KR102665490B1 - Method of controlling power distribution of hybrid electric commercial vehicles - Google Patents

Abstract

In a parallel hybrid electric commercial vehicle in which the engine, engine clutch, and transmission are arranged in a row, a motor is located between the engine clutch and transmission, and a reducer is arranged behind the transmission, the engine and motor reflect the driver's will to accelerate or decelerate. A power distribution control method for a parallel hybrid electric commercial vehicle performed by an HCU (Hybrid Control Unit) that determines the power to be responsible for each and generates and outputs a command value corresponding to the determined power. The characteristics of the electric commercial vehicle (vehicle By distributing power to the drive source by reflecting the weight variability and slow response of the vehicle, optimal control can be performed to minimize the amount consumed from fuel according to the weight of the commercial vehicle, and compensate for the slow response of the commercial vehicle. A power distribution control method for a parallel hybrid electric commercial vehicle that can perform optimal control to have improved response performance is disclosed.

Description

{Method of controlling power distribution of hybrid electric commercial vehicles}

The present invention relates to a method for controlling power distribution of a hybrid vehicle, and particularly to a power distribution control method for a parallel hybrid electric commercial vehicle in which a powertrain including an engine, an engine clutch, and a transmission are arranged in a line, and a motor is arranged between the engine clutch and the transmission. It concerns a distribution control method.

There is a trend to strengthen vehicle exhaust gas regulations day by day to improve the air environment. However, existing power trains based on gasoline or diesel engines have limitations in meeting strengthened exhaust gas regulations. Accordingly, research and development on new power train systems are actively underway to meet increasingly stringent exhaust gas regulations.

One of the many technologies to meet increasingly stringent exhaust gas emission standards is the hybrid system. A hybrid system refers to a system in which the engine's output is assisted by an electric motor or the electric motor alone drives the vehicle. These hybrid systems can generally be divided into parallel hybrid and series hybrid.

Series hybrid is a method in which the engine is used only for power generation and the vehicle is driven only by the motor. The serial type has the advantage of having a relatively simple structure and simple control logic compared to the parallel type. However, the mechanical energy generated by the engine is converted into electrical energy, stored in the battery, and then used to drive the vehicle again, which has the disadvantage of being unreasonable in terms of energy efficiency.

On the other hand, the parallel type has the disadvantage of being relatively more complex in structure and control logic than the series type, but has the advantage of being able to use energy efficiently because it can use the mechanical energy of the engine and the energy of the battery at the same time. In addition, it is advantageous in terms of manufacturing cost as it can be designed without major changes to the drivetrain in existing internal combustion engine vehicles.

Accordingly, the number of development cases for parallel hybrids has recently been increasing. These parallel hybrids are classified into P0, P1, P2, P3, and P4 types depending on where the motor that cooperates with the engine or independently generates driving force for vehicle driving is located in the vehicle's power system. Depending on each type, Different power distribution control strategies are required.

Among them, the P2 type, as shown in FIG. 1, has a powertrain including an engine 10 and a transmission 30 arranged in a line on the coaxial line, and an engine clutch 20 between the engine 10 and the transmission 30. ) is interposed to control power transmission from the engine 10 to the transmission 30, and to allow power to be exchanged between the engine clutch 20 and the transmission 30 on the input shaft of the transmission 30. This refers to a system equipped with a motor (P2).

This P2 type hybrid system comprehensively considers the current vehicle speed, driver required output, SOC (State of Charge), etc. and provides engine mode, hybrid mode (engine + motor), and EV mode (Electric Vehicle Mode, pure motor output). Select the driving mode that can achieve optimal power efficiency and fuel efficiency under the current driving conditions (modes that drive the vehicle by itself) and switch to the selected driving mode to drive the vehicle.

Among these, when the EV mode, which drives the vehicle only with the output of the motor, is selected, the energy of the battery 70 is applied only to the motor P2 with the engine start turned off through cooperative control of multiple controllers in the vehicle, The vehicle is driven using only the output of the motor (P2). At this time, the engine clutch 20 is generally controlled to be OPEN so that the engine drag torque does not interfere with the output of the motor P2.

Reference numeral 50 indicates a starter motor. The start motor 50 is a motor capable of driving/generating, and provides power to the engine for starting the engine. In some cases, it is driven by engine power to generate power. This starter motor 50 can be connected to the engine 10 and a mechanical power transmission device such as a belt, chain, or gear, and the coupling ratio can be determined in various ways according to need.

And reference numeral 60 refers to a reducer that reduces the power shifted by the transmission 30 to a set reduction ratio and outputs it to the drive wheels (a pair of left and right rear wheels in the drawing).

Meanwhile, efforts are being made to electrify the powertrain not only for general passenger vehicles but also for commercial vehicles due to recent strengthening of fuel efficiency and carbon emissions regulations and ESG management. One of the attempts to implement powertrain electrification of commercial vehicles is an attempt to apply the aforementioned parallel hybrid system, which is only applied to general passenger vehicles, to commercial vehicles.

However, commercial vehicles have different characteristics from general passenger vehicles when controlling the optimal fuel efficiency of the powertrain (the weight of the vehicle varies depending on the loading condition, and the powertrain response varies greatly depending on the changing weight of the vehicle). A different driving control strategy is required than that of regular passenger vehicles. Nevertheless, as there is still no driving control technology that takes into account the characteristics of commercial vehicles, there is an urgent need to prepare countermeasures.

Korean Patent Publication No. 2022-0027308 (publication date 2022. 03.08.)

The technical problem that the present invention aims to solve is a hybrid electric vehicle that can perform optimal power distribution to the driving source (engine and motor) by reflecting the characteristics of electric commercial vehicles (vehicle weight variability and slow response) in control. The goal is to provide a power distribution control method for commercial vehicles.

The technical problem that the present invention aims to solve is to reflect the characteristics of electric commercial vehicles (vehicle weight variability and slow response) in control, so that even if the weight of the commercial vehicle changes, the amount of fuel consumed is optimized to match the changed weight. The goal is to provide a power distribution control method for a hybrid electric commercial vehicle that can perform control.

Another technical problem that the present invention aims to solve is to reflect the characteristics of electric commercial vehicles (vehicle weight variability and slow response) in the control, thereby compensating for the slow response of commercial vehicles and providing fuel efficiency with improved response performance. The goal is to provide a power distribution control method for a hybrid electric commercial vehicle that can perform optimal control.

According to one aspect of the present invention as a means of solving the problem,

In a parallel hybrid electric commercial vehicle in which the engine, engine clutch, and transmission are arranged in a row, a motor is located between the engine clutch and transmission, and a reducer is arranged behind the transmission, the engine and motor reflect the driver's will to accelerate or decelerate. A power distribution control method for a parallel hybrid electric commercial vehicle performed by a Hybrid Control Unit (HCU) that determines the power to be responsible for each and generates and outputs a command value corresponding to the determined power, comprising:

(a) receiving an accelerator pedal signal (APS signal) and a brake pedal signal (BPS signal);

(b) calculating the required torque at the transmission input end from the wheel required torque caused by the received accelerator pedal signal and brake pedal signal;

(c) generating a motor torque candidate group to be handled by a motor from the required torque at the transmission input end and generating a battery power candidate group from the motor torque candidate group;

(d) calculating an engine torque candidate group from the required torque at the transmission input stage and the motor torque candidate group, and generating a fuel power candidate group from the engine torque candidate group.

(e) receiving the current estimated weight of the vehicle from a dedicated controller that estimates the weight of the vehicle;

(f) When a command value corresponding to the torque included in the motor torque candidate group and the engine torque candidate group is input to each motor and engine from the motor torque candidate group and the engine torque candidate group, the corresponding torque command value Generating a group of overresponse torque candidates, which are predicted values expected to be output by the motor and engine after a certain period of time from the input point of;

(g) generating an overresponse wheel torque candidate group by considering the current gear ratio and efficiency of the motor overresponse torque candidate group and the engine overresponse torque candidate group;

(h) determining a required wheel torque shortfall candidate group from the difference between the wheel request torque and the over-response wheel torque candidate group;

(i) generating a candidate group of expected accelerations that the vehicle will have when starting from a stationary state from the current estimated weight of the vehicle and the candidate group of hyperresponsive wheel torques;

(j) generating a responsiveness correction function by substituting the required wheel torque shortfall candidate group, the expected acceleration candidate group, and control parameters into a first function equation;

(k) Substituting the battery power candidate group, the fuel power candidate group, and the responsiveness correction function into a second functional equation including an equivalent factor, and a combination of motor torque and engine torque such that the function value by the second functional equation is minimized. determining the optimal motor input torque and the optimal engine input torque, respectively;

(l) applying the optimal motor input torque and optimal engine input torque determined in step (k) to the motor and engine, respectively;

Provides an optimal power distribution control method for hybrid electric commercial vehicles including.

In one aspect of the present invention, the required torque at the transmission input terminal in step (b) is the wheel required torque output from a torque map that dataizes the required torque for each vehicle speed according to the amount of accelerator pedal or brake pedal operation. It can be the value divided by the current gear ratio of the gear ratio, the reduction ratio of the reducer, and the efficiency of the transmission and the efficiency of the reducer.

And in step (c), (c-1) the required torque at the transmission input stage is multiplied by a control input ( u _cv ) ranging from -1 to 1 to generate a group of motor torque candidates to be handled by the motor, (c-2) A motor power candidate group corresponding to the motor torque candidate group is generated by reflecting the rotational speed and efficiency of the motor in the motor torque candidate group, and (c-3) a battery required for the battery to handle the motor power candidate group. A required current candidate group may be generated, and (c-4) the battery power candidate group may be generated by considering the open-circuit voltage of the battery in the battery required current candidate group.

Here, the motor power candidate group is calculated by multiplying the motor torque candidate group by the rotation speed of the motor and dividing this by the efficiency of the motor in a situation where the vehicle is currently running only with a motor or with a motor and an engine, and the current state of the vehicle In a situation where the engine is charging the motor, the motor torque candidate group can be calculated as a value multiplied by the rotational speed of the motor and the efficiency of the motor.

And, the battery demand current candidate group ( I _bat,cand ) can be calculated through the following calculation formula.

Here, V _oc is the battery open-circuit voltage, and R _int is the battery internal resistance, and P _mot,cand represents the motor power candidates.

In addition, in step (d), (d-1) the engine torque candidate group is generated from the difference between the required torque at the transmission input stage and the motor torque candidate group, and (d-2) the engine torque candidate group is used to determine the rotation of the engine. A fuel consumption candidate group required to generate a plurality of engine torque candidates is generated by reflecting speed, efficiency, and low-level heat generation, and (d-3) the fuel power candidate group can be derived by reflecting the low-level heat generation in the fuel consumption candidate group. there is.

In addition, in step (f), a motor overresponse torque candidate group ( T _mot,out,cand ) is generated using the motor overresponse torque candidate group calculation formula below,

The engine overresponse torque candidate group ( T _eng,out,cand ) can be calculated using the engine overresponse torque candidate group calculation formula below.

Here, t _trs refers to the response time and refers to the constant time, T _mot,cand and T _eng,cand refer to a plurality of torque candidates included in each of the motor torque candidate group and the engine torque candidate group, and τ _mot is the time delay constant of the motor, which is a value modeled in advance for the two factors of the motor's rotational speed and torque and stored in the form of map data, and τ _eng is the time delay constant of the engine, which is the two factors of the rotational speed and torque of the engine. It refers to a value that has been modeled in advance and stored in the form of map data.

Additionally, in step (h), the required wheel torque shortfall candidate group (Δ T _lack,cand ) can be calculated using the following calculation formula.

Here, T _wheel,dmd is the torque required for the wheel, and T _{wheel,out,cand} is the torque required in (g) above. Indicates the overresponse wheel torque candidate group generated in this step.

Additionally, in step (i), the expected acceleration candidates ( a _{veh, trs, cand} ) can be calculated using the following calculation formula.

Here, T _{wheel, out, cand} refers to (g) above. This is a candidate group for overresponse wheel torque generated in this step , where m _veh and r _wheel indicate the current estimated weight of the vehicle and the radius of the wheel, respectively.

And, the first functional equation in step (j) is,

It may be, where J _rsps is the responsiveness correction function to improve responsiveness, and Δ T _lack,cand refers to a candidate group for the required wheel torque shortfall generated in step (h). And a _{veh, trs, cand} are the expected acceleration candidates generated in step (i), and k ₁ and k ₂ are the It is a tuning parameter to control the size and importance of factors (wheel torque shortfall candidate group and expected acceleration candidate group) when determining the responsiveness correction function. It is a value obtained through experiment and is a set constant input during the design process, k _a (v ) is a variable depending on the vehicle's longitudinal speed ( v ) so that the expected acceleration candidate group is included in the responsiveness correction function only when the speed is below a certain speed in order to improve the vehicle's responsiveness at the moment of starting from a stop. It is set under the following conditions. It can be.

In addition, the second functional equation in step (k) is,

It may be, where H _cv is the function value, and P _fuel,cand is the fuel power candidate group obtained in step (d). And P _{bat,cand refers} to the battery power candidate group obtained in step (c) above, and S _cv is the current estimated weight and battery state of charge (SOC) of the vehicle, and driving mode (fuel-efficient driving mode, sports driving mode, It may be a value determined from an equivalent factor map that converts equivalent factors into data for each (normal mode, etc.).

Additionally, the control parameters in step (j) may be set to vary depending on the battery state of charge (State of Charge, SOC) or the driver's willingness to accelerate (APS signal).

According to an embodiment of the present invention, by performing power distribution to the driving source (engine and motor) by reflecting the characteristics of the commercial vehicle, optimal control is achieved to minimize the amount of fuel consumed in accordance with the changed weight even if the weight of the commercial vehicle changes. It is possible to perform optimal fuel efficiency control to have improved response performance by compensating for the slow response of commercial vehicles.

In addition, in an embodiment of the present invention, tunable control parameters ( k _1, k ₂ ) are reflected in the process of determining the optimal power distribution value for the driving source (engine and motor), so that the corresponding control parameters ( k _1, k ₂ ) ) has the advantage of being able to control response performance and fuel efficiency by adjusting .

1 is a schematic diagram schematically showing a hybrid system of a typical conventional parallel hybrid vehicle.
Figure 2 is a flowchart showing an optimal power distribution control process for a hybrid electric commercial vehicle according to an embodiment of the present invention performed by the HCU.
Figure 3 shows simulation results when power distribution is controlled using a conventional conventional optimal control method for minimizing fuel consumption that does not take into account the characteristics of commercial vehicles (vehicle weight variability and slow response), and the present invention that reflects the characteristics of commercial vehicles in control A table comparing simulation results for each item when power distribution is controlled using the control method according to the embodiment.

Hereinafter, preferred embodiments of the present invention will be described in detail.

In describing the present invention, terms used in the following specification are only used to describe specific embodiments and are not intended to limit the present invention. Singular expressions include plural expressions unless the context clearly dictates otherwise.

In addition, in this specification, terms such as "include" or "have" are intended to designate the presence of features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, but are intended to indicate the presence of one or more other It should be understood that this does not exclude in advance the presence or addition of features, numbers, steps, operations, components, parts, or combinations thereof.

Additionally, terms such as first, second, etc. may be used to describe various components, but the components should not be limited by the terms. The above terms are used only for the purpose of distinguishing one component from another.

In addition, terms such as “…unit,” “…unit,” and “…module” used in the specification refer to a unit that processes at least one function or operation, which may be implemented by hardware or software or a combination of hardware and software. You can.

In the description with reference to the accompanying drawings, identical drawing reference numerals will be assigned to identical components, and duplicate descriptions of identical components will be omitted. Also, in describing the present invention, if it is determined that a detailed description of related known technologies may unnecessarily obscure the gist of the present invention, the detailed description will be omitted.

A power distribution control method applied to a parallel hybrid electric commercial vehicle as shown in FIG. 1 in which the engine, engine clutch, and transmission are arranged in a row in order, a motor is located between the engine clutch and the transmission, and a reducer is arranged behind the transmission. As such, it provides a power distribution control method that can implement optimal driving control through optimal power distribution to the driving source by reflecting the characteristics of electric commercial vehicles (vehicle weight variability and slow response).

Power distribution to the drive source, which will be explained below, is a hybrid controller (Hybrid Control Unit HCU) that determines the power to be covered by the drive source by reflecting the driver's intention to accelerate or decelerate, generates a command value corresponding to the determined power, and outputs it to the drive source. It may be control performed by . In other words, it is revealed that the subject performing the control to be explained later is the hybrid controller.

The hybrid controller controls the drive source to operate according to the command value through cooperative control with other controllers such as the engine control unit and the motor control unit. Here, the drive source operates the hybrid vehicle. It is an engine and a motor that output power for the engine, and therefore, power distribution in the present invention means distribution of power to the engine and motor.

In addition, among the terms used to describe the present invention hereinafter, the term 'candidate group' refers to a group or It refers to a group of newly created candidate values (e.g., over-response torque candidates, over-response wheel torque candidates, etc.) by combining these candidate values with other candidate values using calculation formulas specified in each step.

Let us take a closer look at the optimal power distribution control process for a hybrid electric commercial vehicle according to an embodiment of the present invention performed by the hybrid controller (hereinafter referred to as 'HCU') with reference to the attached drawings.

Figure 2 is a flowchart showing the optimal power distribution control process for a hybrid electric commercial vehicle according to an embodiment of the present invention performed by the HCU. With reference to this, the power distribution control process will be described in detail by dividing it into steps.

The optimal power distribution control method according to an embodiment of the present invention includes the step of receiving signals (accelerator pedal signal and brake pedal signal) from APS (Accelerator Position Sensor) and BPS (Brake Position Sensor) to determine the driver's driving intention ( Start from S100). Here, the APS and BPS detect the driver's operation of the accelerator pedal and brake pedal, respectively, and convert the operation displacement into a corresponding electric signal and transmit it to the HCU.

The HCU, which receives signals (accelerator pedal signal and brake pedal signal) from APS and BPS, follows the input mechanical algorithm to provide wheel demand torque ( T _{wheel, dmd} (APS, BPS) ) is determined, and the required torque ( T _T/M,dmd ) at the transmission input stage is calculated from the wheel required torque (S200).

The wheel demand torque ( T _{wheel, dmd} (APS, BPS) ) can be output from a torque map that dataizes the torque demand for each vehicle speed according to the accelerator pedal and brake pedal operation amount (displacement amount), and this wheel demand torque ( T _{wheel, dmd} (APS, BPS) ) divided by the current gear ratio of the transmission ( γ _i ) and the reduction ratio of the reducer ( γ _fd ), and the efficiency of the transmission ( η _i ) and the efficiency of the reducer ( η _fd ) is the value obtained at the transmission input stage. It may be the required torque ( T _T/M,dmd ).

In other words, the required torque ( T _T/M,dmd ) at the transmission input stage may be a value calculated through the following calculation formula.

When the required torque ( T _T/M,dmd ) at the transmission input stage is calculated in step S200, the required torque ( T _T/M,dmd ) at the transmission input stage calculated in step S200 is calculated as a series of previously input steps. The process of processing the data and generating certain data is carried out. Preferably, a motor torque candidate group ( T _mot,cand ) to be handled by the motor is generated from the required torque ( T _T/M,dmd ) at the transmission input stage, and battery power is generated from the generated motor torque candidate group ( T _mot,cand ). A candidate group ( P _{bat, cand} ) is created (S300).

Specifically, in step S300, the required torque ( T _T/M,dmd ) at the transmission input stage is multiplied by the control input ( u _cv ) having a range from -1 to 1 to determine a motor torque candidate group ( T _mot ) to be handled by the motor. _,cand ) is first generated (S302), and the rotational speed ( ω _mot ) and efficiency ( η _mot ) of the motor are reflected in each motor torque candidate belonging to the motor torque candidate group ( T _mot,cand ) to create a motor torque candidate group ( T A motor power candidate group ( P _{mot,cand )} consisting of motor power candidates corresponding to each motor torque candidate of mot,cand is generated (S304).

In addition, a battery demand current candidate group ( I _bat,cand ) consisting of battery demand current candidates required for the battery to handle each motor torque candidate belonging to the motor power candidate group ( P _mot,cand ) is generated (S306), Each battery required current candidate belonging to the battery required current candidate group ( I _bat,cand ) is multiplied by the open-circuit voltage ( V _oc ) of the battery to obtain a battery power candidate group ( P _bat,cand , consisting of battery power candidates). P _bat,cand = V _oc I _bat,cand ) is generated (S308).

Due to the nature of the parallel hybrid, the engine and drive motor rotate at the same speed, so the control input ( u _cv ) is adjusted to the required torque at the transmission input end ( T _T/M,dmd ) compared to the input motor torque ( T _mot,dmd ) at the transmission input end. ) can be defined as a ratio.

Here, when the control input is 1, the vehicle is driven only by the motor, and when the control input is greater than 0 but less than 1, both the engine and the motor are driven. If the control input is 0, it means that the vehicle is driven only by the engine, and if the control input is less than 0 and more than -1, it means that the engine is driving while charging the motor.

In order to obtain the minimum value of the price function (the second function in the present invention) in optimal driving control, it is reasonable to investigate the local maximum value through differentiation in a continuous function, but continuous input cannot be considered when applied to an actual vehicle system. The signal must be discretized (digitized) and reflected in the embedded system.

In the present invention, the control input ( u _cv ) can be set to have the range of -1 ≤ u _cv ≤ 1, as mentioned above. For example, if the control input ( u _cv ) is discretized to have a resolution of 0.01, the control input ( u _cv ) and motor torque candidates ( T _mot,cand ) can be expressed as a 201-dimensional vector as shown below.

As mentioned above, the motor power candidate group ( P _mot,cand ) has a rotation speed ( ω _mot ) for each of a plurality of motor torque candidates (e.g., 201 candidates) belonging to the motor torque candidate group ( T _mot,cand ). ) and efficiency ( η _mot ).

In a situation where the vehicle is currently running only with a motor, with a motor and an engine, or with only an engine (a situation where the motor torque is 0 or more, T _mot ≥ 0), the rotational speed of the motor is added to the motor torque candidate group ( T _mot,cand ). It is calculated by multiplying ( ω _mot ) and dividing it by the efficiency of the motor ( η _mot ). If the current state of the vehicle is a situation where the engine is charging the motor (motor torque is less than 0, T _mot < 0), the motor torque It can be calculated by multiplying the candidate group by the rotational speed of the motor ( ω _mot ) and the efficiency of the motor ( η _mot ).

In other words, the motor power candidate ( P _mot,cand ) is,

It may be a value generated through a calculation formula such as .

If the high-voltage battery mounted on a hybrid vehicle is expressed mathematically in a simplified way, the battery open-circuit voltage ( V _oc ), voltage load ( V _L ), battery current ( I _bat ), and internal resistance ( R _int ) are calculated based on the primary circuit model. It can be expressed as follows.

Additionally, the battery current can be estimated from the equation below for the power consumed by the battery ( P _bat ).

Therefore, the battery demand current candidate group ( I _bat,cand ) generated in step S306 is,

It can be generated through a calculation formula such as .

Here, V _oc is the battery open-circuit voltage, and R _int is the battery internal resistance, and P _mot,cand represents the motor power candidates.

Next, an engine torque candidate group ( T _eng,cand ) is generated from the required torque ( T _T/M,dmd ) at the transmission input stage in step S200 and the motor torque candidate group ( T _mot,cand ) in step S300, A process (S400) of deriving fuel power candidates ( P _fuel,cand ) from engine torque candidates ( T _eng,cand ) is performed.

Specifically, in step S400, the difference ( T _{T/M, dmd -} T mot _, From _cand ), the engine torque candidate group ( T _eng,cand = T _T/M,dmd - T _mot,cand = T _T/M,dmd - T _T/M,dmd · u _cv ) is generated (S402), and the engine torque is calculated for a plurality of engine torque candidates belonging to the engine torque candidate group ( T _eng,cand ). A fuel consumption candidate group consisting of fuel consumption candidates required to generate each of the engine torque candidates is generated by reflecting the rotational speed ( ω _eng ), efficiency ( η _eng ), and low-level heat generation ( Q _LHV ) (S404).

Then, the fuel power candidate group ( P fuel _{, cand} ) consisting of a plurality of fuel power candidates is derived by reflecting the low heat value ( Q _LHV ) to each fuel consumption candidate belonging to the fuel consumption candidate group ( m fuel,cand) (S406 ).

In step S404, the fuel consumption candidate group can be calculated specifically using the following calculation formula.

And in step S406, the fuel consumption candidate group ( m _fuel,cand ) obtained in step S404 can be multiplied by the low-level heating value ( Q _LHV ) to derive the fuel power candidate group ( P _fuel,cand ).

In other words, the fuel power candidate ( P _fuel,cand ) is,

It can be derived through a calculation formula such as .

A step (S500) of receiving the current estimated weight of the vehicle is performed in parallel with the series of processes described in each of steps S100 to S400 described above. In step S500, the HCU receives the current estimated weight of the vehicle through CAN communication from a dedicated controller that estimates the weight of the vehicle.

For reference, in order to guarantee the driving performance of commercial vehicles, control that reflects this when the vehicle weight changes significantly is required. In particular, since chassis control and braking performance are most affected by weight, control must be made taking into account changes in the weight of the vehicle. For this reason, the relevant dedicated controller (controller that estimates the current weight of the vehicle) must calculate the current weight of the vehicle. Includes estimation logic.

Since the series of processing for estimating the weight of a commercial vehicle performed by a dedicated controller is already a known technology, its description will be omitted.

Subsequently, for each motor and engine from the above-mentioned motor torque candidate group ( T _mot,cand ) and engine torque candidate group ( T _eng,cand ), the torque candidates included in the motor torque candidate group and the engine torque candidate group for each motor and engine (Motor torque candidate and engine torque candidate) When inputting the corresponding command value, the overresponse torque is the predicted value expected to be output by the motor and engine after a certain period of time ( t _trs ) from the input of the torque command value. A candidate group ( T _mot,out,cand ) is generated (S600).

In step S600, a motor overresponse torque candidate group ( T _mot,out,cand ) is generated using the motor overresponse torque candidate group calculation formula below,

The engine overresponse torque candidate group ( T _eng,out,cand ) can be generated using the engine overresponse torque candidate group calculation formula below.

Here, t _trs refers to the response time and refers to the above constant time. In the above equation, the constant time ( t _trs ) is expressed as 0.1 seconds as a preferred example, but it should be noted that the constant time ( t _trs ) is not limited to 0.1 seconds.

And in the above equation, T _mot,cand and T _eng,cand refer to a plurality of torque candidates included in each of the motor torque candidate group and the engine torque candidate group.

In addition, in the above equation, τ _mot is the time delay constant of the motor, which may be a value modeled in advance for the two factors of the motor's rotational speed and torque and stored in the form of map data, and τ eng is the time delay constant of the engine, and τ _eng is the time delay constant of the engine. The two factors, rotation speed and torque, may be values modeled in advance and stored in the form of map data.

For reference, if the time delay constant ( τ ) is large, the output signal value responds slowly when an input signal is given, and if the time delay constant ( τ ) is small, the output signal Values respond quickly. In other words, the speed of output compared to input can be adjusted through the time delay constant ( τ ).

When the motor's overresponse torque candidate group ( T _mot,out,cand ) and the engine's overresponse torque candidate group ( T _eng,out,cand ) are generated through step S600, the HCU generates the generated motor's overresponse torque candidate group ( T _mot,out,cand ) and the engine's overresponse torque candidate group ( T _eng,out,cand ), the current gear ratio of the transmission ( γ _i ), transmission efficiency ( η _i ), and reduction gear ratio ( γ _fd ) of the reducer. By reflecting ( η _fd ), an overresponse wheel torque candidate group ( T _{wheel,out,cand} ) is generated (S700).

Here, the overresponse wheel torque candidate group ( T _{wheel,out,cand} ) is the motor overresponse torque candidate group belonging to the motor overresponse torque candidate group ( T _mot,out,cand ) and the engine overresponse torque candidate group ( T _{eng,out , cand} ), the combined value of the engine overresponse torque candidates is multiplied by the current gear ratio ( γ _i ), transmission efficiency ( η _i ), reduction gear ratio ( γ _fd ), and reducer efficiency ( η _fd ). It can be defined as:

In other words, the overresponse wheel torque candidate group ( T _{wheel,out,cand} ) can be generated through the following calculation formula.

The overresponse wheel torque candidate group ( T _{wheel,out,cand} ) generated in the S700 step is used to generate the yoke wheel torque shortfall candidate group (Δ T _lack,cand ) in the next step, the S800 step. Specifically, in the S800 step, the wheel demand torque ( T _wheel,dmd (APS, BPS) ) in the above-described step S200 and a plurality of overresponse wheel torque candidates belonging to the overresponse wheel torque candidate group ( T _{wheel,out,cand} ) From the difference, the required wheel torque shortfall candidate group ( ΔT _lack,cand ) is determined.

That is, in step S800, the required wheel torque shortfall candidate group (Δ T _lack,cand ) can be generated through the following calculation equation.

Here, T _wheel,dmd is the wheel demand torque, and T _{wheel,out,cand} is the S700 Indicates the overresponse wheel torque candidate group generated in this step.

When a certain amount of time ( t _trs ) has passed since the torque command, the shortfall in torque required by the vehicle in the overresponse state (Δ T _lack ) is the difference between the overresponse wheel torque ( T _wheel,out ) and the required wheel torque ( T _wheel,dmd ). It can be expressed as Here, the large lack of required torque ( ΔT _lack ) means that the difference between the torque required by the vehicle and the actual response torque is large.

In other words, the larger the required torque shortfall ( ΔT _lack ), the slower the vehicle's control response can be judged to be. Conversely, if the required torque shortfall ( ΔT _lack ) is small, the actual response torque may not satisfy the torque required by the vehicle. Since the output is as high as the output, it can be considered to be responsive.

In an embodiment of the present invention, in parallel with the process of generating the required wheel torque shortfall candidate group ( ΔT _lack,cand ), the current estimated weight of the vehicle received in step S500 and the overresponse wheel torque candidate group ( T _{wheel, out,cand} ), a group of expected acceleration candidates ( a _veh,trs,cand ) that the vehicle will have when starting from a stop state is generated from the overresponse wheel torque candidates belonging to (S900).

At this time, the expected acceleration candidates ( a _{veh, trs, cand} ) can be generated through the following calculation formula.

Here, T _{wheel, out, cand} is the S700 This is a candidate for over-response wheel torque at this stage , and m _veh and r _wheel indicate the current estimated weight of the vehicle and the radius of the wheel, respectively.

Next, the required wheel torque shortfall candidate group ( ΔT _lack,cand ) in step S800, the expected acceleration candidate group ( a _veh,trs,cand ) in step S900, and control parameters are substituted into the first functional equation to determine the responsiveness. A step (S1000) of generating a correction function is performed.

Below is a first functional equation applied to an embodiment of the present invention to generate the responsiveness correction function in step S1000.

[First functional equation]

In the first functional equation above, J _rsps refers to the responsiveness correction function to improve responsiveness, and Δ T _lack,cand refers to a candidate group for the required wheel torque shortfall generated in step S800. And a _{veh, trs, cand} are the expected acceleration candidates calculated in step S900.

Additionally, in the first functional formula above, k ₁ and k ₂ are It is a tuning parameter to adjust the size and importance of factors (wheel torque shortfall candidates and expected acceleration candidates) when determining the responsiveness correction function, and k _a (v) is a constant to improve vehicle responsiveness at the moment of starting from a stop. It is a variable according to the longitudinal speed ( v ) of the vehicle so that the expected acceleration candidate group is included in the responsiveness correction function only when it is below the speed, and can be set under the following conditions.

Here, k ₁ and k ₂ , which are tuning parameters for controlling the size and importance of factors (wheel torque shortfall candidates and expected acceleration candidates) when determining the responsiveness correction function, are values obtained through experiment and input during the design process. It may be a set constant, and in some cases, it may be set to vary depending on the battery state of charge (State of Charge, SOC) or the driver's willingness to accelerate (APS signal).

In allowing k ₁ and k ₂ to vary depending on the battery state of charge (State Of Charge, SOC) or the driver's willingness to accelerate (APS signal), for example, in a situation where SOC is very low, the motor output is expected to decrease and respond. Reduce the parameters ( k ₁ , k ₂ ) that complement performance, and set k ₁ and k ₂ to 0 when the APS signal is 10% or less to ensure maximum fuel-efficient driving. When the APS signal exceeds 40%, the driver Reflecting the will, k ₁ and k ₂ can be set to increase in proportion to the APS.

Once the responsiveness correction function is determined in step S1000, the HCU includes the battery power candidate group ( P _bat,cand ) in step S308 described above, the fuel power candidate group ( P _fuel,cand ) in step S400, and the responsiveness correction function and Considering the equivalence factor ( S _cv ) to equalize the energy consumed by the battery with the energy resulting from engine fuel consumption, a combination of engine torque and motor torque values that satisfy the driver's required torque in the current engine and motor conditions is determined. Decide (S1100).

Specifically, in step S1100, the second battery power candidate group ( P _bat,cand ), the fuel power candidate group ( P _fuel,cand ), the responsiveness correction function ( J _rsps ), and the equivalence factor ( S _cv ) are used. Substitute into the function expression.

[Second functional expression]

Then, a combination of battery power and fuel power that minimizes the function value according to the second functional equation is selected from the necessary conditions of the pontriazine minimum principle below.

Then, the motor torque and engine torque corresponding to the selected combination are derived through the equation below, and the derived motor torque and engine torque are calculated as the optimal input motor torque value ( T ^* _mot,cmd ) and the optimal input engine torque value ( It is decided as T ^* _eng,cmd ).

For reference, the second functional equation refers to a functional equation applied to obtain the torque distribution value through an optimization process based on ECMS (Equivalent Consumption Minimization Strategy), and equates the energy consumed from the battery with the energy resulting from fuel consumption of the engine. The equivalent factor ( S _cv ) for the vehicle's current estimated weight, battery charge state, and driving mode (fuel efficiency driving mode, sports driving mode, normal mode, etc.) can be output from an equivalent factor map that dataizes the equivalent factors for each driving mode. .

In FIG. 2, S1200 finally indicates the step of applying the optimal motor input torque and optimal engine input torque determined in step S1100 to the motor and engine, respectively.

Figure 3 shows the simulation results ((a) of Figure 3) when power distribution is controlled using a conventional conventional fuel consumption minimization optimal control method that does not take into account the characteristics of commercial vehicles (vehicle weight variability and slow response), and the commercial vehicle This is a table comparing and organizing simulation results by item when power distribution is controlled through a control method (Figure 3(b)) according to an embodiment of the present invention that reflects the characteristics of in control.

In this simulation, fuel efficiency driving mode driving cycle driving (equivalent factor tuning with SOC difference before/after driving less than 1%) is used as the driving condition, and maximum acceleration will (APS = 100%) is applied from a standstill to reach the target speed. Time was measured.

Looking at the simulation results of FIG. 3 (simulation results of the prior art shown in (a) of FIG. 3 and simulation results of controlling power distribution with the technology proposed by the present invention shown in (b) of FIG. 3), When controlling with the technology proposed in the invention, it can be seen that the fuel efficiency is slightly reduced compared to the control results of the existing technology focusing on fuel efficiency, but the response performance is improved.

According to the embodiment of the present invention discussed above, power distribution for the driving source (engine and motor) is performed by reflecting the characteristics of the commercial vehicle, so that even if the weight of the commercial vehicle changes, the amount of fuel consumed is minimized in accordance with the changed weight. Optimal control can be performed, and fuel efficiency optimal control can be performed to have improved response performance by compensating for the slow response of commercial vehicles.

In addition, in an embodiment of the present invention, tunable control parameters ( k _1, k ₂ ) are reflected in the process of determining the optimal power distribution value for the driving source (engine and motor), so that the corresponding control parameters ( k _1, k ₂ ) ) has the advantage of being able to control response performance and fuel efficiency by adjusting .

In the above detailed description of the present invention, only special embodiments thereof have been described. However, it should be understood that the present invention is not limited to the particular form mentioned in the detailed description, but rather is understood to include all modifications, equivalents and substitutes within the spirit and scope of the present invention as defined by the appended claims. It has to be.

Claims (12)

In a parallel hybrid electric commercial vehicle in which the engine, engine clutch, and transmission are arranged in a row, a motor is located between the engine clutch and transmission, and a reducer is arranged behind the transmission, the engine and motor reflect the driver's will to accelerate or decelerate. A power distribution control method for a parallel hybrid electric commercial vehicle performed by a Hybrid Control Unit (HCU) that determines the power to be responsible for each and generates and outputs a command value corresponding to the determined power, comprising:
(a) receiving an accelerator pedal signal (APS signal) and a brake pedal signal (BPS signal);
(b) calculating the required torque at the transmission input end from the wheel required torque caused by the received accelerator pedal signal and brake pedal signal;
(c) generating a motor torque candidate group to be handled by a motor from the required torque at the transmission input end and generating a battery power candidate group from the motor torque candidate group;
(d) generating an engine torque candidate group from the required torque at the transmission input end and the motor torque candidate group, and generating a fuel power candidate group from the engine torque candidate group;
(e) receiving the current estimated weight of the vehicle from a dedicated controller that estimates the weight of the vehicle;
(f) When a command value corresponding to the torque included in the motor torque candidate group and the engine torque candidate group is input to each motor and engine from the motor torque candidate group and the engine torque candidate group, the corresponding torque command value Generating a group of overresponse torque candidates, which are predicted values expected to be output by the motor and engine after a certain period of time from the input point of;
(g) generating an overresponse wheel torque candidate group by considering the current gear ratio and efficiency of the motor overresponse torque candidate group and the engine overresponse torque candidate group;
(h) determining a required wheel torque shortfall candidate group from the difference between the wheel request torque and the overresponse wheel torque candidate group;
(i) generating a candidate group of expected accelerations that the vehicle will have when starting from a stationary state from the current estimated weight of the vehicle and the candidate group of hyperresponsive wheel torques;
(j) generating a responsiveness correction function by substituting the required wheel torque shortfall candidate group, the expected acceleration candidate group, and control parameters into a first function equation;
(k) Substituting the battery power candidate group, the fuel power candidate group, and the responsiveness correction function into a second functional equation including an equivalent factor, and a combination of motor torque and engine torque such that the function value by the second functional equation is minimized. determining the optimal motor input torque and the optimal engine input torque, respectively;
(l) applying the optimal motor input torque and optimal engine input torque determined in step (k) to the motor and engine, respectively;
Optimal power distribution control method for a hybrid electric commercial vehicle including.
According to claim 1,
In step (b), the required torque at the transmission input stage is,
The wheel required torque output from the torque map that dataizes the required torque for each vehicle speed according to the amount of accelerator pedal or brake pedal operation is divided by the current gear ratio of the transmission, the reduction ratio of the reducer, and the efficiency of the transmission and the efficiency of the reducer. Optimal power distribution control method for hybrid electric commercial vehicles.
According to claim 1,
In step (c) above,
(c-1) multiplying the required torque at the transmission input stage by a control input ( u _cv ) ranging from -1 to 1 to generate a group of motor torque candidates to be handled by the motor,
(c-2) Reflecting the rotational speed and efficiency of the motor in the motor torque candidate group to generate a motor power candidate group corresponding to the motor torque candidate group,
(c-3) Generating a battery demand current candidate group required for the battery to handle the motor power candidate group,
(c-4) An optimal power distribution control method for a hybrid electric commercial vehicle that generates the battery power candidate group by considering the open-circuit voltage of the battery in the battery demand current candidate group.
According to claim 3,
The motor power candidates are:
In a situation where the vehicle is currently running only with a motor or with a motor and an engine, the motor torque candidate group is calculated by multiplying the rotational speed of the motor and dividing this by the efficiency of the motor,
An optimal power distribution control method for a hybrid electric commercial vehicle that is calculated by multiplying the motor torque candidate group by the rotational speed of the motor and the efficiency of the motor when the current state of the vehicle is that the engine is charging the motor.
According to claim 4,
The battery demand current candidate group ( I _bat,cand ) is an optimal power distribution control method for a hybrid electric commercial vehicle that is generated through the following calculation equation.

Here, V _oc is the battery open-circuit voltage, and R _int is the battery internal resistance, and P _mot,cand represents the motor power candidates.
According to claim 1,
In step (d) above,
(d-1) generating the engine torque candidate group from the difference between the required torque at the transmission input stage and the motor torque candidate group,
(d-2) Generating a fuel consumption candidate group required to generate a plurality of engine torque candidates by reflecting the engine rotation speed, efficiency, and low-level heat generation in the engine torque candidate group,
(d-3) An optimal power distribution control method for a hybrid electric commercial vehicle in which the fuel power candidate group is derived by reflecting the low heat generation amount in the fuel consumption candidate group.
According to claim 1,
In step (f) above,
Generate a motor overresponse torque candidate group ( T _mot,out,cand ) using the motor overresponse torque candidate group calculation formula below,

An optimal power distribution control method for a hybrid electric commercial vehicle that generates an engine overresponse torque candidate group ( T _eng,out,cand ) using the engine overresponse torque candidate group calculation formula below.

Here, t _trs refers to the response time and refers to the constant time, T _mot,cand and T _eng,cand refer to a plurality of torque candidates included in each of the motor torque candidate group and the engine torque candidate group, and τ _mot is the time delay constant of the motor, which is a value modeled in advance for the two factors of the motor's rotational speed and torque and stored in the form of map data, and τ _eng is the time delay constant of the engine, which is the two factors of the rotational speed and torque of the engine. It refers to a value that has been modeled in advance and stored in the form of map data.
According to claim 1,
In step (h), the required wheel torque shortfall candidate group (Δ T _lack,cand ) is generated through the following calculation equation. An optimal power distribution control method for a hybrid electric commercial vehicle.

Here, T _wheel,dmd is the torque required for the wheel, and T _{wheel,out,cand} is the torque required in (g) above. Indicates the overresponse wheel torque candidate group generated in this step.
According to claim 1,
In step (i), the expected acceleration candidates ( a _{veh, trs, cand} ) are generated through the following calculation equation. An optimal power distribution control method for a hybrid electric commercial vehicle.

Here, T _{wheel, out, cand} is (g) above. This is a candidate group for overresponse wheel torque generated in this step , where m _veh and r _wheel indicate the current estimated weight of the vehicle and the radius of the wheel, respectively.
According to claim 1,
The first functional equation in step (j) is,

Optimal power distribution control method for hybrid electric commercial vehicles.
Here, J _rsps is the responsiveness correction function to improve responsiveness, and Δ T _lack,cand refers to a candidate group for the required wheel torque shortfall generated in step (h). And a _{veh, trs, cand} are the expected acceleration candidates generated in step (i), and k ₁ and k ₂ are the It is a tuning parameter to control the size and importance of factors (wheel torque shortfall candidate group and expected acceleration candidate group) when determining the responsiveness correction function. It is a value obtained through experiment and is a set constant input during the design process, k _a (v ) is a variable depending on the vehicle's longitudinal speed ( v ) so that the expected acceleration candidate group is included in the responsiveness correction function only when the speed is below a certain speed in order to improve the vehicle's responsiveness at the moment of starting from a stop. It is set under the following conditions. It can be.

According to claim 1,
The second functional equation in step (k) is,

Optimal power distribution control method for hybrid electric commercial vehicles.
Here, H _cv is the function value, and P _fuel,cand is the fuel power candidate group obtained in step (d). And P _{bat,cand refers} to the battery power candidate group obtained in step (c) above, and S _cv is the current estimated weight and battery state of charge (SOC) of the vehicle, and an equivalent factor map that dataizes equivalent factors for each driving mode. It is a value determined from .
According to claim 1,
An optimal power distribution control method for a hybrid electric commercial vehicle in which the control parameters in step (j) are set to vary according to the battery state of charge (State Of Charge, SOC) or the driver's willingness to accelerate (APS signal).

Images