controlling driving force of a vehicle.

4. 4. The method for controlling driving force of a vehicle according to claim 3, the controller notifies the owner that the optimized request torque characteristic model has been generated, and controls the driving force using the optimized request torque characteristic model only when the owner permits use of the optimized request torque characteristic model. A method for controlling driving force of a vehicle.

5. 3. The method for controlling driving force of a vehicle according to claim 2, The driving route that the owner regularly uses is a driving route to a destination input by the owner. A method for controlling driving force of a vehicle.

6. 4. The method for controlling driving force of a vehicle according to claim 3, The owner can select whether or not to store the optimized torque characteristic model and use it the next time the vehicle is driven when the vehicle is driven. A method for controlling driving force of a vehicle.

7. In a driving force control device for a vehicle, A driving force control device for a vehicle, comprising: a controller that generates characteristics of an owner's acceleration / deceleration requirements for each driving route based on the driving history of the owner of the vehicle; generates a characteristic model that models the relationship between accelerator opening and torque based on the characteristics of the owner's acceleration / deceleration requirements; and controls the driving force using the characteristic model.

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