JP6775637B1 - Vehicle control device and vehicle control method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a vehicle control device and a vehicle control method for improving search time and search accuracy in a total input power minimum search and improving fuel consumption or electricity cost while conditions for vehicle operation by a plurality of power sources are satisfied. provide. SOLUTION: Of the torques required for an engine and a motor as a drive source, which is a search parameter that can search for an operating point that minimizes the total input power in a short time is selected, and the selected search parameter is selected. The total input power minimum search control is performed using, and the distribution of torque required for each drive source is calculated. [Selection diagram] Fig. 2

Description

The present application relates to a vehicle control device and a vehicle control method.

In recent years, vehicles that obtain power from a plurality of drive sources such as an engine and a motor, for example, a hybrid vehicle, have become widespread as vehicles that consider energy saving and the environment. Hybrid vehicle driving modes include the "EV (Electric Vehicle) mode", which runs on the power of the motor without operating the engine, the "series mode", which runs on the power of the motor while generating electricity using the engine, and the engine and motor. There is a "parallel mode" that runs on both of these motors, and the switching of the running mode is controlled according to the vehicle conditions.

In the parallel mode in which the vehicle travels with the power of both the engine and the motor, the output power of the vehicle (power of output) required by the driver, that is, the torque borne by each of the engine and the motor in order to obtain the torque of the vehicle ( Torque distribution) must be determined and indicated as the required torque. At that time, if the torque distribution is not determined in consideration of the fuel and electric power that must be input in order to obtain the required torque of the engine and the motor, that is, the power of the input, the fuel and electric power are consumed more than necessary. There is a problem that the fuel consumption is deteriorated because the operation is performed in an inefficient state. Therefore, it is necessary to control to determine the required torques in consideration of the efficiency of the engine and the motor.

For example, in Patent Document 1, when the required power is larger than the reference power at which the thermal efficiency of the engine is the optimum value, the fuel consumption of the vehicle is calculated using the motor assist power as a parameter, and when this fuel consumption becomes the minimum value. A technique for searching for the motor assist power of the engine, setting the searched value to the optimum motor assist power, and controlling the distribution of torque between the engine and the motor is disclosed.

JP-A-2018-134901

The problem in the control for searching the minimum value of the vehicle fuel consumption amount or the input power disclosed in Patent Document 1 is the search time. Since the search is repeated until the minimum value is found by the predetermined algorithm during the calculation timing of the electronic control unit in the predetermined time cycle, if the algorithm or the setting value used at the time of the search is not appropriate, the search time becomes long, and during that time. Other processing will not be possible. In order to prevent such a situation, the limit time or the limit number of repetitions of the search is set at the time of search, and if it exceeds that, the search is terminated halfway even if the minimum value is not found, and other processing is performed. It is common to secure time for this. Instead, when the search ends due to the limit time or the limit number of repetitions, the fuel consumption effect obtained from the search result becomes small.

Here, the gradient method can be mentioned as one of the algorithms for searching the minimum value. The gradient method searches for a solution using information about the gradient of a given function. For example, in the steepest descent method, which is one of the gradient methods, the value corresponding to the slope (first-order differentiation) of a predetermined function is subtracted from the current value, and it is calculated until the change of the variable becomes sufficiently small or becomes 0. Iteratively calculate to find the minimum value. If the value corresponding to the slope of this predetermined function is smaller than necessary, the number of iterative calculations until the solution of the minimum value is found becomes large, and the search time becomes long. On the other hand, if the value corresponding to the slope of the predetermined function is larger than necessary, the solution of the minimum value may not be approached or may be moved away even by iterative calculation.

Further, the above-mentioned predetermined function in the control for searching for the minimum value of the fuel consumption amount or the input power is an expression obtained by arranging the relational expression of the fuel consumption amount or the input power with the required torque of each drive source as a variable. .. That is, the distribution of the required torque of each drive source is determined in consideration of the input power. Here, the required torque of the vehicle is preset according to the current state of the vehicle and the operation of the driver, and is the total of the required torques for each drive source. That is, among the required torques of the plurality of power sources, the required torque of one power source can be converted into the relational expression between the required torque of the other power source and the required torque of the vehicle. Therefore, the above-mentioned predetermined function in the control for searching the minimum value of the fuel consumption amount or the input power is an expression with the required torque of (number of power sources-1) as a variable.

Here, the search time in the control for searching the minimum value may change depending on which power source required torque is used as the relational expression between the required torque of another power source and the required torque of the vehicle. For example, when there are two power sources, an engine and a motor, the search time is shorter when the required torque of the engine is used as a parameter at one operating point, and the search time is shorter when the required torque of the motor is used as a parameter at other operating points. May be short. In such a case, if the search logic mounted on the electronic control unit is constructed with the required torque of either one as a parameter, a case may occur in which the search time is long during the operation of the search logic, and the search limit time or limit repetition occurs. There is a possibility that the obtained fuel efficiency effect will be reduced due to the end of the search depending on the number of times.

Here, in the minimum value search disclosed in Patent Document 1, the motor assist power is changed to search for the motor assist power when the vehicle fuel consumption becomes the minimum value, and the searched value is the optimum motor assist. Since it is set as power, there is a possibility that the search time may be long and the fuel consumption effect may be reduced.

The present application discloses a technique for solving the above-mentioned problems, and improves the search time and search accuracy in the total input power minimum search while the conditions for vehicle operation by a plurality of power sources are satisfied. It is an object of the present invention to provide a vehicle control device and a vehicle control method for improving fuel efficiency or electricity cost.

The vehicle control device disclosed in the present application is a vehicle control device equipped with a plurality of drive sources, and searches for torque required for the first drive source and the second drive source constituting the plurality of drive sources. As parameters, a total input work rate minimum search control means for searching for an operating point that minimizes the total input work rate of the plurality of drive sources, and a first drive source request searched by the total input work rate minimum search control means. and torque distribution control means for operating the vehicle by the torque and the second drive source required torque, the gradient method the gradient of the total input power rate of each of the first driving source required torque and the second drive source required torque as a parameter A parameter having a large gradient between the gradient of the total input power with the first drive source required torque as a parameter and the total input power with the second drive source required torque as a parameter is the total input power. A search parameter selection means for selecting as a search parameter in the minimum search control means is provided.
The search parameter selected by the search parameter selection means is used to operate the total input power minimum search control means.

According to the vehicle control device disclosed in the present application, the search time and search accuracy in the total input power minimum search can be improved while the conditions for vehicle operation by a plurality of power sources are satisfied. Therefore, it is possible to operate the vehicle with the minimum necessary fuel or electric power in response to the driver's request and improve fuel efficiency or electric power cost.

It is a block diagram which shows the whole system including the control device of the hybrid vehicle which concerns on Embodiment 1. FIG. It is a block diagram which shows the structure and function of the control device of the hybrid vehicle which concerns on Embodiment 1. FIG. It is a flowchart which shows the operation of the search parameter selection means of the electronic control unit for vehicle control in the control device of the hybrid vehicle which concerns on Embodiment 1, the motor driveable maximum torque calculation means, and the search parameter learning means. It is a figure which showed the example of the map which trains the search parameter by the search parameter learning means in the control device of the hybrid vehicle which concerns on Embodiment 1. FIG. It is an image diagram which showed the efficiency characteristic of the engine and the motor of the control device of the hybrid vehicle which concerns on Embodiment 1. FIG. It is a timing chart which shows the operation of the search parameter selection means of the electronic control unit for vehicle control in the control device of the hybrid vehicle which concerns on Embodiment 1, the motor driveable maximum torque calculation means, and the search parameter learning means. It is a figure which showed the example of the difference in the number of searches by the difference in the search parameters of the control device of a hybrid vehicle and the control device of a conventional hybrid vehicle which concerns on Embodiment 1. FIG. It is a block diagram which shows the whole system including the control device of the electric vehicle which concerns on Embodiment 2.

Hereinafter, preferred embodiments of the vehicle control device and the vehicle control method according to the present application will be described with reference to the drawings. In addition, while the conditions for vehicle operation by a plurality of power sources are satisfied, the total input power minimum search is performed by using the search parameter learning value or the parameter with a large gradient as the search parameter in the total input power minimum search. An embodiment of calculating the distribution of torque in the power source will be described.

Embodiment 1.
FIG. 1 is a configuration diagram showing the entire system including the control device for the hybrid vehicle according to the first embodiment. Further, FIG. 2 is a block diagram showing a configuration and a function of a control device for a hybrid vehicle according to the first embodiment, and each means is stored in an electronic control unit for vehicle control described later.

In FIG. 1, the engine 101, which is the first drive source, and the motor 102, which is the second drive source, are connected by a crank pulley 103 of the engine 101, a pulley 104 of the motor 102, and a belt 105. The power of the engine 101 and the motor 102 is transmitted to the tire 109 connected to the drive shaft 108 via the transmission 106 and the differential gear 107.

The motor 102 is connected to a battery (not shown) via an inverter (not shown), and generates electric power by power transmission from the engine 101 or power transmission from the drive shaft 108 when the vehicle is decelerating. While supplying power, power is transmitted to the drive shaft 108 by performing power running when the vehicle is accelerating. The motor control electronic control unit 110 (hereinafter, referred to as a motor ECU) is composed of a microprocessor or the like, and controls the motor 102 in an appropriate state. Further, the engine control electronic control unit 111 (hereinafter, referred to as an engine ECU) is composed of a microprocessor or the like, and controls the engine 101 in an appropriate state.

The vehicle control electronic control unit 112 (hereinafter referred to as a vehicle ECU) is composed of a microprocessor or the like, and uses sensor input signals such as a vehicle speed sensor 113, an accelerator position sensor 114, and a brake stroke sensor 115 to control a vehicle. The driving state and the driver's vehicle operation request are judged, and the vehicle is controlled to an appropriate state. In order to control the vehicle to an appropriate state, the vehicle ECU 112 includes the total input power minimum search control means 201, the torque distribution control means 202, the search parameter selection means 203, and the second drive source drive shown in FIG. A program or fixed value data relating to the motor driveable maximum torque calculation means 204 and the search parameter learning means 205, which are the possible maximum torque calculation means, is stored. Further, as will be described later, the engine required torque Te which is the first drive source required torque and the motor required torque Tm which is the second drive source required torque distributed by the torque distribution control means 202 are the engine ECU 111 and the motor ECU 110, respectively. The engine 101 and the motor 102 are properly controlled.

The total input power minimum search control means 201 shown in FIG. 2 uses the algorithm of the optimization problem by the gradient method to calculate the minimum total input power that realizes the torque of the vehicle required by the driver, and each power source. Calculate the required torque for. A specific calculation method will be described in the flow of control using FIG. 3 described later.

The torque distribution control means 202 transmits the required torque for each power source calculated by the total input power minimum search control means 201 to the ECUs of each power source, that is, the motor ECU 110 and the engine ECU 111.

The search parameter selection means 203 calculates the gradient of the total input power using the torque of each power source as a parameter, and selects a parameter having a large gradient as a search parameter in the total input power minimum search control means 201. A specific calculation method will be described in the flow of control using FIG. 3 described later.

The motor drive maximum torque calculation means 204 calculates the motor drive maximum torque Tmmx that can be output within the motor operation limit depending on the state of charge of the battery or within the motor operation limit for motor protection. A specific calculation method will be described in the flow of control using FIG. 3 described later.

The search parameter learning means 205 performs total input work on a map corresponding to the motor drive maximum torque Tmmx calculated by the motor drive maximum torque calculation means 204 and the operating point of the vehicle (wheel rotation speed Nw and vehicle required torque Tw). It is memorized and learned whether the search parameter in the rate minimum search control means 201 is the motor required torque Tm or the engine required torque Te. A specific calculation method will be described in the flow of control using FIG. 3 described later.

Hereinafter, in the hybrid vehicle control device configured as described above, a search parameter learning value or a parameter having a large gradient in the total input power minimum search is searched for while the conditions for vehicle operation by a plurality of power sources are satisfied. The operation of performing the minimum total input power search as a parameter and calculating the torque distribution in a plurality of power sources will be described.

First, each operation will be described with reference to the flowchart of FIG. This operation is executed as a subroutine in the main routine having a predetermined time cycle in the vehicle ECU 112.

In step S301, it is determined whether or not the vehicle is currently in the mode of vehicle operation by a plurality of power sources. For example, this is established when both the fuel for operating the engine 101 and the electric power (battery charged state) for operating the motor 102 when accelerating the vehicle are sufficient.

If it is determined in step S301 that the mode of vehicle operation by a plurality of power sources is not currently established, the processing of this routine is unnecessary and the subroutine is terminated as it is. If it is determined in step S301 that the mode of vehicle operation by a plurality of power sources is currently established, the process proceeds to the next step S302.

In step S302, the maximum torque that can be output is calculated within the motor operation limit depending on the state of charge of the battery or within the motor operation limit for motor protection. It is necessary to limit the drive of the motor 102 and protect the battery or the motor 102 when the battery charge state SOC is low or the temperature TMPM of the motor 102 is high. Therefore, for example, as in the following equation (1), the motor drive maximum torque Tmmx is calculated with the minimum value of the table value corresponding to each state.
Tmmx = min (table (SOC), table (TMPM)) ... (1)
This step S302 corresponds to the motor driveable maximum torque calculation means 204.
After step S302, the process proceeds to step S303.

In step S303, for example, the wheel rotation number Nw calculated from the vehicle speed Vv and the tire radius rt is calculated according to the following equation (2), the current vehicle speed Vv, and the driver's accelerator operation PosAPS. The required torque Tw is calculated by the following equation (3).
Nw = Vv ÷ rt × 60 ÷ (2π) ÷ 3.6 ... (2)
Tw = map (Vv, PosAPS) ... (3)

Then, as in the following equation (4), referring to the learning map according to the motor drive maximum torque Tmmx, the wheel rotation number Nw, and the vehicle required torque Tw calculated in step S302, the learning map value PrmL is not the initial value. That is, if the total input power minimum search control means 201, which will be described later, has been executed at the same operating point in the past, 1 is substituted for the learning flag FLGL to proceed to step S305, and the total is totaled with the search parameter of the learning map value PrmL. The input power minimum search control means 201 is executed. If the learning map value PrmL is the initial value in step S303, 0 is assigned to the learning flag FLGL and the process proceeds to step S304.
PrmL = map (Tmmx, Nw, Tw) ... (4)

The learning map of equation (4) has axes of the maximum motor drive torque Tmmx and the operating point of the vehicle (wheel rotation speed Nw and vehicle required torque Tw) as shown in FIG. 4, and is an initial value when not learned. 0 is input as. If the search parameter in the total input power minimum search control means 201 is the motor required torque, 1 is input as a value representing it, and the search parameter in the total input power minimum search control means 201 is the engine required torque. In some cases, 2 is input as a value representing it and the map is learned.

Further, the number of search parameters is (number of power sources-1), and since there are two power sources in this embodiment, it is possible to determine what is the search parameter, but there are three power sources. In the above case, since there are a plurality of search parameters, the learning map may be a map that can discriminate "parameters that are not search parameters".

In step S304, the search gradient SLM based on the motor required torque Tm and the search gradient SLE based on the engine required torque Te are compared, and the one having the larger gradient is selected as the search parameter. Here, each search gradient is a gradient calculation formula of the total input power minimum search control means 201, which will be described later, and is calculated as the following equations (5) and (6) as the gradient calculation of the steepest descent method, for example. Will be done.
SLM = α × ∂Pt / ∂Tm ・ ・ ・ (5)
SLE = β × ∂Pt / ∂Te ・ ・ ・ (6)
Equation (5) is obtained by multiplying the partial differential ∂Pt / ∂Tm of the motor required torque Tm with respect to the total input power Pt by the update coefficient α. Equation (6) is obtained by multiplying the partial differential ∂Pt / ∂Te of the engine required torque Te with respect to the total input power Pt by the update coefficient β.

Here, the motor required torque Tm or the engine required torque Te can be arranged by either variable using the vehicle required torque Tw, so that the partial differential ∂Pt / ∂Tm and the partial differential ∂Pt / ∂Te are Basically, the values are the same, but they may not be the same when calculating the slope of a minute section in the efficiency map used during the calculation. Further, the update coefficient α and the update coefficient β may be set to change depending on the conditions.

That is, when the operating point moves in the direction from "A" to "B" as shown in FIG. 5A, it can be seen that the amount of change in efficiency is large because the engine efficiency straddles a plurality of contour lines. As in (b), the amount of change in efficiency is not as large as the engine efficiency even if the operating point changes in the motor drive efficiency characteristics. In view of these characteristics, the update coefficient α and the update coefficient β are set according to the operating point as values that can balance the convergence accuracy and the convergence time of the total input power minimum search control means 201. For example, at the operating point of “A” in FIG. 5 (b), since the change in the motor drive efficiency characteristic is small, the gradient update coefficient α with the motor required torque Tm as a parameter is the engine required torque Te. It is set larger than the update coefficient β of the gradient used as a parameter.

From these facts, the search gradient SLE and the search gradient SLM have different values, and the search time changes depending on the search parameters. That is, the larger the search gradient, the larger the update per search, so that the total input power minimum search control means 201 converges faster. This step S304 corresponds to the search parameter selection means 203.

In step S305, the total input power minimum search in the total input power minimum search control means 201 is executed using the search parameters selected in step S303 or step S304. For example, the motor required torque Tm or engine required torque Te of the search parameter is updated with a gradient as in the above equation (5) or (6) for each subroutine in the main routine having a predetermined time cycle, and the search value convergence condition is obtained. The calculation is continued until the establishment or the predetermined number of searches is reached. In this way, the minimum value of the total input power Pt is calculated by the optimization problem algorithm based on the gradient method such as the steepest descent method.

Next, in step S306, the motor drive maximum torque Tmmx calculated in step S302, the wheel rotation speed Nw calculated in step S303, and the learning map value in the vehicle required torque Tw can be discriminated from the search parameters used in step S305. (For example, as described above, 1 is set when the engine required torque Te is the search parameter, 2 is set when the motor required torque Tm is the search parameter, and so on). Steps S306 and S303 correspond to the search parameter learning means 205.
After the end of step S306, the subroutine is terminated, and the torque distribution control means 202 for distributing and controlling the torque to the calculated engine required torque Te and motor required torque Tm is executed.
As described above, the search parameter is selected in step S304, and then the total input power minimum search is executed in step S305. Therefore, the motor required torque Tm and the engine required torque Te in the above equations (5) and (6) calculated to select the search parameter are currently stored in the memory, that is, one arithmetic cycle of the subroutine. It is the required torque of the motor and the required torque of the engine. Therefore, since each partial differential value uses these values, it becomes a unique value in one operation cycle of the subroutine.
Further, the update coefficients α and β for the motor required torque Tm and the engine required torque Te that change in each operation cycle of the subroutine are set in advance as described above.

In the hybrid vehicle control device according to the first embodiment described above, while the conditions for vehicle operation by a plurality of power sources are satisfied, a search parameter learning value or a parameter having a large gradient in the total input power minimum search is searched for. An execution example in which the minimum total input power search is performed as a parameter and the torque distribution in a plurality of power sources is calculated will be described with reference to the timing chart of FIG.

First of all, at the timing of time a, the vehicle required torque Tw increases in response to the accelerator being stepped on by the driver's acceleration request. Since the learning map value PrmL referenced from the motor drive maximum torque Tmmx, wheel rotation speed Nw, and vehicle required torque Tw at this timing was the initial value, the learning flag FLGL becomes 0. As a result of comparing the search gradient based on the motor required torque Tm and the search gradient based on the engine required torque Te, the motor required torque Tm becomes the search parameter Prm, and the total input power Pt is minimized by the minimum total input power search. Is calculated. At this time, the search parameter Prm is stored in the learning map and updated. Since the engine 101 and the motor 102 operate at the engine required torque Te and the motor required torque Tm, which are the total input power Pt, the vehicle speed is increased with the minimum necessary fuel and electric power in response to the driver's acceleration request.

Next, at the timing of time b, acceleration is continuously requested from time a, but since the maximum motor drive torque Tmmx, wheel rotation speed Nw, and vehicle required torque Tw have not changed significantly from time a, time a The map value learned in is referred to, and the learning flag FLGL becomes 1. Using the learning map value, that is, the motor required torque Tm as the search parameter Prm, the minimum total input work rate Pt is calculated by the total input power minimum search, and the engine is calculated with the engine required torque Te and the motor required torque Tm at that time. The 101 and the motor 102 operate. Therefore, the learning map can reduce the processing time when selecting search parameters and allow a margin in the search time in the total input power minimum search, so that the search for the minimum total input power Pt can be reliably calculated. Vehicle speed increases with the minimum amount of fuel and power required.

Next, at the timing of time c, an acceleration request larger than that of time a and time b is requested. Therefore, the maximum motor drive torque Tmmx, the wheel rotation speed Nw, and the vehicle required torque Tw change significantly, and the reference destination of the learning map is different from the time a and the time b.
Since the reference destination of the learning map at time c is the initial value, the learning flag FLGL becomes 0. Then, as a result of the search parameter selection, the engine required torque Te becomes the search parameter Pr, the total input power Pt that becomes the minimum by the total input power minimum search is calculated, and the search parameter Prm is stored in the learning map.
Here, the calculation result of the total input power minimum search, that is, the behavior in which the total input power Pt is different between the case of using the conventional search parameter and the case of using the search parameter of the present embodiment will be described with reference to FIG.

FIG. 7 shows the number of searches for the minimum total input power search at the timing of time c in FIG. 6 and the total input power Pt during the search. Conventionally, that is, when the motor required torque Tm is used as the search parameter, the gradient in the total input power minimum search is relatively small, and the total input power Pt during the search converges, that is, the value that minimizes the total input power is searched. It takes a lot of searches to complete. However, due to problems such as the processing time of the vehicle ECU 112, the search is forcibly terminated due to the limit on the number of searches. Therefore, the total input power Pt at that time is the total input power Pt at time c in FIG. Become.

On the other hand, in the present embodiment, the engine required torque Te is used as the search parameter instead of the motor required torque Tm, and the total input power Pt in FIG. 7 converges before the search number limit is limited. Therefore, the minimum value of the total input power Pt is the final total input power Pt at time c in FIG.

Therefore, at time c in FIG. 6, there is a difference in the total input power Pt, which is the search result, between the conventional method and the present embodiment, and as a result, the total input power minimum search in the present embodiment is performed more than before. By implementing the above, it is possible to operate the vehicle with the minimum required fuel and electric power and improve fuel efficiency.

At the timing of time d in FIG. 6, the search is performed using the search parameters learned at time c. In the conventional control, the search for the minimum total input power is not completed by the limit of the number of searches even at this point, and the total input power during the search is reached. However, since the total input power is gradually approaching the minimum value, after time d, the same result as the total input power Pt according to the present embodiment can be obtained.

At the timing of time e, since the destination for referring to the learning map has changed, the learning flag FLGL is temporarily set to 0, and the search gradient based on the motor required torque Tm and the search gradient based on the engine required torque Te are compared and selected. Using the search parameter Prm, the total input power Pt that is minimized by the minimum search for the total input power is calculated, and the search parameter Prm is stored in the learning map. As a result, vehicle speed increases with the minimum amount of fuel and power required.

As described in detail above, according to the hybrid vehicle control device and the hybrid vehicle control method according to the first embodiment, while the conditions for vehicle operation by the plurality of power sources, that is, the engine 101 and the motor 102 are satisfied, By performing the total input power minimum search with the parameter that increases the gradient in the total input power minimum search as the search parameter, the search time and search accuracy in the total input power minimum search can be improved. Therefore, it is possible to operate the vehicle with the minimum necessary fuel and electric power in response to the driver's request and improve fuel efficiency. In addition, by storing in the learning map the parameters that increase the gradient in the total input power minimum search once calculated, the processing time when selecting search parameters is reduced, and the search time in the total input power minimum search has a margin. Therefore, the search for the minimum total input power can be reliably calculated, the vehicle can be operated with the minimum required fuel and power, and fuel efficiency can be improved.

Embodiment 2.
Next, the vehicle control device and the vehicle control method according to the second embodiment will be described. In the first embodiment, a hybrid vehicle having an engine and a motor as a drive source has been described as an example, but it can also be applied to a motor and an electric vehicle having the motor as a drive source.
FIG. 8 is a configuration diagram showing the entire system including the vehicle control device according to the second embodiment in which both the first drive source and the second drive source are motors.
In FIG. 8, the power of the first motor 401, which is the first drive source, is transferred from the gear mechanism (not shown) to the tire 404 connected to the first drive shaft 403 via the first differential gear 402. Be transmitted. The power of the second motor 405, which is the second drive source, is transmitted from the gear mechanism (not shown) to the tire 408 connected to the second drive shaft 407 via the second differential gear 406.

The first motor 401 and the second motor 405 are connected to a battery (not shown) via an inverter (not shown), respectively, and the first drive shaft 403 and the second drive shaft 403 and the second motor 405 during vehicle deceleration are used. While power is generated and supplied by power transmission from the drive shaft 407, power is transmitted to the first drive shaft 403 and the second drive shaft 407 by performing power running operation when the vehicle is accelerating. The first motor control electronic control unit (hereinafter referred to as the first motor ECU) 409 and the second motor control electronic control unit (hereinafter referred to as the second motor ECU) 410 are a microprocessor or the like. The first motor 401 and the second motor 405 are controlled to an appropriate state. Other configurations are the same as those in the first embodiment, and duplicate description will be omitted by assigning the same reference numerals.

In the vehicle control device configured as described above, the search parameter learning value or the total input work is performed while the conditions for vehicle operation by the plurality of power sources, that is, the first motor 401 and the second motor 405 are satisfied. The total input power minimum search is performed with the parameter that increases the gradient in the rate minimum search as the search parameter, and the torque distribution in multiple power sources is calculated.
Regarding this calculation, the motor 102 of the first embodiment is replaced with the second motor 405, the engine 101 is replaced with the first motor 401, the motor ECU 110 is replaced with the second motor ECU 410, and the engine ECU 111 is replaced with the first motor ECU 409. This is the same as the calculation operation described in the first embodiment, and the description thereof will be omitted.

Similarly to the first embodiment, the second embodiment also has a gradient in the total input power minimum search while the conditions for vehicle operation by the plurality of power sources, that is, the first motor 401 and the second motor 405 are satisfied. By performing the total input power minimum search with the parameter that increases the total input power as the search parameter, the search time and search accuracy in the total input power minimum search can be improved. Therefore, it is possible to operate the vehicle with the minimum necessary electric power in response to the driver's request and improve the electric power cost. In addition, by storing the parameters that increase the gradient in the total input power minimum search once calculated in the learning map, the processing time when selecting the search parameters is reduced, and the search time in the total input power minimum search has a margin. Therefore, the search for the minimum total input power can be reliably calculated, the vehicle can be operated with the minimum required power, and the power cost can be improved.

In the first embodiment, a hybrid vehicle equipped with one engine and one motor as a plurality of drive sources has been described, and in the second embodiment, an EV vehicle equipped with two motors as a plurality of drive sources has been described. The present application can also be applied to an EV vehicle equipped with four other motors, or a hybrid vehicle equipped with an engine and two motors, and can be applied to a vehicle equipped with a plurality of drive sources.
Further, in the first embodiment, it is explained that the hybrid vehicle in which one engine and one motor are mounted as a plurality of drive sources each have an effect of improving fuel efficiency, and in the second embodiment, two as a plurality of drive sources. We have explained that EV vehicles equipped with a motor have the effect of improving electricity costs, but in this application, for example, for plug-in hybrid vehicles equipped with an engine and a motor as multiple drive sources and capable of external charging, fuel consumption and electricity costs It has the effect of improving.

As described above, although exemplary embodiments have been described in the present application, the various features, aspects, and functions described in the embodiments are not limited to the application of the specific embodiments, but are independent. It is applicable to embodiments in or in various combinations.
Therefore, innumerable variations not illustrated are envisioned within the scope of the techniques disclosed in the present application. For example, it is assumed that at least one component is modified, added or omitted.

101 engine, 102 motor, 103 crank pulley, 104 pulley, 105 belt, 106 transmission, 107 differential gear, 108 drive shaft, 109 tire, 110 motor ECU, 111 engine ECU, 112 vehicle ECU, 113 vehicle speed sensor, 114 Accelerator position sensor, 115 brake stroke sensor, 201 total input work rate minimum search control means, 202 torque distribution control means, 203 search parameter selection means, 204 motor driveable maximum torque calculation means, 205 search parameter learning means, 401 first Motor, 402 1st differential gear, 403 1st drive shaft, 404,408 tires, 405 2nd motor, 4062 differential gear, 407 2nd drive shaft, 409 1st motor ECU, 410 2nd ECU for motor.

Claims (3)

A vehicle control device equipped with multiple drive sources.
The torque required for the first drive source and the second drive source constituting the plurality of drive sources is used as a search parameter, and the total input power minimum for searching the operating point at which the total input powers of the plurality of drive sources are minimized. Search control means and
A torque distribution control means for operating the vehicle with the first drive source required torque and the second drive source required torque searched by the total input power minimum search control means, and
The total input power rate respectively calculated by the gradient method the gradient of the total input work rate as parameters, in which the said first driving source torque demand as parameters of the first drive source required torque and the second drive source required torque A search parameter selection means for selecting a parameter having a large gradient between the gradient of the above and the total input power with the second drive source required torque as a parameter as a search parameter in the total input power minimum search control means is provided.
A vehicle control device for operating the total input work rate minimum search control means using the search parameters selected by the search parameter selection means. The second drive source driveable maximum torque calculation means for calculating the driveable maximum torque of the second drive source, and
The second drive source driveable maximum torque calculation means and the search parameter learning means for learning the search parameters of the search parameter selection means according to the operating point of the vehicle are provided.
The vehicle control according to claim 1, wherein when the total input power at the operating point of the vehicle is not the minimum, the learned search parameters are used as the search parameters of the search parameter selection means from the next time onward. apparatus. It is a control method for a vehicle equipped with multiple drive sources.
The first step of searching for an operating point that minimizes the total input power of the plurality of drive sources , using the torques required for the first drive source and the second drive source constituting the plurality of drive sources as search parameters. ,
A second step of operating the vehicle with the first drive source required torque required for the first drive source and the second drive source required torque required for the second drive source, which are searched for in the first step.
The gradient of the total input power is calculated by the gradient method with each of the first drive source required torque and the second drive source required torque as parameters, and the total input power with the first drive source required torque as parameters. A third step of selecting a parameter having a large gradient between the gradient of the above and the total input power with the required torque of the second drive source as a parameter
A vehicle control method comprising searching for an operating point that minimizes the total input power using the parameters selected in the third step.

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